Martin Luther and His Impact on the Modern Church Essay

Western Europe was in urgent need of progress during the sixteenth century. The well known cry among the Europeans was a call for â€Å"reform†. The political climate was flimsy because of brutal authority changes during the destroying of the medieval framework. This disunity of the individuals made a general environment among the Europeans of discontent, turmoil, and dissatisfaction. Monetarily, the occupants experienced expanded neediness and money related difficulties. The congregation was seriously tormented by ravenousness and defilement among the pastorate, even in the more elite classes. The profound nature of the ministry was being debased through the arrangement of familial or political applicants. The religious personalities, similar to Martin Luther’s, looked for a renewal of chapel tenet and an arrival to the essentials of Christianity. The accentuation put on Luther’s regulation of avocation and scriptural authority assisted with improving church con ventions and break the coupling hold of degenerate pioneers over the congregation. The sixteenth century was a turbulent time for Western Europe and the Church. Numerous Europeans were loaded with stress concerning the conservative, strict, and social issue. As the print machine got mainstream, the center and lower classes were being overflowed with data that had recently been inaccessible; a few contending precepts were being given a voice through printed media. Beforehand, individuals would go to strict foundations for expectation and direction in the midst of this kind of turmoil. In any case, the condition of the congregation in the sixteenth century was delicate. This is expected partially with the impacts of the Great Schism in the fourteenth century. â€Å"[The Great Schism] separated the political, just as the religious world, and splits up the Christian Europe into a few threatening camps†. The Great Schism was a consequence of a slow decay along political and religious lines. Before the Great Schism, the Papacy had ascended to a degree of unmistakable quality in the hearts and psyches of Western European Christians. The Church controlled practically every part of human life inside Western Europe and the Pope was looked to as the expert on all issues; otherworldly and common. The Church had a huge level of the locale and had set up one of the most proficient frameworks of government ever. As of now, religious avocation for the matchless quality of the papacy had been built up under the rule of â€Å"the subjection of the state to the church† by Pope Innocent III. Be that as it may, in Eastern Europe, the Pope was to a greater degree an outside power; accessible just when their own political initiative couldn't settle matters. The realm was perceived as the incomparable force. They contended that solidarity for the residents must be acknowledged through submission to one position; the domain. Since Christ had not given the authority of the state to the congregation, the congregation reserved no privilege to guarantee matchless quality over the domain. A few strict pioneers in the East tried to repress the force and authority of the Papacy. The atmosphere of Europe was ready for division. The start of the division came as the â€Å"Babylonian Captivity of the Church† that kept going from 1309 to 1377. During this period, the French King Phillip IV held Pope Boniface VIII hostage, and sequestered the College Cardinals to Avignon. This would present a rule of defilement among the French papacy and present a separating line between European Christians; one side supporting the French papacy and another side that contradicted it. These variables prompted a last calamitous occasion that earned the title â€Å"The Great Schism†. At the point when Gregory IX, the last Avigonese pope, came back to Rome, the Church endeavored to restore the papacy in its noteworthy setting. Upon his demise, the papacy confronted an extraordinary test. Urban IV, an Italian pope, was chosen at the encouraging of the nearby horde, while Clement VII, a Frenchman, guaranteed rights to the seat also. This created turmoil over the authority of the congregation and division among politica l lines; England, Germany, Italy, and their partners bolster Urban IV, while France, Scotland, Spain, and their partners upheld the â€Å"anti-pope† Clement VII. The Great Schism greatly affected the confidence and otherworldliness of the majority. There was a general inclination of doubt toward the Church and authority. Much after the Great Schism had finished, â€Å"many thought that it was hard to accommodate their confidence in the papacy with their doubt for its genuine occupants†.Corruption and pay off were presently typical among the upper level church; the act of â€Å"the offer of indulgences† would even fill in for the procedure of salvation. Ethically, the congregation was bombing the adherents. Nonetheless, there were different blemishes inside the congregation during the sixteenth century. Toward the finish of the fifteenth century, Western Europe had been overwhelmed with writing from unique religious ways of thinking. â€Å"New philosophical viewpoints were introduced†. Magic and humanism were presently starting to supplant the already strong educational philosophy of the congregation. Supporters of Reform were requiring a difference in the customary practices. It is in this setting with which Martin Luther was impacted during the fifteenth and sixteenth century. Martin Luther experienced childhood in a stark situation in Germany in the 1500’s. As a youthful grown-up, Luther entered the religious community out of worry for his own salvation. He thought, â€Å"’Oh, in the event that I go into a religious community, and serve God in shaven crown and cowl, he will compensate and welcome’†. Luther started a determined investigation of religious philosophy during his time at the Augustinian Priory in Erfurt. He exceeded expectations mentally and profoundly, however he encountered extreme individual battles. â€Å"He looked to work out his own salvation via cautious recognition of the religious principle, steady admission, and self mortification†. This cautious dutifulness to the devout life was without much of any result; Luther was tormented with despair at the idea of his corruption. Added to his dissatisfactions, Luther had encountered bafflement during his residency at the religious community. The lewdly disapproved of pastorate of Rome stunned him. â€Å"In Rome, the primary flashes of uncertainty flew into his spirit, which, maybe, while he was oblivious to it, faintly gleamed, yet which, with the main open door that may introduce itself, were bound to ascend into a flaring fire†. It is through these encounters that Luther would arrive at huge religious resolutions concerning the convention of avocation by confidence. These determinations assisted with starting a development that would always change the attitude of the Christian people group. As Martin Luther proceeded with his investigations of sacred text, he was tormented by the blame of transgression. â€Å"He was struck by the petition of Psalm 31:1, ‘in thy uprightness convey me’. Be that as it may, how could God’s honorableness convey him? The nobility of God was without a doubt determined preferably to sentence the miscreant over to spare him†. His inquiries expected him to survey Paul’s’ principle of avocation nitty gritty in Romans. The teaching of support by confidence is the essential message of God’s pardoning towards the wrongdoing of man. Employment considered this inquiry in the Old Testament; â€Å"How can an individual be advocated before God?† (Job 9:2, HCSB). Avocation, at that point, is a demonstration of beauty by God, where he acknowledges the honorableness of Christ as our own by our confidence in Christ. Paul remarks that â€Å"For I am not embarrassed about the gospel, since it is God’s power for salvation to each and every individual who accepts, first to the Jew, and furthermore to the Greek. For in it God’s exemplary nature is uncovered from confidence to confidence, similarly as it is composed: The noble will live by faith.† (Romans 1:16-16, HCSB) The honesty of God presently holds new significance for Luther; the message of the gospel, or uplifting news in Christ. The equity or honesty of God doesn't mean discipline as Luther initially suspected, but instead the attribution of God’s honorableness to the devotee. The honesty of God is credited to the devotee, even as the adherent is a delinquent, in light of God’s elegance and kindness. Basically, as Christians we are both wicked and advocated. God doesn't pardon or overlook the wrongdoing of man. Notwithstanding, God proclaims us as legitimized on account of the penance of Christ once we accept and trust in Him. Our confidence isn't the reason for avocation, as though we are remunerated for confidence. But instead, confidence and legitimization are unconditional presents to miscreants who acknowledge them. Luther’s new disclosure drove him to another perspective and lecturing. His musings reasoned that â€Å"I before long felt as though conceived once more; as though I had discovered the entryways of Paradise opened up to me. Presently I likewise viewed the favored Scriptures more respectfully than in previous occasions, and read them through rapidly† . This message of avocation was gotten energetically by the majority. The western Europeans felt that Luther addressed their condition, and expanded their longing for change. For Luther, the Word of God was of most extreme significance and it helped him discover the responses to the issues of salvation that overpowered him. In the brain of Martin Luther, the Word of God was more than the content of the Bible. â€Å"For the laws of the Bible become sweet unto us when we peruse and get them, in books, yet in the injuries of our valuable Savior†. The expression of God is basically more than scriptural content; it is the disclosure of God and the Word of God cooperating. In the essential sense we are told in John 1:1 that the Word of God is really God himself. â€Å"In the start was the Word, and the Word was with God, and the Word was God.† Here point by point, the Word of God is really the personage of Christ, the Second Person of the Trinity. This

Intergenerational Transmission of Education Research Paper

Intergenerational Transmission of Education - Research Paper Example Be that as it may, others have accepted related segment marvels and reached conflicting inferences. Female horse (1997) in his article, â€Å"Differential Fertility, Intergenerational Educational Mobility, and Racial Inequality† discusses the effects of differential richness models among African-American and white ladies for mainstream floats in instructive capacity inside every one of these populaces and for race separations in instructive capacity. Female horse has created models that join recovery of populace with intergenerational versatility for the 1925-1995 periods and investigate the total results of segment floats on financial premiums and disparity with an emphasis on the pattern and distribution of instructive accomplishment. He delineated the degree to which differential productiveness inside the highly contrasting populaces has given to dissimilarities in the instructive accomplishments of these two gatherings. In spite of the fact that this paper talks about current issues identifying with shared disparity, it is incited by hypothetical issues about collective definition too. The examination concentrated on the intergenerational transmission of financial position. He depicted the populace projection model that is applied to duplicate patterns in instructive accomplishment for blacks and whites. He fabricated discrete-time models of populace extension that incorporate the effects old enough explicit ripeness and mortality notwithstanding mutual versatility for blacks and whites. Differential ripeness by instructive accomplishment of mother has upset the extension of normal instructive achievement in the course of recent years; be that as it may, this outcome is little. Despite the fact that the differential degree of fruitfulness of ladies has diminished yet the differential planning of richness has around no effect. Enhancements in ripeness, mortality, and intergenerational adaptability make them destroy consequences for racial disparity1. . Clearly there ought to have been contrasts

Business Finance Written Assignment

Q1. Characterize a â€Å"efficient market† and the three types of market proficiency. Clarify how every one of the structures contrasts from an ideal market. Characterize exchange and clarify what sort of data is required for you to acquire exchange in every one of the types of market productivity. (5 focuses) Q2. If it's not too much trouble analyze the points of interest and burdens of the accompanying speculation rules: Net Present Value (NPV), Payback Period, Discounted Payback Period, Average Accounting Return, Internal Rate of Return (IRR) and Profitability Index (PI). You can begin by considering the accompanying inquiries for every venture rule: Does it use incomes or bookkeeping profit? Does it consider all incomes or not? Does it apply a legitimate markdown rate? Regardless of whether the acknowledgment standards are clear and sensible? In what circumstance it very well may be applied? What sort of shortcoming does it have? ) (5 focuses) Question 1 An effective marke t is pushed by a speculation that under free development of data, the genuine estimation of protections are reasonably valued, which promptly and precisely mirror all data accessible to investors.By the presumptions that sound financial specialists assess the cost by found out future incomes, and can learn and respond rapidly to new data once conveyed, speculators don't hope to accomplish returns in abundance of normal market returns. The three types of market proficiency are powerless, semi-solid, and solid. Distinctive level of data is reflected by cost in various structures. Under powerless structure, the costs mirror all past openly accessible data, as recorded costs developments. Under semi-solid structure, the costs mirror all openly accessible data, as budget summaries and news reports.Under solid structure, the costs mirror all open and private data. For the most part, in light of fast impression of data in cost and snappy reaction of financial specialists to the market, it is unthinkable for speculators to acquire or utilize new data to discover underestimated stocks. To delineate, in powerless structure, utilizing past costs for specialized investigation is futile to foresee future pattern as past data is unessential to what's to come. In semi-solid structure, utilizing key examination isn't helpful as the costs are promptly balanced once the data generally circled in the market.In solid structure, finding underestimated stocks isn't reliable as all data is notable. Hence, no speculators can acquire overabundance return by exchanging the data or selling the stocks with too high anticipated returns. An ideal market is the place no exchange openings happen (I. e. Law of One Price) on the grounds that total data is shared among all speculators. Contrasted and productive market, no differentiation in level of data is reflected in cost here. Exchange implies the act of purchasing and offering identical merchandise in various markets to exploit a value dif ference.An exchange opportunity happens if making a benefit without facing any challenge. An effective market doesn't really mean financial specialists can't yield overabundance return. Rather, an exchange opportunity exists on the off chance that they request suitable data rapidly. On the off chance that a market accomplishes solid structure proficiency given that it is full grown enough, no financial specialist can yield any abundance return in since quite a while ago run. Accordingly, no more data is required. Then again, private and most recent open data are expected to acquire exchange in semi-solid and feeble structure effectiveness separately. (395 words) Question 2 Use of incomes and markdown rateAll speculation rules are controlled by evaluated incomes however just NPV, IRR and PI consider all incomes all through the project’s life. But restitution period, the incomes are limited by legitimate markdown rate under each standard. A positive NPV expects the venture incr easing the value of firm and shareholders’ riches. All limited expected future incomes are mulled over contrasted and the underlying expense. The markdown rate assesses the hazard level and the arrival and accordingly it is suitable. In this way, NPV is the best since it represents time estimation of cash and danger of money flows.IRR is the arrival that set NPV to zero. Additionally, the computation depends on incomes and rebate rate (I. e. same advantage as NPV). It gives a basic device without evaluating all subtleties however instinctively engaging know. On the off chance that IRR is sufficiently high, the time spent on evaluating a necessary expense of capital is avoidable. PI estimates advantage per unit cost dependent on time estimation of cash to assess an extra incentive to firm. Two forms of PI give same choice and both are straightforward and convey. For ascertaining PI, NPV figuring is utilized and in this way PI’s advantage is same as NPV’s.Payback period is the measure of time for future incomes taken to recuperate the underlying speculation. It is a filtering device for questionable incomes. In any case, it disregards cost of capital and time estimation of cash since just incomes for that present period are concerned. Additionally, not all incomes are considered as incomes past recompense period are disregarded. Like recompense period, the main distinction is limited compensation period better considers markdown rate (I. e. time estimation of cash). Along these lines, compensation period on a limited premise will be longer. Clearness and sensibility of acknowledgment criteriaNPV, IRR and PI can give clear and sensible models while no one but NPV can be applied to all circumstances. The NPV rule is to acknowledge an independent undertaking with positive NPV or a fundamentally unrelated task with the most noteworthy NPV. As NPV is assessed completely, the standard can at present be applied in spite of various size of activitie s. The IRR rule is to acknowledge an independent undertaking with IRR more noteworthy than cost of capital or a fundamentally unrelated venture with the most noteworthy IRR. Be that as it may, IRR rule is reliable with NPV rule just if all negative incomes go before positive incomes. As such, the contention is expected to non-traditional debris streams and change in signs more than once. In this manner, non-existent or various IRR(s) may cause vulnerability in dynamic. IRR is untrustworthy when fundamentally unrelated ventures are diverse in scale, hazard and time skyline. PI is firmly identified with NPV, for the most part prompting indistinguishable choices. PI assesses and distinguish the ideal mix under asset requirement, particularly for constrained financial plan. The venture with the most noteworthy PI ought to be picked first. All things considered, it overlooks the size factor and in this manner prompts mistaken choices among fundamentally unrelated projects.Moreover, PI ca n't be applied during numerous asset limitations. The standard of (limited) compensation period is to acknowledge the undertaking on the off chance that it is not exactly a pre-indicated time allotment. It is effectively comprehended and just utilized due to clear acknowledgment models. In any case, a discretionary cutoff point is required for assurance. It is abstract since disregarding the effect of incomes after restitution period favors short â€term activities and inclinations against long â€term ventures. End NPV is the most normally utilized speculation models and valid whenever. On the off chance that any contentions exist among the venture rules, NPV rule ought to win. 605 words) Reference 1. Hong Kong Institute of Investors (2001), â€Å"Efficient Market Hypothesis†, recovered 1 April 2012, from http://td. hkii. organization/investu/168ch7/7-5. php 2. NYU Stern, â€Å"Market Efficiency †Definition and Tests†, recovered 1 April 2012, from http://page s. harsh. nyu. edu/~adamodar/New_Home_Page/invemgmt/effdefn. htm 3. Wikipedia, â€Å"Efficient-showcase hypothesis†, recovered 1 April 2012, from http://en. wikipedia. organization/wiki/Efficient-market_hypothesis 4. Lowlife (21 February 2006), â€Å"Efficient Market Hypothesis†, recovered 1 April 2012, from http://www. fraud. cc/blog/jeysafe/3421966

Improving The Risk Return Performance Of Portfolios Finance Essay - Free Essay Example

With the development of the Chinese capital market, more and more investors start to look for a more rational way to invest. To increase the investment return and decrease the risk, investors must learn to allocate their funds in order to diversify risk. However, due to the limited assets that can be invested in, the convenience and effectiveness of portfolio diversification must be studied. This paper mainly explores the function of futures in the ordinary stock portfolio through the study of risk-return performance. By comparing the efficient frontiers of different portfolios, the risk-return performance of the futures portfolio and mixed stock-futures portfolio is better than the stock only portfolio. Futures play an important role in upgrading the integrated portfolio of stock and futures. The results of this study provide investors with a feasible way to diversify their funds in multi-type investment portfolios, which is of great theoretical and practical significance. I. An introduction to Chinese capital market Ever since December 19, 1990, when Shanghai stock exchange opened, people become more and more interested in investing in the security market to make money. After twenty years, investing in stocks is a quite popular and important way for ordinary Chinese people to manage their money. However, stock market itself can not meet investorsà ¢Ã¢â€š ¬Ã¢â€ž ¢ needs of diversifying risk and increase capital return, and investment diversification becomes a natural solution and guiding concept. Although twenty years have passed since Shanghai stock exchange came in existence, development of Chinese capital market is quite slow, with limited kinds of investment products. Lack of varieties of trading tools and incomplete structure of capital market products make it difficult to diversify in Chinese capital market. In developed capital markets such as Hong Kong, over 80% of financial derivative instruments in international financial market have been introduced. In stock market, the trading o f index futures, options and warrants is quite active with a trending of exceeding the trading of spot market. Hong Kong bond market is even more diversified. Based on three basic kinds which are bond, note and certificate of deposits of fund-raising tools, many more complicated derivatives such as floating rate bonds, variable rate bonds, convertible bonds, credit card receivables, and the current debt instruments traded on the Hong Kong Stock Exchange listing has been increased to 129.(2009) On the contrary, despite of stocks, there are few more than five years investment instruments in mainland China capital markets. The trading of 1-5 year instruments is also confined so that the available trading instruments are quite limited. As an emerging market the risk of stock market is higher than normal, both systematic risk and market risk. The systematic flaws in Chinese stock market such as no trades of state owned and corporation owned stocks and lack of index futures  [1]  o r other kinds of hedging instruments make the whole stock exchange system more uncertain. The strong influence of state policy changing is also a reason for high uncertainty. As for the market risk, stock market is in sharp adjustment since the end of 2007. On the one hand, the overall risk has lowered a little; it is still too high compared with the mature capital markets. On the other hand, the low self-control ability of the participants involved in stock market makes the unsystematic risk higher than average. Investing only in stock market can not successfully diversify risk. Considering the incompleteness of Chinese warrant market, futures have been chosen to diversify risk. Chinese future market also started in 1990. After six years of cleaning up and reconstruction (1995-2000), future market is in good development. In 2002, stock market turned down, which made part of the stock market capital switch to future market and made it a hot deal. This situation is quite similar t o what happened in 2007-2008. Chinese future market developed from first pilot reform to rectification and now has entered a new stage of stable development. The legal operation and market discipline have been significantly improved. These features make futures possible as a component of portfolio. At present, research of the role of futures in the portfolio is focused on index futures and its hedging properties, while the research of commodity futures is focused on its function of price discovering. Adding futures into ordinary stock portfolio has not been well discussed so that this article will research on the performance of portfolio with commodity futures to see whether futures can effectively diversify risk and raise the return. How to optimize investment portfolio becomes the first and most important question that investors need to consider. Thus, modern portfolio theory becomes quite widely applied in practice. Portfolio means investors allocate certain amount of money to different kinds of assets in order to gain as much as possible return or to get the lowest possible risk. II. Past literature review in portfolio selection theories In 1959, Markowitz published his paper named Portfolio Selection: Eficient Diversification of Investments, which conducted a pioneering study of optimizing portfolio in the security market. Ever since then, modern finance and investment decision making comes into a quantitative stage. Portfolio theory is a set of theories and methods to help investors choose certain types and allocate their money from varieties of instruments to form efficient portfolio. In Markowitz theory, mean-variance model can be applied to any class of financial assets, as long as its expected return and the correlation of each asset can be accurately estimated (Markowitz, 1959). In his model, mean represents the expected return of an asset and its risk is represented by the variance. In order to use the Markowitz mean-variance method, we need to find the expected rate of return and risk. However, considering the ineffectiveness of Chinese stock market, the simple mean-variance is not applicable. Thus, more a ppropriate method of evaluating return and risk needs to be found. Among these different evaluating methods, people tend to agree using expected return as a representative of future earnings. The return of a financial asset is consisted of two parts: intertemporal cash flows and capital premium (asset price changes during the holding period). The return that this article is going to use is the daily logarithmic rate of return, so the intertemporal cash flows can be ignored. The yield can be expressed as: Because logarithmic rate of return can be simply added which facilitate the data processing by software and its value can be any real numbers, this article will use logarithmic rate of return as the evaluation of asset yields. The simplest way to get the expected rate of return is calculating its average. Its flaws are also quite obvious: the result is far from accurate. In order to find more accurate estimation, we need to fit time series data to appropriate model and find the unconditional expectation of asset return. In 1980s and 1990s, lots of literatures have discussed the predictability of stock market and suitable model of predicting asset returns. M.Hashem Pesaran and Allan Timmermann (1995) found that the predictable components of stock returns are highly correlated with business cycle and the magnitude of shocks influences the model more than expected. But because what they studied is a long term relationship in the stock market, the results can only be a consultation. As for the daily stock return, many researches suggest that it shows significant dependence on former returns. Vedat Akgiray found in his paper about the conditional heteroscedasticity in stock returns that the probability distribution of return lag of s days is dependent on return today for several values of s (1989). He used daily returns on the CRSP (Center for Research in Security Prices) value-weighted and equal-weighted index from January 1963 to December 1986 to find th at GARCH (1,1) shows the best fit and forecast ability among the econometric models. Noticing that the return he used is also logarithmic rate, the features of logarithmic rate in this article can be expected to be just like that in his study. Similar results can be obtained from other literatures. There is a positive relation between the expected risk premium and the predictable level of volatility and a negative relation between unpredictable component of stock market risk and excess holding period return (K. R. French et al, 1987). Although they can not determine a certain model to describe the exact relation (difficult to choose between ARIMA and GARCH-M), the relation between return and risk is quite significant. Studies about Chinese stock market also show evidence of fitting stock return data in ARMA or GARCH models. The daily returns of Shanghai and Shenzhen index indicates significant ARCH effect and the data fit in GARCH-M model well (Hua Tian and Jiahe Cao, 2003). I t is reasonable to choose ARMA or GARCH model to simulate the actual stock movement. But as for the measurement of risk there are comparably various methods. Markowitz explained the mean-variance theory in his 1959 portfolio selection paper which introduced the statistical concept of expectation and variance into the study of investment portfolio. Under a certain probability distribution of returns, he used the average deviation from the average return of all the random returns. Thus, risk can be quantified with the expectation of return as return expected and standard deviation as the measurement of risk. Although variance has some easy to use features such as simple calculating and easy understanding, it is only an approximate measurement of risk. Using variance needs the distribution to be systematic and does not take the investors different feeling about capital gain and loss into consideration. Given the same amount of gain and loss, the pain of loss is usually larger tha n the happiness of the capital earnings. Variance ignores this asymmetry while LPM (lower partial moments) would be a better measurement. Harlow proposed this new indicator as a more accurate way to describe risk (1991). LPM is an abbreviation of lower partial moment, which P (partial) stands for its measuring only one side of the returns compared with the fundamental rate and L (lower) stands for less than fundamental rate (downside risk). LPM is a risk measurement which meets the requirements of Von Neumann à ¢Ã¢â€š ¬Ã¢â‚¬Å" Morgenstern utility function and can cover almost all peopleà ¢Ã¢â€š ¬Ã¢â€ž ¢s risk preference. It shows a new way to describe risk apart from the traditional utility measurement which is the function of variance or the standard deviation. The expression of LPM is: , where n is called the order of LPM indicators, representing the risk aversion of investors, and z is called fundamental rate of return which is the minimum return that investors would accept. Different values of n would change LPM into different measurements of risk and therefore meet different investorsà ¢Ã¢â€š ¬Ã¢â€ž ¢ risk preference, from risk preference to risk neutral, then risk aversion. One advantage of LPM is that it can show only the pain or loss possibility when the return is lower than the expected. The other is it can show what investorsà ¢Ã¢â€š ¬Ã¢â€ž ¢ different risk preference can affect the feelings to the same asset by simply changing the order n. LPM is less popular in evaluating volatility than variance as the calculation of LPM is more complicated. Another reason is that LPM must be calculated separately for each variable while variance can be added or processed under certain assumptions. This means people need to program it in order to use LPM with comput er data processing programs. On the contrary, all the data processing programs have a default function of calculating variance. The way to evaluating the performance of asset portfolios is its efficient frontier. Every combination of risky assets can be plotted in a risk-return space, and those combinations with the highest return under the same risk or with the lowest risk under same return are called efficient portfolios. Usually, the upper part of the curve which describes risk-return features of efficient portfolios is called efficient frontier. Ordinary efficient frontier of investment portfolio is calculated by Markowitzà ¢Ã¢â€š ¬Ã¢â€ž ¢s mean-variance method. This article will use LPM to substitute variance to calculate efficient frontier which makes it more like investorsà ¢Ã¢â€š ¬Ã¢â€ž ¢ thoughts of risk. Merriken suggested that variance and LPM are suitable for the study of short-term investment (1994), which is quite popular in Chinese capital market. Based on the review of the related literatures, this article will use econometric models to get expectation daily return of stock and futures and both variance and LPM to calculate efficient frontiers to see whether adding futures into stocks would improve the performance of portfolios. III. Theoretical study and empirical data results i. Theories of econometric models and multi-type asset portfolio The econometric models used to estimating the expected return and risk are ARMA and GARCH models depending on the features of different stock and futures time series. ARMA is an abbreviation of autoregressive and moving average model, which is typically used in estimating autocorrelated time series. As what is mentioned in the literature, auto-correlation in daily logarithmic return is shown by theoretical study, and the empirical study of the realistic data also suggests this result. Typical ARMA model is consisted of two parts: AR (auto-regressive) part and MA (moving average) part. It is normally notified as ARMA (p, q) where p is the order of autoregressive part and q is the order of moving average part. AR part is written as: , where are the parameters and is the error term (usually white noise). The value of p suggests how many lags of are regressed on and therefore is a measurement of autocorrelation. For the need of stays stationary, usually we need the absolute value of is less than unit. MA part is written as: , where are the parameters, is the expectation of , and is still the error term (usually white noise). The value of q suggests how many error terms are included in the smoothing process of average and MA process is always a stationary time series. Thus, ARMA model is written as: , which is a combination of autoregressive part and moving average part. The value of parameters is generally determined by the least square method which minimized the residual error term. The value of p and q is chosen to better fit the model without too much lags or smoothing terms. The method used in this article is through the value of ACF (autocorrelation function, which is used to determine the order of moving average) and PACF (partial autocorrelation function, which is used to determine the order of autoregressive part). In spite of autocorrelation, there are other special features of financial time series data such as fat tails, extreme values and volatility clustering. Simple ARMA models assume that the error term is independently and identically distributed which does not meet the fact. Thus, Engle (1982) posed ARCH (Autoregressive Conditional Heteroscedasticity) model to analyze this volatility feature of financial data. Four years later, T.Bollerslev improved this mod el and made it GARCH which is a generalized ARCH model. GARCH model is developed specially for financial data and is widely used in the study of volatility. In addition to the normal econometric model, people use GARCH to better analyze and forecast volatility. GARCH model can be written as: where the first equation is a simple ARMA model, but this time is not an independently and identically distributed normal error term. is an independently and identically distributed error term and is called conditional variance which is estimated by the third equation (also an ARMA model). and are independent of each other and the distribution of is not restricted as normal but can be changed to satisfy actual situation. This makes GARCH a more accurate model in estimating the expected rate of return and risk. Hiroshi Konno and Katsunari Kobayashi (1997) made an attempt to add bonds into ordinary stock portfolio to find a new way of allocating investment. Their purpose is to extend the mean-variance model normally used in optimizing stock portfolios to integrated bond-stock portfolios. At that time, big scale mean-variance models were restricted in stock portfolios although the computer technology and mathematical methods in financial engineering developed fast. Although bonds seem always to be considered separately when people intend to invest in financial market, Hiroshi and Katsunari still want to add bonds into portfolios. The reason is that before 1980s, the return of bond was far less risky than that of stock due to the stable interest rate. However, after 1980s, interest rate became much more volatile and investors bore heavily loss and huge risks. Actually, the volatility of bonds at that time was even higher than that of stocks. Considering this, combining bonds and stocks into the same portfolio is of great realistic meanings. The method they used is mean-variance and mean-absolute deviation models where variance and absolute deviation are as the diff erent measurement of risk. The results are also quite satisfied as adding bonds into stock portfolios can increase the expected return under the same risk level. Never the less, Raimond Maurer and Frank Reiner in 2001 also used this idea of multi-type asset portfolio to discuss the possible outcomes of adding real estate securities into international asset portfolios under a shortfall risk frame. They noticed the fact that financial time series data had its own features and the tradition way of evaluating risk using variance can not reflect what investors think in the reality. Therefore, LPM was introduced as the way of measuring risk to reflect the asymmetry in the rate of return of asset. They compared the situation in Germany and in US by calculating the efficient frontiers of common portfolios, then calculating the efficient frontiers of adding real estate securities into portfolios. Because they studied between different countries, Raimond Maurer and Frank Reiner also cal culated the effects of hedging. The results are also quite satisfied as the efficient frontiers move to the left, especially for those high risk-averse investors in Germany. Also, hedging could improve the performance of portfolios, especially for the US investors. With hedging they can build investment portfolios with higher rate of return under a relatively low risk level. But as mentioned above in the introduction part, there are few commercial bonds besides the government bonds; the only possible type of asset besides stocks that can be added into investment portfolios is futures. This article will also calculate the efficient frontiers of stocks, futures and combined portfolios separately, using both variance and LPM as the measurement of risk. As to the number of assets that should be held in one portfolio, investors have different opinions. Most mutual funds in the US market hold more than 100 stocks. Although these over-sized investment portfolios may well diversified risks, the expected return can be just acceptable as higher operational fee are needed to maintain such a huge portfolio and these stocks usually contains some low return ones. Xianyi Lu (2006) discussed this question that how many stocks are suitable for Chinese investors to hold in a single portfolio. He constructed portfolios with different number of stocks to compare their risk-return performance. The measurement of risk he used is variance. He came to the conclusion that 20 stocks would be enough to diversify most of the risk. The close-up price of stock is quite easily obtained while to find suitable closing price of futures is somewhat tricky. Futures are contracts which specify certain quantity and quality of fundamental assets between two parties to trade at a specified date in the future with a price agreed today. Thus there can be various contracts with the same kind of fundamental asset in different delivery date. Considering the trading characteristics of Chinese fut ure market, Chengjie Ge and Yong Liang from a Chinese fund called Guotai Junan tried to construct a continuous future contract to get the daily closing price in 2008. When a contract first comes into market, the transactions are quite few. One contract is traded most actively just three or four months before delivery date, as the coming of specified date the trading volume begins to fall quickly. Those investors, especially the speculators would only trade those contracts that so-called à ¢Ã¢â€š ¬Ã…“dominant contractà ¢Ã¢â€š ¬?. Thus, each future contract is in good liquidity only for a short time period. A continuous future contract is selecting the most actively traded contract of same fundamental asset at the same time to form a new, artificial contract to get the continuous price time series of one asset. ii. Data collection and analysis This article uses daily closing price of stock and futures from the time period 04/01/2007 to 31/12/2008. The data is obtained from RESSET database  [4] Futures chosen are copper, aluminum, rubber and fuel oil from Shanghai Future Exchange, corn and soybean meal from Dalian Future Exchange and cotton and wheat gluten from Zhengzhou Future Exchange. In order to get daily return we need to construct continuous future contracts by selecting the most active contracts. As to the 8 futures used in this article, the most active contracts of wheat gluten, soybean meal, cotton, fuel oil and corn are those contracts with delivery date four months before the current month (not accounting current month); the most transacted contracts of rubber, aluminum and copper are those with delivery date two months before the current month (still not accounting current month). For example, current time is 19970201, so the contract which should be selected for cotton is the 199705 contract whose de livery month is May 1997. When it comes to 19970301, the contract selected for cotton should be 199703, and so on, so forth. After constructing eight continuous future contracts, we can get the time series of close-up price. The calculation of logarithmic rate of return, variance and LPM is just like the stock data. Table 1 shows the descriptive statistics of futures like the mean, the standard deviation, and some others. As the bond market is not mature in China, the risk free rate that used in this article is the three-month central bank bill rate which is also from the RESSET database, same database as the closing price of stocks and futures. From the statistics in the table we can find that the logarithmic daily return of futures shows asymmetry and fat tails, far from the assumption of mean-variance model that the distribution of returns should be normal distribution, or at least a symmetric bell-shaped distribution. Thus, using variance or standard deviation or any other ki nd of symmetric statistics would be less accurate. Fitting data into econometric models should provide a better estimation of expected rate of return and risk. Table 2.1-2.4 and Table 3.1-3.3 show the estimation of coefficients using ARMA and GARCH models. The models of stock returns are mostly ARMA models, but of futures are half GARCH models and half ARMA models. Table 2 is the results of future data and table 3 is the results of stock data. From the table we can see that there are four futures which are better fit in GARCH models and for the other four, ARMA is enough as the residual series after ARMA does not show significant heteroscedasticity in error terms. As for stocks, none of the 19 stock time series show significant heteroscedasticity which means ARMA could describe the features of stock price series. One interesting finding is that only 11 stock price time series show the correlation effect while the other 8 stock price series seem to be random walk. Table 2.1 and Table 2.2 are the GARCH results of future returns. Cotton, soybean meal, aluminum and copper show significant auto correlated heteroscedasticity. The basic model that used to estimate the return is ARMA model, and the first two lags show the most correlation with current logarithmic rate of return. The null hypothesis for all the coefficient in the model is the coefficient equals zero. The constant terms in the models are not significant despite that of soybean meal whose p-value is 0.0202, which means we can reject the null hypothesis under a 5% confidence level. The reason for not able to reject the null hypothesis of constant terms equaling zero may be the absolute value of daily logarithmic rate of return is too small, usually under 0.01. In such a low level the normal test to calculating p-value may become not suitable. So the value of constant terms is still used in the ultimate model to calculate the estimation of expected return although we can not reject the possibility th at it actually equals zero. Table 2.3 and Table 2.4 show the ARMA results of future returns. Wheat gluten, corn, fuel oil and rubber daily logarithmic rate of return are estimated by ARMA model. The null hypothesis is also that any coefficient equals zero with p-value stands for the probability of making mistakes when rejecting the null hypothesis. The problem is the same with that of GARCH models as the p-values are too large to reject. But still we accept this result and make forecast using the present model. In spite of the not-so-satisfying results in the constant term, the coefficients of AR term and MA term are quite significantly different from zero which can be tell from the p-values. This is also true in futures GARCH model and stocks ARMA models. The significance of correlations in logarithmic rate of return series matches the features of financial time series and is what we would like to expect when estimating these coefficients. There are 19 stock return series to be modeled, but only 11 of them shows autocorrelation with their lags. None of these shows significant heteroscedasticity in the error terms so the model chosen is ARMA model. The constant terms of each stock return model is smaller than that of future return model, and the p-value is bigger than 0.05 as expected. The current return of four stocks out of this eleven shows significant correlation with the six and seven lags, showing the existence of cycle effects in the stock market. For these four stocks, what happened in the week before affects the price of this week more compared with other time. Other seven stocks show the ordinary one or two lags correlation. The coefficients of AR and MA part are also of great significance and the null hypothesis can be rejected. For those 8 stocks which do not show the existence of autocorrelation, the processing method is to calculate the basic descriptive statistics such as mean and variance. This method may ignore the asymmetry and fat t ails of the data, but as there is no good econometric model to estimate random walk series, this simple way has its own advantage and also of quite high accuracy in estimating the expected rate of return and risk. This article use the forecast value of each model as the expected rate of return, and the variance of the sample as the expected risk for the mean-variance model of investment portfolios. For those 4 GARCH future models, the expected risk is the forecast value of the error part model. As for those eight stocks whose logarithmic daily return series are random walk, simply use the mean as the expected rate of return and the variance as the expected rate of risk. LPM1 is using the three-month central bank bill rate as fundamental rate of return because of its risk-free characteristic. The mean-LPM model also uses the results of expected rate of return from the forecast of GARCH and ARMA models as the only change in this new model is the risk measurement from variance to LP M. Someone may argue that different econometric models could cause different estimation of expected rate of return, thus the results of efficient frontiers become not so convincing. The purpose of this article is to compare the efficient frontiers of different asset portfolios, trying to find the possible improvement of adding futures into the ordinary stock portfolios. The econometric estimation is used to construct Markowitzà ¢Ã¢â€š ¬Ã¢â€ž ¢s mean-variance model. What can be seen from Table 2 and Table 3 is that most of the assets can be fitted into ARMA model. As a matter of fact, because the absolute value of daily logarithmic rate of return is too small, the difference of constant terms between GARCH and ARMA model for the same asset is very small that can be ignored. The calculation of efficient frontiers is using MATLAB financial tool box, and the original data is what has been done above. After calculating the correlation coefficient matrix of 19 stocks and 8 futures, there is not much correlation of each asset. In fact, most of the correlations coefficients are between 0.1 to 0.3, with some of them even to be negative correlated. It suggests that the risk diversify of investment portfolios should successful using these 27 assets according to the statement of Markowitz. Table 1: Descriptive statistics of futures Soybean meal Aluminum Copper Cotton Wheat gluten Fuel oil Rubber Corn mean 0.0275 0.1074 -0.164 0.0527 0.00117 -0.0207 0.1434 0.00632 Standard deviation 1.82 1.29 2.073 1.007 0.0105 2.0109 2.11 0.928 LPM1 0.667 0.476 0.875 0.353 0.365 0.751 0.821 0.315 Skewness -0.496 -0.577 -0.326 0.237 1.19 -0.742 -0.672 0.385 kurtosis 4.45 7.445 3.31 9.12 12.57 4.63 5.60 8.515 J/B 62.15 405 10.08 746 1936 93.6 164 624 (the mean, standard deviation and LPM1 are all in percentage. LPM1 is order 1 lower partial moment with the fundamental rate is the risk free interest rate.) Table 2.1: the estimated coefficients of each model Cotton Soybean meal ARMA equation: r=c+ar(1)*r(-1)+ma(1)*e(-1) ARMA equation: r=c+ar(2)*r(-2)+ma(2)* e(-2) coefficient p-value coefficient p-value c -0.000269 0.4884 c 0.000752 0.0202 AR(1) 0.813401 0.0007 AR(2) 0.965705 0 MA(1) -0.851776 0.0001 MA(2) -0.96641 0 Variance equation: e= C(4) + C(5)*RESID(-1)^2 + C(6)*e(-1) Variance equation: e = C(4) + C(5)*RESID(-1)^2 + C(6)*e(-1) C(4) 4.83E-06 0.002 C(4) 1.18E-06 0 C(5) 0.063917 0.0002 C(5) -0.01277 0 C(6) 0.887646 0 C(6) 1.013578 0 Table 2.2: the estimated coefficients of each model (continued) Aluminum Copper ARMA equation: r=c+ar(1)*r(-1)+ar(4)*r(-4)+ma(1)*e(-1)+ma(4)*e(-4) ARMA equation: r=c+ar(1)*r(-1)+ar(2)*r( -2)+ma(1)*e(-1)+ma(2)*e(-2) coefficient p-value coefficient p-value c 0.000757 0.3249 c -0.00091 0.2975 ar(1) -0.12668 0.4223 ar(1) 0.675058 0 ar(4) 0.822443 0 ar(2) -0.48379 0.0007 ma(1) 0.117813 0.3959 ma(1) -0.7834 0 ma(4) -0.85309 0 ma(2) 0.64634 0 Variance equation: e= C(4) + C(5)*RESID(-1)^2 + C(6)*e(-1) Variance equation: e= C(4) + C(5)*RESID(-1)^2 + C(6)*e(-1) C(4) 5.20E-06 0.0208 C(4) 6.77E-06 0.0336 C(5) 0.046297 0.0048 C(5) 0.128242 0.0009 C(6) 0.942352 0 C(6) 0.860077 0 Table 2.3: the estimated coefficients of each model (continued) Wheat gluten Fuel oil ARMA equation: r=c+ar(3)*r(-3)+ma(3)*e(-3) ARMA equation: r=c+ar(2)*r(-2)+ar(3)*r(-3)+ma(2)*e(-2)+ma(3)*e(-3) coefficient p-value coefficient p-value c -2.68E-05 0.9622 c -0.00057 0.787 AR(3) 0.772531 0 AR (2) 0.421234 0.0041 AR(3) 0.514736 0.0005 MA(3) -0.747211 0 MA(2) -0.3081 0.0378 MA(3) -0.55602 0.0002 Table 2.4: the estimated coefficients of each model (continued) Rubber Corn ARMA equation: r=c+ar(2)*r(-2)+ma(2)* e(-2) ARMA equation: r=c+ar(1)*r(-1)+ma(1)*e(-1) coefficient p-value coefficient p-value c -0.00161 0.2191 c -7.88E-05 0.8534 AR(2) 0.665529 0 AR(1) -0.821529 0 MA(2) -0.556 0.0002 MA(1) 0.768263 0 Table 3.1: the estimated coefficients of each model stock 01 stock 07 ARMA model: r=c+ar(6)*r(-6)+ma(6)* e(-6) ARMA model: r=c+ar(6)*r(-6)+ma(6)* e(-6) à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value C -0.00125 0.0022 C -0.004621 0.0697 AR(6) 0.892999 0 AR(6) 0.972813 0 MA(6) -0.975019 0 MA(6) -0.980419 0 à £Ã¢â€š ¬Ã¢â€š ¬ à £Ã¢â€š ¬Ã¢â€š ¬ à £Ã¢â€š ¬Ã¢â€š ¬ à £Ã¢â€š ¬Ã¢â€š ¬ à £Ã¢â€š ¬Ã¢â€š ¬ à £Ã¢â€š ¬Ã¢â€š ¬ stock 02 stock 09 ARMA model: r=c+ar(6)*r(-6)+ar(7)*r(-7)+ma(6)*e(-6)+ma(7)*e(-7) ARMA model: r=c+ar(2)*r(-2)+ma(2)* e(-2) coefficient p-value coefficient C -0.00057 0.5591 C -0.000835 0.3755 AR(7) -0.580883 0 AR(2) -0.832358 0 AR(6) 0.306957 0.0093 MA(2) 0.768651 0 MA(7) 0.594833 0 MA(6) -0.388734 0.0007 Table 3.2: the estimated coefficients of each model (continued) stock 03 stock 11 ARMA model: r=c+ar(2)*r(-2)+ma(2)* e(-2) ARMA model: r=c+ar(2)*r(-2)+ma(2)* e(-2) à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value C -3.32E-05 0.9565 C 0.000128 0.8024 AR(2) 4.55E-01 0.0777 AR(2) 0.699258 0 MA(2) -0.567395 0.0176 MA(2) -0.763239 0 stock 04 stock 13 ARMA model: r=c+ar(1)*r(-1)+ar(2)*r(-2)+ma(1)*e(-1)+ma(2)*e(-2) ARMA model: r=c+ar(1)*r(-1)+ar(2)*r(-2)+ma(1)*e(-1)+ma(2)*e(-2) à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value C 4.12E-05 0.9448 C -0.000181 0.8822 AR(1) -1.010713 0 AR(2) 0.51781 0.0152 AR(2) -0.813403 0 AR(1) 0.214893 0.5356 MA(1) 1.080601 0 MA(2) -0.538276 0.0054 MA(2) 0.880392 0 MA(1) -0.099508 0.772 stock06 stock 15 ARMA model: r=c+ar(3)*r(-3)+ar(4)*r(-4)+ma(3)*e(-3)+ma(4)*e(-4) ARMA model: r=c+ar(1)*r(-1)+ar(2)*r(-2)+ma(1)*e(-1)+ma(2)*e(-2) à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value C -0.000439 0.6981 C -0.001621 0.5432 AR(4) -0.525641 0.0006 AR(1) 0.895432 0 AR(3) 0.273493 0.0475 AR(2) 0.097598 0.5815 MA(4) 0.611753 0 MA(1) -0.787578 0 MA(3) -0.145839 0.2854 MA(2) -0.226713 0.2128 Table 3.3: the estimated coefficients of each model (continued) stock 18 ARMA model: r=c+ar(6)*r(-6)+ar(7)*r(-7)+ma(6)*e(-6)+ma(7)*e(-7) à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value C -0.00026 0.7174 AR(6) -0.35917 0.002 AR(7) -0.45655 0.0006 MA(6) 0.261067 0.0289 MA(7) 0.505517 0.0003 Figure 1: the efficient frontiers of stock, future and mixed portfolios using mean-variance model 19 stocks portfolio 8 futures portfolio Stock and future portfolio (The green line is the efficient frontiers of 19 stocks portfolio, the purple line (in the middle) is of 8 futures portfolio and the blue line is of the mixed stock and future portfolio.) Compared these three efficient frontiers, we can find that adding futures into the ordinary stock portfolio can greatly improve the performance of portfolios, which is even greater under lower risk level. Single future portfolio also performs well compared with single stock portfolio as it can offer higher rate of return under the same risk level. From Figure 1 we can find with the same expected return of 0.4ÃÆ'—10-3, the mixed stock and future portfolio can reduce the risk from 0.012 of single stock portfolio to less than 0.006. This more than fifty percent of risk reduction shows great practical meaning of multi-type asset investment portfolios. Figure 2: the efficient frontiers of stock, future and mixed portfolios using mean-LPM model Figure 2 shows the same results as the Fig ure 1. The mixed stock and future investment portfolio can improve the risk-return performance of portfolios. Similarly, future portfolio performs much better than stock portfolio, and it can greatly raise the expected return under higher risk level. The mixed portfolioà ¢Ã¢â€š ¬Ã¢â€ž ¢s improvement is mainly under low risk level, as the risk becomes bigger, the performing difference between future portfolio and mixed portfolio are not so significant, for the efficient frontiers overlap each other. The efficient frontiers are straight lines in Figure 2 while they are curves in Figure 1. The different risk measurement may result in this. Because LPM only calculates the downside risk, the risks of the portfolios which provide same return are not the same. Every single LPM must be calculated separately. So the shape of the new efficient frontiers may look different from the traditional hyperbola-shaped curves in mean-variance models. Both the mean-variance model and the mean-LPM model show that only investing in stock market can not get as much return as investing only in future market under the same risk level because the efficient frontier of stock portfolio is to the right of that of future portfolio and the distance between the two efficient frontiers is quite large. It reveals a fact that investing only in stock market can not guarantee ideal revenue. Although twenty years has passed since the establishment of Chinese stock market, there still exist some system flaws which raise the systematic risk of stocks. Thus, 19 biggest market value stocks from the market can not efficiently diversify the risk. Chinese future market resumes development since 2001, but the return of future portfolio is quite high. The efficient frontier is to the left which suggests low risk under the same rate of return. Moreover, margin trade system is implemented in future exchange, and the average leverage ratio can reach as much as fifteen. Both of them can insure a very impressive return when investing in the future market. An argument for this result is that whether it is consistent with other stocks and futures. These two figures are based on the most representative stocks and futures of Chinese capital market. Other stocks and futures may not provide such a high return but as long as they are not correlated much with each other, this improvement can also be expected as the results come from the traditional risk diversification theory of Markowitz. Although Chinese capital market is not so mature, the results of multi-type asset investing portfolios shows the similar results as that in the US and German market. Adding different types of assets into the original single-type asset investing portfolio can extremely improve the risk-return performance, with a much obvious improvement under relatively low risk level. The difference in the methods of measuring risk does not affect this conclusion. In order to get higher return, investors should r easonably allocate their fund into stock and future market to construct a multi-type asset investing portfolios instead of investing only in stock or future market. IV. Conclusion The aim of this dissertation is to find whether adding futures into a stock portfolio can improve its risk-return performance. This article uses Chinese capital market data to construct three investment portfolios: only stock portfolio, only future portfolio and the mixed stock and futures portfolio. The way to evaluate their performance is to calculate their own efficient frontiers. The main problems that exist in Chinese capital market include as follows: the behavior of market players is not standard and their corporate structure is imperfect; market structure is irrational and dysfunctional; investorsà ¢Ã¢â€š ¬Ã¢â€ž ¢ expectation violates greatly because of some history issues such as policy-controlled market; the regulatory functions and approaches can not meet the needs of fast developing market. These problems reduce the efficiency of capital market and make the actual distribution of returns far from the assumption of using ordinary mean-variance model. Considering the immaturity of the Chinese capital market, the simple statistical mean and variance can not reflect the true value. Under this circumstances, this article use econometric model to fit the data of security and future returns, trying to find the best estimation of expected rate of return and risk. Thus ARMA and GARCH models are introduced to better estimate the expectation. Also, the traditional way of using variance as the measurement of risk has been challenged in recent years, so this article uses the LPM (lower partial moment) to measure risk, which is more reasonable in meeting the utility function of investors. The results of sample asset portfolio analysis using mean-variance model and mean-LPM model suggest that diversifying investment into different types of assets can efficiently reduce risks. Through comparing three efficient frontiers we can find that adding futures into stock portfolio can significantly increase the expected return under same risk level. Although the ri sk measurement is not the same in mean-variance model and mean-LPM model, the conclusion of multi-type asset investment portfolio reducing risk remains the same. With this conclusion, we can find that investing in any single market can not get the expected rate of return under acceptable risk level. Especially after the huge volatility of Chinese stock market during 2007-2008, investors who only built stock portfolios suffered huge losses. One possible solution is to allocate fund into different markets to construct multi-type asset investment portfolios, which ensures a higher rate of return under a relatively low risk. Considering the reality of Chinese capital market, the best choice is to invest both in stock and future market. Investors need to find suitable stocks and futures to build up portfolios according to their own preference of risk and return. Generally speaking, higher return is companied by higher risk. As to the choosing of specific stocks and futures, investors should pay attention to the less correlated ones in order to better diversify the non-systematic risk. Because of the time and knowledge restriction, this article only discusses the general performance of stock and future portfolios. The best proportion of fund allocating to each stock and future under the sample data is not calculated, and the sample studied are only stock and future market data. As the development in Chinese capital market, more and more investment tools will appear, and the investment policy would also change with the maturing of market. The new stock index future is a very useful tool in hedging portfolios. The accurate proportion of fund allocation and adding more types of asset into portfolios are the next topics to be studied.

The Morality of Sin and Nature - 902 Words

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When reading â€Å"Young Goodman Brown,† by Nathaniel Hawthorne, you come across many examples of symbolism relating to Goodman’s relationship with God, portrayed by the use of color and light, nature and its surroundings, and innocence and morality. Faith wears a pink ribbon the night Goodman leaves his house to venture into the forest. Her pink ribbon symbolizes a childlike innocence. When Goodman meets the devil, he sees Faith’s pink ribbon fluttering downRead MoreMorality And Where Is All Common Ground On Where Augustine s Views On Natural Desires And Free Will1153 Words   |  5 PagesAn Analysis of the concept of morality in Augustine reveals questions facing humankind: what is morality and where is it based. This is the common theme throughout Augustine’s writings, he seem to struggle with making the right choices. His natural desires tend to take over whenever he comes to tough decision. Throughout this paper we will be analyzing this theme, to see if we can come to some common ground on where Augustine stood and what he had to say on the issue. First let’s analyze Augustine’sRead MoreMorality And Tolerance Paper :1610 Words   |  7 PagesMorality and Tolerance Paper 1 Every person is born with a moral compass, the knowledge of right and wrong. As we grow and experience life, we get to choose what we believe is right and wrong. This becomes the foundation of your life; it’s why we do what we do. And as every person grows up and discovers his or her own principles, it’s easy to meet a person with different values, especially as Christians. But why should you, or anyone care about morality and the problems involved with tolerance?

Australian Organizations are Prone to Cybercrime for Dilemma

Question: Discuss about theAustralian Organizations are Prone to Cybercrimefor Ethical Dilemma. Answer: Introduction In this particular essay, primary concern is put forward as Australian organizations gradually becoming easygoing targets for cyber criminals. Majority of Australian organizations are turning in to low-hanging fruit as lack of appropriate controls exists for cybercrime. Therefore, an ethical controversy emerges where a decision should be made among possible actions; though possible actions are not adequate to resolve the ethical issue (Hursthouse, 2013). In this essay, this concern is analyzed in view of four ethical theories that are mentioned as utilitarianism, deontology, virtue and contract. These theories, their implications towards primary concern, critical viewpoint are discussed for raising arguments (Ferrero Sison, 2014). Moreover, the ethical outcomes and analysis is summarized for providing recommendations. The recommendations are included in later section so that ethical approach can be effective for handling cybercrime threat for Australian organizations. Australian Organizations are Prone to Cybercrime: Ethical Dilemma Discussion Background: As in current context, Deloittes Asia pacific unit leader, James Nunn-Price told that organizations were unable to report against ransomware. Ransomware locks the user from accessing systems until they make a ransom amount of payment to the attacker; rather perpetuate the crime; organizations are paying ransom amount of money (Condie, 2016). Deloitte leader also exclaimed that several Australian organizations are paying money just because the organizations are realizing payment of money is easier than investigate ransomware incident (Tonge, Kasture Chaudhari, 2013). Australian organizations have huge funding support from external and internal corporate and they can afford few hundred dollars. In this situation, Australian organizations are choosing an apparent decision with paying money to resolve the ransomware issue. The companies think they are not gullible rather they are making conscious decision. Most likely, they are sorting the problem out by just paying the atta ckers and carrying on their business (Andress Winterfeld, 2013). In this way, ransomware incident was kept under control until the number of involved accounts escalated and victim organizations reported to federal police. Former FBI cybercrime special agent Mary Galligan, declared that involved accounts were not protected well enough. The protection level was not at simple password protection or granting access and management; whereas, protection level was quite poor; causing criminals to bully banks and organizations as weakest kid on the block (Condie, 2016). CERT Australia, partner agency and computer emergency response team, combined together responded to more than 11,000 cybercrime incidents during 2014 to 2015. Tommy viljoen, leader of Deloittes risk advisory and security team told that business entrepreneurs need to understand about cyber security and finance values. Tommy Viljoen put up two different scenarios as when someone asks to fix bank account reconcilement that is under poor shape for six months and system is hit by malware and patched for few years (Miller et al., 2013). For first scenario, organization responds responsibly and promptly taking consideration of appropriate steps to res olve the issue. However, in the second scenario, organizations still cannot understand the urgency of removing malware issue from system. Therefore, risk advisory security team leader thinks that Australian organizations and banks need to conduct several activities to stop becoming easy targets for cybercrime. Implications from Utilitarianism ethical theory: Utilitarianism ethical theory is utilized for identifying major utilities for any action that would be adequate for increasing advantages of actions. As per Utilitarianism ethical theory, the Australian organizations should assess the malware and ransomware consequences and outcomes inevitable for organization. Utilitarian argues about the consequences being several numbers of individuals for given society deserving moral deliberation (Von Solms Van Niekerk, 2013). As per the theory, Australian organizations should not pay the ransom demanded by the attackers. Paying ransom is most realistic decision for resolving the issue though; paying money cannot guarantee unlocking access to stolen files. Therefore, earlier the files should have proper and secured backup storage; so that in ransomware incidents, the files can be restored from backup. Best way is to remove the victim system and remove the threat from network architecture (Chakrab arty Bass, 2015). Therefore, ethically appropriate action should be taken as not to pay ransom and remove threat by removing affected system and recover files from good backup. Implications from Deontology ethical theory: Deontology helps to analyze morality behind an action that is entirely dependent over rules and regulations for organization. Deontology ethical theory can determine some rules, policies, and regulations on which ransomware issue can be resolved (Hayry, 2013). Prone organizations should implement Symantec Endpoint Protection 12.1 (SEP 12) so that spyware protection policy can be generated for handling ransomware threats. Default policies can be edited though; the SEP and protection policy contains specific details for mitigating threat. In Virus and Spyware Protection Policy, the download protection feature can be utilized with specific modifications (Dierksmeier, 2013). Modifications in policy can result in to detection of ransomware threat and enabling suitable measures for preventing the threat. Endpoint anti-virus can be used with Virus and Spyware Protection Policy enabling quarantine the risk. Implications from Contract Ethical theory: Contract based ethical theory states societal lawfulness and originality; this theory is dependent on socio-contract model providing motivation to be moral and develop moral system with rules (Hursthouse, 2013). As per contract-based ethical theory application, bank and organizations heads and authority should be made aware about ransomware threats and its consequences. Authorities should know ransomware threat vector is spear phishing that employs unsolicited mail from unknown sender and attachment for executing the attack (Ferrero Sison, 2014). Therefore, employees should not check unidentified senders and their mail attachments and it is crucial to handle the unsolicited mails with specific actions. To resolve unsolicited mail; mail server should include filter for mail content scanning and block potential attachments that can pose major threat. Implications from Virtue Ethical theory: Character-based ethical theory pinpoints person character or virtue as primary element that is not related with rules. Therefore, as per virtue-based ethical theory, current ransomware threat can be resolved with putting user restrictions. Ransomware threat can easily peek inside mapped drive through encrypting data (Chakrabarty Bass, 2015). User access restriction can provide limitations to mapped drives so that the attack cannot encrypt files from mapped drives. Therefore, individual user should be restricted from endless access inside mapped drives of mainframe. Moreover, critical, sensitive, transactional data should be kept in secured backup. This particular backup solution should be contained in removable media and should be stored disconnected from network server (Miller et al., 2013). Removable and isolated backup solution is most important safeguarded data from ransomware threat. Conclusion and Recommendations This particular essay addressed primary situation for Australian banks and organizations facing real cybercrime threats and attacks. The essay considered consequences of this concern, duties to be performed to mitigate the concern, contract and character ethics for analyzing appropriate activities to resolve threat. Ethical theories are applied to show justification on whether the prescribed actions can be adequate with utilities, policies, socio-contract model, and virtue of individual or not. The Australian organizations should incorporate proper measures and actions to prevent ransomware threat while not paying ransom amount to resolve the threat. Therefore, applying ethical theories and proper implications of them obtains list of recommendations that could be helpful for Australian organizations to resolve ransomware threat properly. First, the organizations should incorporate removable backup storage for sensitive and critical files. Backup storage should be placed in workstation and it should be accessible during ransomware incident. Secondly, the organization should not pay ransom to the attackers. Paying ransom can never stop and prevent the attack; resulting in continuously posing threat. Thirdly, the affected systems should be removed from internal network architecture. End point should be incorporated with anti-virus solutions for enabling quarantine feature to reduce impact of threat. Finally, the mail server should be filtered for mail content scanning and stopping malicious attachments from unsolicited mails. These recommendations are justified with utilitarianism, deontology, virtue and contract ethical theories. References Andress, J., Winterfeld, S. (2013).Cyber warfare: techniques, tactics and tools for security practitioners. Elsevier. Chakrabarty, S., Bass, A. E. (2015). Comparing virtue, consequentialist, and deontological ethics-based corporate social responsibility: Mitigating microfinance risk in institutional voids.Journal of Business Ethics,126(3), 487-512. Condie, S. (2016). Australian companies 'open to cyber crime'. The Sydney Morning Herald. Retrieved 11 May 2017, from https://www.smh.com.au/it-pro/security-it/australian-companies-open-to-cyber-crime-20160201-gmiwrw.html Dierksmeier, C. (2013). Kant on virtue.Journal of Business Ethics,113(4), 597-609. Ferrero, I., Sison, A. J. G. (2014). A quantitative analysis of authors, schools and themes in virtue ethics articles in business ethics and management journals (19802011).Business Ethics: A European Review,23(4), 375-400. Hayry, M. (2013).Liberal utilitarianism and applied ethics. Routledge. Hursthouse, R. (2013). Normative virtue ethics.ETHICA,645. Miller, S., Mameli, P., Kleinig, J., Salane, D., Schwartz, A. (2013).Security and privacy: global standards for ethical identity management in contemporary liberal democratic states(p. 291). ANU Press. Tonge, A. M., Kasture, S. S., Chaudhari, S. R. (2013). Cyber security: challenges for society-literature review.IOSR Journal of Computer Engineering,2(12), 67-75. Von Solms, R., Van Niekerk, J. (2013). From information security to cyber security.Computers Security,38, 97-102.

Causal Argument Essay Samples

Causal Argument Essay SamplesCausal argument essay samples are among the most common and most needed by students in college and universities. These essays serve as an introduction to a given topic or they may be utilized as the main body of text.There are a number of uses for causal argument essay samples and some of them are more important than others. Of course, it is paramount that the student learns how to place all the ideas and arguments of the essay into one coherent pattern. A pattern is essential in order to come up with an intelligent written piece.It is quite imperative that the student should understand all the assumptions and the main points of a causal argument essay. This will enable him to make better choices and to come up with more intelligible essays. He can also become a writer.The causal argument essay samples provide different writing styles, but the main style remains the same. Most of these essays are very structured and contain a particular plot or a storylin e that builds itself on a general premise. It is vital to ensure that all the points made in the essay are logically connected.Most students choose a thesis statement as the starting point for a thesis paper and then set out to explore their chosen topic. They may do this in a more direct way, while others choose to develop their argument or a topic via a systematic approach.The main body of the essay is usually a synopsis of all the ideas that they have covered in the body of the paper. In this way, the student has the chance to come up with a proper argumentation.The more advanced readers will use this opportunity to bring in some fresh ideas, which they can use as a further part of a well-crafted persuasive argument. The argument must be stated in a simple manner and not too technical. One need to remember that he or she is developing a highly readable essay.Some of the usual themes from the causal argument essay samples include crime, sex, education, religion, and art. They are some of the most used areas of study in the college syllabus and are very much worth learning and using.

General Knowledge Essay Topics to Know

General Knowledge Essay Topics to KnowIt is important to be aware of the general knowledge essay topics that will be discussed in an essay. These will include your personal experiences, real life experiences and personal preferences.These essay topics will go into the important areas of life such as family and friends, romantic partners, work, school, religion, health, fashion, movies, food, weather, politics, movies, work, families, political affiliations, sports, songs, words, weather, parties, meals, conversations, novels, poetry, art, literature, and movies. In this article you will learn about these general knowledge essay topics. All of the examples are based on real life situations and were written by an experienced admissions writer.The main subject of your essay will most likely revolve around your opinion or point of view. It is important to write your essay in such a way that you are able to be specific about what you are going to say. You should try to be as unique as pos sible so that your reader will be able to relate to you. Do not write the exact same essay all over again.It is important to keep in mind that you are only providing information to your reader based on your personal preference and personal experiences. In this article we have looked at some of the topics that will be discussed and hopefully you will be able to create an essay on these topics.As an example, let's look at a simple essay on your personal experiences. For example, let's say that you have been in love with your boyfriend for a while now and you think that you have been cheating on him. You are willing to give up the relationship so that you can move on with your life.If you are unable to get across this important information in your essay then you might need to change your general knowledge essay topics to something more specific. If this is the case then you should also consider using your experiences in a non-traditional way. For example, you could mention that you spe nt time as a kid around the neighborhood playing in the park.It is also important to always keep in mind that the personal opinion of a person is a personal opinion. Therefore it is not important to be too personal or too over discuss a topic that is personal to you. It is important to remember that writing is writing and it is important to learn the proper techniques in order to be successful at writing.

Word Scholarship Essay Samples Secrets

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Best Writing Services Online

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Know What an Old Pro Thinks About Sample Research Paper

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