Skewness refers to a distortion or asymmetry that deviates from the symmetrical bell curve, or normal distribution, in a set of data. If the curve is shifted to the left or to the right, it is said.. * In statistical and financial literature, the issue of skewness in financial returns is not clear enough*. Most of the studies rely on the sample skewness (third central moment divided by the cube of standard deviation) and use its asymptotic distribution under normality. With this test statistic and this asymptotic distribution, symmetry is rejected in eight of the nine series of stock returns and in the three series of exchange rate returns. But these results are worthless as they. Skewness is a measure of degree of asymmetry of a distribution. It measures the degree to which a distribution leans towards the left or the right side. While skewness is a measure of asymmetry, kurtosis is a measure of the 'peakedness' of the distribution. Both skewness and kurtosis are measured relative to a normal distribution. Just like a distribution can be negatively or positively skewed, it can be Leptokurtic or Platykurtic depending on whether the peakedness is more than or less. In this paper the symmetry of daily returns is addressed in eight international stock markets and three spot exchange rates. Tests of symmetry with the sample **skewness** seem of little value, due to the non-normality of the returns. Under alternative non-normal distributions, the symmetry of the returns cannot be rejected for most markets Realized skewness is a significant indicator of returns across commodities, government bonds, and equity indices. Taking on skewness risk is broadly compensated within asset classes, as well as at..

- Skewness range from negative infinity to positive infinity & it sometimes becomes difficult for an investor to predict the trend in the data set. An analyst is forecasting the future performance of an asset using the financial model, which usually assumes that data is normally distributed, but if the distribution of data is skewed, then this model will not reflect the actual result in its assumption
- Skewness. The frequency of occurrence of large returns in a particular direction is measured by skewness. A distribution with no tail to the right or to the left is one that is not skewed in any direction. This is the same as a normal distribution i.e. a distribution which has zero skewness
- Skewness can be used to obtain approximate probabilities and quantiles of distributions (such as value at risk in finance) via the Cornish-Fisher expansion. Many models assume normal distribution; i.e., data are symmetric about the mean. The normal distribution has a skewness of zero. But in reality, data points may not be perfectly symmetric. So, an understanding of the skewness of the.
- The volatility skew, which is affected by sentiment and the supply and demand relationship of particular options in the market, provides information on whether fund managers prefer to write calls..

- Skewness risk in financial modeling is the risk that results when observations are not spread symmetrically around an average value, but instead have a skewed distribution. As a result, the mean and the median can be different. Skewness risk can arise in any quantitative model that assumes a symmetric distribution (such as the normal distribution).
- Skewness: Die Skewness (deutsch: Schiefe) ist eine Kennzahl aus der Statistik. Sie analysiert, basierend auf der Asymmetrie, inwiefern eine Häufigkeitsverteilung von der Normalverteilung abweicht. Die Normalverteilung verfügt über eine Schiefe von Null und ist somit symmetrisch. Weist eine Häufigkeitsverteilung eine Schiefe von grösser als Null auf, so liegt eine rechtsschiefe.
- group_by(asset) %>% summarize(skew_assets = skewness(returns)) %>% add_row(asset = Portfolio, skew_assets = skew_tidy$skew_byhand)%>% ggplot(aes(x = asset, y = skew_assets, colour = asset)) + geom_point() + geom_text( aes(x = Portfolio, y = skew_tidy$skew_builtin + .04), label = Portfolio, color = cornflowerblue) + # alternate geom_text() # Here's a way to label all the points # geom_text(aes(label = asset), # nudge_y = .04) labs(y = skewness
- Die Schiefe (englisch skewness bzw. skew) ist eine statistische Kennzahl, die die Art und Stärke der Asymmetrie einer Wahrscheinlichkeitsverteilung beschreibt. Sie zeigt an, ob und wie stark die Verteilung nach rechts (rechtssteil, linksschief, negative Schiefe) oder nach links (linkssteil, rechtsschief, positive Schiefe) geneigt ist

Skewness. Negative skewness means there is a substantial probability of a big negative return. Positive skewness means that there is a greater-than-normal probability of a big positive return. Copyright © 2012, Campbell R. Harvey. All Rights Reserved Whereas skewness measures symmetry in a distribution, kurtosis measures the heaviness of the tails or the peakedness. Kurtosis is useful in statistics for making inferences, for example, as to financial risks in an investment : The greater the kurtosis, the higher the probability of getting extreme values In finance and investing (and even more so in options pricing and trading), knowing skewness of return distributions is very useful, as it may indicate frequency or probability of extremely large gains and (more importantly) losses

In finance, the concept of skewness is utilized in the analysis of the distribution of the returns on investments Return on Investment (ROI) Return on Investment (ROI) is a performance measure used to evaluate the returns of an investment or compare efficiency of different investments.. Although many finance theories and models assume that the returns of securities follow a normal distribution, in reality, the returns are usually skewed Der Skewness-Wert kann positiv, null, negativ oder undefiniert sein. Bei einer unimodalen Verteilung zeigt ein negativer Versatz üblicherweise an, dass sich der Schwanz auf der linken Seite der Verteilung befindet, und ein positiver Versatz zeigt an, dass sich der Schwanz auf der rechten Seite befindet. In Fällen, in denen ein Schwanz lang, der andere fett ist, folgt die Schiefe nicht einer einfachen Regel. Zum Beispiel bedeutet ein Nullwert, dass sich die Schwänze auf beiden Seiten des.

When analysing the performance of financial securities, we give a lot of importance to the mean and the standard deviation as measures of the average return and risk, respectively. However, the risk-adjusted performance of financial securities also depends upon the risks which arise due to the shape of the distribution of their returns. These are the higher-moment risks such as the skewness and kurtosis risks, which need to be taken into consideration for proper evaluation Skewness is often thought of as a simple measure of asymmetry in the distribution of market returns. The board investigated more thoroughly the concept of skewness and how it applies to both markets and investors. OVERVIEW. Skewness is an imperfect measure of asymmetry in return distributions ** Skewness Kurtosis**. 2018-07-29 From a financial perspective, skewness is crucial since it may itself be considered as a measure of risk. For example, Kim and White (2004) stressed that, if investors prefer right‐skewed portfolios then, for equal variance, one should expect a skew premium to reward investors willing to invest in left‐skewed portfolios Skewness is used as an alternative risk measurement tool when the data is exhibits asymmetrical distribution. Skewness can either be negative or positive. A stock with negative skewness is one that generates frequent small gains and few extreme or significant losses in the time period considered. On the other hand, a stock with positive skewness is one that generates frequent small losses and.

Skewness exists in most financial markets and is an important measure of risk most likely not subsumed by HML or SMB. It is still unclear why skewness exists though several compelling arguments have been made; including, good/bad news asymmetry, price discovery, prospect theory and uncertainty of information. Negative skew had been shown to receive higher expected returns. It is generally. Why skewness in returns is important in asset pricing. Modern finance is heavily based on the unrealistic assumption of normal distribution. This discussion aims to highlight the importance of skewness in asset pricing. The primary reason skew is important is that analysis based on normal distributions incorrectly estimates expected returns and. * Coskewness and cokurtosis are statistical metrics that are used in finance to analyse how securities comove*. While skewness and kurtosis are based on data on a single variable, coskewness and cokurtosis analyse the relationship between two variables. That's why these measures are referred to as higher order cross moments or multivariate higher moments. On this page, we discuss the coskewness. with positive skewness after the financial crisis of 2008. The results for our study remain qualitatively unchanged after robustness testing. Although the financial system and the real economy received a powerful shock, together with huge investor losses, the memory seems to be short. Investors continue to pursue high gains with small probabilities by applying a premium to stocks possessing. How to use the Skewness Calculator . Let's suppose that your goal is to calculate the Skewness, given the following values: 2, 7, 15, 4, 8 . To start, enter the values in the Skewness calculator, and then click on the 'Calculate Skewness' button: You'll then get the Skewness of 0.61439:. How to Manually Calculate the Skewness

Skewness refers to the distribution of returns of a single asset while co-skewness compares the returns of the asset to the market, i.e. is the asset's returns more (positively) or less (negatively) skewed than the market's returns. Finance theory suggests investors generally prefer both positive skewness and positive co-skewness Investors use skewness to judge a return distribution as it considers the extremes of a dataset rather than focusing on the mean. Skewness is particularly useful for investors looking to invest in the short- or medium-term. These investors usually need to look at extremes as they are less likely to hold a position long enough to rely on the average working out. By looking at skewness, they can achieve that Relevance and Uses of Skewness Formula. As seen already in this article, skewness is used to describe or estimate the symmetry of data distribution. It is very important from the perspective of risk management, portfolio management, trading, and option pricing. The measure is called Skewness because the plotted graph gives a skewed display. A positive skew indicates that the extreme variables are larger than skews. The data distribution is such a way it escalates the mean value in a. included. Systematic skewness is economically important and commands a risk premium, on average, of 3.60 percent per year. Our results suggest that the mo-mentum effect is related to systematic skewness. The low expected return momen-tum portfolios have higher skewness than high expected return portfolios Thus, such a smile or skewness provides information on whether investors such as fund managers prefer to write calls or puts, as shown in the following code: Copy from pandas.io.data import Options from matplotlib.finance import quotes_historical_yahoo # Step 1: define two functions def call_data(tickrr,exp_date): x = Options(ticker,'yahoo') data= x.get_call_data(expiry=exp_date..

So the HF returns is considered as stock A and the Vix is considered as stock B. I have been using the matrices method to calculate the comoments. I need to find a formula to calculate the portfolio skewness and kurtosis.I have already calculated the skewness and kurtosis of each variable on their own. TY $\endgroup$ - user1642 Nov 8 '11 at 15:4 In time periods when skewness is positive, hedge funds have a stronger preference for negative skewness, indicative of their strong aversion to skewness. These results suggest that skewness preference in financial markets need not always be for positive skewness, which seems to be the general consensus in the literature, once the agency problem and survival and compensation motives of money managers are recognized Then what matters is the amount of skewness an asset adds to the portfolio as the market moves (coskewness measure). This leads to the Higher Moment CAPM. There is some evidence that higher moment.

In statistics, skewness is a measure of the asymmetry of the probability distribution of a random variable about its mean. If skewness is between -1 and -0.5 or between 0.5 and 1, the distribution is moderately skewed. If skewness is between -0.5 and 0.5, the distribution is approximately symmetric For both the academic and the financial communities it is a familiar stylized fact that stock market returns have negative skewness and severe excess kurtosis. This stylized fact has been supported by a vast collection of empirical studies. Given that the conventional measures of skewness and kurtosis are computed as an average and that averages. * Asymmetric Signal and Skewness Fang Zhen1 China Economics and Management Academy Central University of Finance and Economics Beijing 100081, P*.R. China Email: zhenfang@cufe.edu.cn First Version: January 2018 This Version: August 2019 Keywords: Asymmetry; Skewness; Institutional Ownership JEL Classi cation Code: G12; G14 1Corresponding author. Tel: 86 18810118307. Fang Zhen has been supported.

skewness is important and, when combined with the economy-wide reward for skewness, helps explain the time-variation of the ex ante market risk premiums. Conditional skewness has greater success in explaining the ex ante risk premium for the world portfolio than for the U.S. portfolio. 1. INTRODUCTIO portfolio-management portfolio-selection skewness statistical-finance kurtosis. asked Nov 15 '13 at 10:41. Sai. 31 1 1 bronze badge. 6. votes. 1answer 11k views How to annualize skewness and kurtosis based on daily returns. I'm trying to annualize the four moments based on a string of daily returns (continuously compounded) for 11 years. The formulas for the annualization of the mean and the. shocks to nancial skewness are important drivers of business cycles, identifying these shocks using both vector autoregressions and a dynamic stochastic general equilibrium model. Financial skewness appears to re ect the exposure of nancial rms to the economic performance of their borrowers According to Grigoletto and Lisi (2009), and from a financial perspective, skewness is crucial since it may be considered as a measure of risk. Positive skewness means that the distribution has a.

- Normal Distributions, Standard Deviations, Modality, Skewness and Kurtosis: Understanding concepts - Duration: 5:07. NurseKillam 276,051 view
- Applied to financial markets, skewness measures the degree of return asymmetry in terms of the probability distribution around the mean. In English, skewness tells us if returns have been extreme.
- A Skewed GARCH-Type Model for Multivariate Financial Time Series. In Mathematical and Statistical Methods for Actuarial Sciences and Finance. XII , Corazza M. and Pizzi C. (Eds.), ISBN: 978-88-470-1480-. French, K. R., Schwert, G. W. and Stambaugh, R. F. (1987): Expected stock returns and volatility. Journal of Financial Economics, 19, 3-29
- In Excel, skewness can be comfortably calculated using the SKEW Excel function. The only argument needed for SKEW function is the range of cells containing the data. For example the function: SKEW(B3:B102) will calculate skewness for the set of values contained in cells B3 through B102. Calculating Sample Skewness in Exce
- Abstract In this paper the symmetry of daily returns is addressed in eight international stock markets and three spot exchange rates. Tests of symmetry with the sample skewness seem of little value, due to the non-normality of the returns. Under alternative non-normal distributions, the symmetry of the returns cannot be rejected for most markets

Financial Assets When examining nancial assets it is most common to study the returns rather than the actual raw asset prices. The reasons for analysing the returns rather than the asset price are that they give us a scale-free assessment of the performance of the asset and that returns also have more attractive statistical properties for analysis. However, it is important to note that there are man Skewness, in basic terms, implies off-centre, so does in statistics, it means lack of symmetry.With the help of skewness, one can identify the shape of the distribution of data. Kurtosis, on the other hand, refers to the pointedness of a peak in the distribution curve.The main difference between skewness and kurtosis is that the former talks of the degree of symmetry, whereas the latter talks. Skewness [r]: a measure of the lobsideness of a probability distribution.Positive skewness indicates that the tail of the distribution is more stretched on the side above the mean - indicating that there are more positive than negative deviations from the mean. [e] Skewness - Occurs when a distribution is not symmetrical about its mean. A distribution is symmetrical when its median, mean. Today, the overall skewness is negative, but the rolling skewness in mid-2016 was positive and greater than 1. It took a huge plunge starting at the end of 2016, and the lowest reading was -1.65 in March of 2017, most likely caused by one or two very large negative returns when the market was worried about the US election. We can see those worries start to abate as the rolling skewness becomes. Skewness is an asymmetry measure of probability distribution of a real valued random variable. The value of skewness can be positive or negative, or even undefined. A negative skew specifies that the tail on the left side of the probability density function is longer than the right side and the size of the values probably including the median lie to the right of the mean. A positive skew specifies that the tail on the right side is longer than the left side and the size of the values lie to.

- What is Skewness in statistics? What are the different types of Skewness? To learn more about Statistics, enrol in our full course now: https://bit.ly/Stati... What are the different types of.
- We can confirm these observations using three functions from the moments package: kurtosis(),skewness(), You can now get financial data using quantmod, calculate log returns, analyze the return distribution with themoments package, and measure VaR and ES a few different ways. There are even more advanced ways to measure financial risk, and I hope to cover them in future posts. For more on.
- Skewness values and interpretation. There are many different approaches to the interpretation of the skewness values. A rule of thumb states that: Symmetric: Values between -0.5 to 0.5; Moderated Skewed data: Values between -1 and -0.5 or between 0.5 and 1; Highly Skewed data: Values less than -1 or greater than 1; Skewness in Practice. Let's calculate the skewness of three distribution. We.
- Here, skewness is the skewness, Ri is the i th return, is the mean return, n is the number of returns, and σ is the standard deviation of returns. The kurtosis reflects the impact of extreme values because a power of 4 is very high
- ate data.
- THE JOURNAL OF FINANCE •VOL. LXXV, NO. 5 OCTOBER 2020 Low-Risk Anomalies? PAUL SCHNEIDER, CHRISTIAN WAGNER, and JOSEF ZECHNER∗ ABSTRACT This paper shows that low-risk anomalies in the capital asset pricing model and in traditional factor models arise when investors require compensation for coskew-ness risk. Empirically, we ﬁnd that option-implied ex ante skewness is strongly related to.
- Applied to financial markets, skewness measures the degree of return asymmetry in terms of the probability distribution around the mean. In English, skewness tells us if returns have been extreme or not. A relatively high positive skewness reading indicates returns deep in the right tail of the distribution. A negative number equates with a loss in the left tail. In short, skewness offers a.

skewness of the U.S. business cycle has become increasingly negative. Our key contribution is to show that occasionally binding -nancial constraints, combined with a sustained increase in -nancial leverage, allow us to account for several facts associated with the evolution of business cycle asymmetry. Figure 1reports the post-WWII rate of growth of U.S. real GDP, together with the 68% and. Leverage and Deepening Business-Cycle Skewness by Henrik Jensen, Ivan Petrella, Søren Hove Ravn and Emiliano Santoro. Published in volume 12, issue 1, pages 245-81 of American Economic Journal: Macroeconomics, January 2020, Abstract: We document that the United States and other G7 economies have be.. Skewness and asymmetry in futures returns and volumes, Applied Financial Economics, Taylor & Francis Journals, vol. 18(10), pages 777-800. Amado Peiro, 2002. Skewness in individual stocks at different investment horizons , Quantitative Finance , Taylor & Francis Journals, vol. 2(2), pages 139-146 compute skewness of a univariate distribution. skewness: Skewness in R-Finance/PerformanceAnalytics: Econometric tools for performance and risk analysis. rdrr.io Find an R package R language docs Run R in your browse Skewness gives a way to attach a numerical value to the asymmetry of a distribution. One important distribution that we will examine is the exponential distribution. We will see how to prove that the skewness of an exponential distribution is 2. Exponential Probability Density Function . We begin by stating the probability density function for an exponential distribution. These distributions.

- The recent empirical works have pointed out that the realized skewness, which is the sample skewness of intraday high-frequency returns of a financial asset, serves as forecasting future returns in the cross section. Theoretically, the realized skewness is interpreted as the sample skewness of returns of a discretely observed semimartingale in a fixed interval
- cannot take values less than zero; many financial variables (income, wealth, prices) typically have right skewness since they rarely take values less than zero; and adult body weight has right skewness since most people are closer to the lower limit than to the upper limit of viable body weight. Left skewness is less common in practice, but it can occur when a variable tends to be closer to.
- Conditional heteroskedasticity, skewness and leverage effects are well known features of financial returns. The literature on factor models has often made assumptions that preclude the three effects to occur simultaneously. In this paper I propose a conditionally heteroskedastic factor model that takes into account the presence of both the conditional skewness and leverage effects

The skewness of the return distribution is one of the important features of the security price. In this paper, the authors try to explore the relationship between the skewness and the coefficient of risk premium. The coefficient of the risk premium is estimated by a GARCH-M model, and the robust measurement of skewness is calculated by Groeneveld-Meeden method Skewness is a key statistics concept you must know in the data science and analytics fields; Learn what is skewness, and why it's important for you as a data science professional . Introduction. The concept of skewness is baked into our way of thinking. When we look at a visualization, our minds intuitively discern the pattern in that chart Market skewness appears to be the most robust of the measures, with some evidence of market volatility also being robust earlier in the sample period. The effect of market kurtosis never comes through in a consistent conclusive manner. What's Inside? In an exhaustive study, the authors measure the effects of market volatility, market skewness, and market kurtosis on the cross-section of. We show how firm-level real options lead to idiosyncratic skewness in stock returns. We then document empirically that growth option variables are positive and significant determinants of idiosyncratic skewness. The real option impact on skewness is more significant in firms with lottery-type features, small size, high volatility, distressed, low return on assets, and low book-to-market ratio.

Swansea University Research Excellence Scholarships (SURES) Swansea University is proud to offer 15 fully-funded PhD scholarships for students commencing study in October 2018 or January 2019. The scholarships will be awarded on the basis of student excellence across a portfolio of 34 potential projects. Project title: Using Quantile Regressions to Obtain Skewness and Forecas Skewness in Business und Finance. Skewness ist extrem wichtig für Finanzen und Investitionen. Die meisten Datensätze, einschließlich Aktienkurse und Anlagenrenditen, weisen entweder positive oder negative Skew auf, anstatt der ausgeglichenen Normalverteilung zu folgen, die eine Schiefe von Null aufweist

- While a skewness of zero indicates symmetry, a value of -1.0 or greater is where negative skewness becomes a significant problem. As we can see, most financial assets are significantly negatively skewed. More surprising, there does not appear to be any return premium attached to this negative skewness risk, Instead, we see no relationship between return and negative skewness
- ation of utility function, construction of portfolio evaluation model, and formulation of goal program
- Skewness is the average cube deviation from the mean, divided by the cube of the standard deviation. Positive for positively skewed, negative for negative skewed, zero for symmetry. Our interest and objective is to push the distribution to the right, into the positive skewed territory to improve the odds of seeing higher, positive returns
- Skewness of S&P 500 returns using overlapping ten-year periods. The charts show the skewness coefficient of the excess log returns on the S&P 500 computed using rolling ten-year periods, starting in July 1963 and ending in June 2011, together with the 5% and 95% confidence intervals. In the top panel, the skewness for each period is computed using daily returns over the period, whereas in the lower panel the computation is done using nonoverlapping monthly returns. Confidence intervals are.
- Skewness and Kurtosis in Finance Skewness in Finance | Kurtosis in Finance | Non-Normal Returns | Skewness and Kurtosis: Excel in Finance. Search Courses. Search for: Search. Courses categories Products. Banking Risk Management 105.00 $ 88.00 $ per 365 days; Business Skills 105.00 $ 88.00 $ per 365 days; Corporate Banking 105.00 $ 88.00 $ per 365 days; Investment Banking 105.00 $ 88.00 $ per.
- ants, I estimate the effect of financial openness on growth, volatility, and skewness jointly. Finally, I relate financial openness to economic and financial development in an attempt to gauge the importance of institutional complementarities in mitigating disaster risk
- Abstract. This study documents a positive relationship between the option-implied risk-neutral skewness (RNS) of individual stock returns' distribution and future realized stock returns during the period 1996-2012. A strategy that goes long the quintile portfolio with the highest RNS stocks and short the quintile portfolio with the lowest RNS.

We compute lower skewness (by definition always negative), upper skewness (by definition always positive), and total skewness which is the sum of upper and lower skewness. We sort firms into decile portfolios based on the three option‐implied skew measures and compute the portfolios average measure of credit risk, where high (low) values generally imply high (low) credit risk. Portfolios P1 and P10 contain firms with the lowest and highest values of the sort variables, respectively. In the. Skewness is the measure of the asymmetry of an ideally symmetric probability distribution and is given by the third standardized moment. If that sounds way too complex, don't worry! Let me break it down for you ** Skewness**. It is the degree of distortion from the symmetrical bell curve or the normal distribution. It measures the lack of symmetry in data distribution. It differentiates extreme values in one versus the other tail. A symmetrical distribution will have a skewness of 0. There are two types of** Skewness**: Positive and Negativ We find a robust negative relation between skewness/lotter-like features, proxied by maximum return (MAX) over the last month, and future returns for stocks preferred by individual investors. This negative relation is nonexistent for the rest of stocks. We identify stocks preferred by individual investors through bundling 10 stock characteristics associated with their stock preferences. The negative relation between MAX and future return is produced by the stocks preferred by.

Low-Skewness Effect | Finance | Fandom. Games Movies TV Video. Wikis. Explore Wikis; Community Central; Start a Wiki; Search This wiki This wiki All wikis | Sign In Don't have an account? Register Start a Wiki. Finance. 1,214 Pages. Add new page. Wiki Content. Recently Changed Pages. Investing; Investment ; Income Tax; AUD - Australian Dollar; TMI Music Production (Record Label) Economy. Business Cycle Skewness Henrik Jenseny Ivan Petrellaz Słren Hove Ravnx Emiliano Santoro{August 2017 Abstract We document that the U.S. economy has been characterized by an increasingly negative business cycle asymmetry over the last three decades. This -nding can be explained by the concurrent increase in the -nancial leverage of households and -rms. To suppor Average skewness, which is the average of monthly skewness values across firms, performs well at predicting future market returns. This prediction still holds after controlling for the size or liquidity of the firms or for current business cycle conditions. Also, average skewness compares favorably with other economic and financial predictors of subsequent market returns. The asset allocation exercise based on predictive regressions also shows that average skewness generates superior.

Skewness is a commonly used measure of the symmetry of a statistical distribution. A negative skewness indicates that the distribution is left skewed and the mean of the data (average) is less than the median value (the 50th percentile, ranking items by value). A positive skewness would indicate the reverse; that a distribution is right skewed. A right skewed distribution would be biased towards higher values, such that the mean of the distribution will exceed the median of the distribution Our daily data shows that skewness estimates are slightly negative for most indices and individual stocks, but only a few present values significantly different from the characteristics of a normal distribution. We compare our results with skewness estimates for 21 major and emerging stock market indices around the world and find that such results are similar to other low capitalization and trading volume markets. For all the Romanian and international assets studied, the Studentized-Range. Overview: Skewness is a measure of asymmetry of a distribution. Another measure that describes the shape of a distribution is kurtosis.; In a normal distribution, the mean divides the curve symmetrically into two equal parts at the median and the value of skewness is zero.; When a distribution is asymmetrical the tail of the distribution is skewed to one side-to the right or to the left skew, skewness Term used to describe an asymmetrical probability distribution. American Banker Glossary Negative skewness means there is a substantial probability of a big negative return. Positive skewness means that there is a greater tha In particular, the paper derives the mathematical moments and other properties of the distribution and assesses its ability to fit the empirical distribution of several financial series characterized by skewness and excess kurtosis. In all cases the skewed GT provides an excellent fit to the empirical distribution of data. Previous. Back to Top