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For the KOSPI 200 index options market. we examine the power of influence on pricing options of the skewness and the kurtosis of the risk neutral distribution. We compare the Black and Scholes (1973) model which does not consider the skewness or the kurtosis of the risk neutral distribution with Corrado and sue 1996)’s model which consider both the skewness and the kurtosis and the models which consider only the skewness or the kurtosis.

It is found that Corrado and sue 1996)‘s model which consider both skewness and kurtosis shows the best performance closely followed by the model which consider only the skewness for tile in-sample pricing and the out-of-sample pricing. As a result. it contributes to pricing options to consider both skewness and kurtosis and the skewness is more important factor for pricing options than the kurtosis.

Nowadays businesses tend to compete with rivals by improving capability to meet customer demands. One of the key to improve logistics efficiency of a firm is to select appropriate supplier. In the past, to select the most suitable supplier, most people evaluated performance by using average performance or variance from historical data but did not mentioned skewness. In other words, skewness impact on supplier performance is ignored by researchers and buyers. In fact, supplier with greatest average performance does not confirm to be the most suitable one because of uncertainties which make its performance skew either to the left or right, i.e., lower or higher than expectation. Therefore, this empirical study aims to discover and determine the important role of skewness on supplier selection problem. After identifying influential criteria on supplier selection, we analyze skewness effect on suppliers’ performance in each criterion by surveying real data of suppliers’ performances. Skewness effect can be rated in 3 levels; no effect, moderately effect, and highly effect. The results show that, there is only one criterion with no skewness effect, which is price. Criteria which have high skewed performance, for both of medium-sized and large-sized buyers, are lead time, product quality and reliability, and on-time delivery. Also, skewness has higher effect on suppliers’ performance of medium-sized buyers than large-sized buyers. The conclusion surprisingly shows that, skewness is the best index to distinguish between good and bad suppliers, while mean is the worst index.

It has been demonstrated in the US market that expected market excess returns can be predicted using the average higher-order moments of all firms. This study aims to empirically test this theory in emerging markets.

Two measures of average higher moments have been used (equal-weighted and value-weighted) along with the market moments to predict subsequent aggregate excess returns using the linear as well as the quantile regression model.

The authors report that both equal-weighted skewness and kurtosis significantly predict subsequent market returns in two countries, while value-weighted average skewness and kurtosis are significant in predicting returns in four out of nine sample markets. The results for quantile regression show that the relationship between the risk variable and aggregate returns varies along the spectrum of conditional quantiles.

This is the first study that investigates the impact of third and fourth higher-order average realized moments on the predictability of subsequent aggregate excess returns in the MSCI Asian emerging stock markets. This study is also the first to analyze the sensitivity of future market returns over various quantiles.

The scalability of similarity joins is threatened by the unexpected data characteristic of data skewness. This is a pervasive problem in scientific data. Due to skewness, the uneven distribution of attributes occurs, and it can cause a severe load imbalance problem. When database join operations are applied to these datasets, skewness occurs exponentially. All the algorithms developed to date for the implementation of database joins are highly skew sensitive. This paper presents a new approach for handling data-skewness in a character- based string similarity join using the MapReduce framework. In the literature, no such work exists to handle data skewness in character-based string similarity join, although work for set based string similarity joins exists. Proposed work has been divided into three stages, and every stage is further divided into mapper and reducer phases, which are dedicated to a specific task. The first stage is dedicated to finding the length of strings from a dataset. For valid candidate pair generation, MR-Pass Join framework has been suggested in the second stage. MRFA concepts are incorporated for string similarity join, which is named as “MRFA-SSJ” (MapReduce Frequency Adaptive – String Similarity Join) in the third stage which is further divided into four MapReduce phases. Hence, MRFA-SSJ has been proposed to handle skewness in the string similarity join. The experiments have been implemented on three different datasets namely: DBLP, Query log and a real dataset of IP addresses & Cookies by deploying Hadoop framework. The proposed algorithm has been compared with three known algorithms and it has been noticed that all these algorithms fail when data is highly skewed, whereas our proposed method handles highly skewed data without any problem. A set-up of the 15-node cluster has been used in this experiment, and we are following the Zipf distribution law for the analysis of skewness factor. Also, a comparison among existing and proposed techniques has been shown. Existing techniques survived till Zipf factor 0.5 whereas the proposed algorithm survives up to Zipf factor 1. Hence the proposed algorithm is skew insensitive and ensures scalability with a reasonable query processing time for string similarity database join. It also ensures the even distribution of attributes.

The variance risk premium defined as the difference between risk neutral variance and physical variance is one of the most crucial information recovered from option prices. It does not, however, reflect the asymmetry in upside and downside movements of underlying asset returns, and also has limitation in reflecting asymmetric preference of investors over gains and losses. In this sense, this paper decomposes variance risk premium into downside - and upside-variance risk premium, and then derives the skewness risk premium and examines its effectiveness in predicting future underlying asset returns. Using KOSPI200 option prices, we obtained the following results. First, we found out that the estimated skewness risk premium has meaningful forecasting power for future stock returns, while the estimated variance risk premium has little forecasting power. Second, by utilizing our results of skewness risk premium, we developed a profitable investment strategy, which verifies the effectiveness of skewness risk premium in predicting future stock returns. In conclusion, the empirical results of this paper can contribute to the literature in that it helps us understand why variance risk premium, in most global markets except the US market, has not been successful in forecasting future stock returns. In addition, our results showing the profitability of investment strategies based on skewness risk premium can also give important implications to practitioners.

This paper investigates the effects of risk neutral distribution (RND) from option prices on the distribution of the underlying asset. More specifically, we focus on the third moment of distribution, called skewness, which contains important information predicting the jumps of stock index. The sample period covers from January 2002 to July 2006 with the closing price returns of KOSPI200 Index and the KOSPI200 options. The skewness of the risk neutral distribution is estimated from non-parametric method of Bakshi et al.(2003) and the parametric method of Corrado and Su (1996). When estimating the skewness of the underlying assets, we employ Chen et al.(2001) model and calculate the historical skewness from the1-month ahead return underlying asset. Using statistical methodology such as VAR (Vector Autoregressive model), Granger causality test, impulse response and variance decomposition model, we examine whether the skewness of the underlying asset responds to the change of the implied RND. Followings are the major findings and implications drawn from the empirical analysis of the Korean options market. First of all, skewness of options estimated from non-parametric method have information contents predicting the third-moment of KOSPI200 index return whereas skewness of options estimated from parametric method does not have any information forecasting the skewness of KOSPI200 index return.

We explore the cross-section of realized variance, skewness, and kurtosis for stock returns obtained from intraday data. We investigate the properties of the realized higher moments, and more importantly, examine relations between the realized moments and subsequent stock returns. We find evidence of a negative relation between realized skewness and next week’s returns. A strategy buying stocks in the lowest realized skewness quintile and selling stocks in the highest realized skewness quintile earns 0.79 percent per week a risk-adjusted basis. Our results on the realized skewness are robust to controls for various firm characteristics such as size and book-to-market. Little evidence exists that either the realized volatility or the realized kurtosis is significantly related to next week’s returns.

We examine the relation between investor sentiment proxies and the risk neutral skewness of S&P 500 index option. The risk neutral skewness is estimated by the method of Bakshi, Kapadia and Madan (2003), which is non-parametric method, and the interpolation-extrapolation method and trapezoidal rule is used. We use four sentiment proxies: Michigan Consumer Sentiment Index, non-commercial trader's net position of S&P 500 futures market, Baker and Wurgler (2006)'s sentiment index, and bull-bear survey of American Association of Individual Investors. We firstly conduct the regression to find the general relations of two variables, and then examine the lead-lag relation between investor sentiment proxies and risk neutral skewness through VAR analysis. Contrary to the previous studies, we observe that sentiment proxies show different signs by the economic conditions. Overall, the sentiment proxies explain the three-dimension moment better in the crisis in U.S, and especially non-commercial trader's net position of S&P 500 futures market explains bet among the proxies.

In this paper, we examine whether the risk neutral skewness and kurtosis from S&P 500 options have information for predicting the higher moments of the stock returns called skewness and kurtosis, which contain the important information for forecasting potential crash, spike upward and the fluctuations of stock index. We find that the implied risk neutral skewness and kurtosis does not provide the information contents for predicting the higher moments of S&P 500 index return, after eliminating the overlapping data. All the results are robust to the alternative measures of risk neutral moments from options prices, the sub-periods and forecasting periods.

This paper investigates the relative importance of the skewness and kurtosis of the risk neutral distribution for pricing KOSPI200 options. The skewness and kurtosis are estimated from non parametric method of Bakshi, Kapadia, and Madan (2003) and the parametric method of Corrado and Su (1996). We show that the skewness of the risk neutral distribution is more important factor than the kurtosis irrespective of the estimation method, the definition of pricing errors, the moneyness, the type of options and a period of time.