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Journal articles on the topic 'And Skewness'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Eling, Martin, Simone Farinelli, Damiano Rossello, and Luisa Tibiletti. "Skewness in hedge funds returns: classical skewness coefficients vs Azzalini's skewness parameter." International Journal of Managerial Finance 6, no. 4 (2010): 290–304. http://dx.doi.org/10.1108/17439131011074459.

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2

Zaremba, Adam, and Andrzej Nowak. "Skewness preference across countries." Business and Economic Horizons 11, no. 2 (2015): 115–30. http://dx.doi.org/10.15208/beh.2015.09.

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3

Roll, Richard, and Avanidhar Subrahmanyam. "Liquidity skewness." Journal of Banking & Finance 34, no. 10 (2010): 2562–71. http://dx.doi.org/10.1016/j.jbankfin.2010.04.012.

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4

Neuberger, Anthony. "Realized Skewness." Review of Financial Studies 25, no. 11 (2012): 3423–55. http://dx.doi.org/10.1093/rfs/hhs101.

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5

Oh, Sangmin S., and Jessica A. Wachter. "Cross-Sectional Skewness." Review of Asset Pricing Studies 12, no. 1 (2021): 155–98. http://dx.doi.org/10.1093/rapstu/raab023.

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Abstract What distribution best characterizes the time series and cross-section of individual stock returns? To answer this question, we estimate the degree of cross-sectional return skewness relative to a benchmark that nests many models considered in the literature. We find that cross-sectional skewness in monthly returns far exceeds what this benchmark model predicts. However, cross-sectional skewness in long-run returns in the data is substantially below what the model predicts. We show that fat-tailed idiosyncratic events appear to be necessary to explain skewness in the data. (JEL, G10,

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6

Harvey, Campbell R., and Akhtar Siddique. "Autoregressive Conditional Skewness." Journal of Financial and Quantitative Analysis 34, no. 4 (1999): 465. http://dx.doi.org/10.2307/2676230.

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7

Gunasingham, Brindha. "Expected Idiosyncratic Skewness." CFA Digest 40, no. 2 (2010): 8–9. http://dx.doi.org/10.2469/dig.v40.n2.28.

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8

Jeong, Giho, Jangkoo Kang, and Kyung Yoon Kwon. "Liquidity skewness premium." North American Journal of Economics and Finance 46 (November 2018): 130–50. http://dx.doi.org/10.1016/j.najef.2018.04.015.

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9

Kallersjo, Mari, James S. Farris, Arnold G. Kluge, and Carol Bult. "SKEWNESS AND PERMUTATION." Cladistics 8, no. 3 (1992): 275–87. http://dx.doi.org/10.1111/j.1096-0031.1992.tb00071.x.

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10

Jondeau, Eric, Qunzi Zhang, and Xiaoneng Zhu. "Average skewness matters." Journal of Financial Economics 134, no. 1 (2019): 29–47. http://dx.doi.org/10.1016/j.jfineco.2019.03.003.

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11

Langlois, Hugues. "Measuring skewness premia." Journal of Financial Economics 135, no. 2 (2020): 399–424. http://dx.doi.org/10.1016/j.jfineco.2019.06.002.

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12

Boyer, Brian, Todd Mitton, and Keith Vorkink. "Expected Idiosyncratic Skewness." Review of Financial Studies 23, no. 1 (2009): 169–202. http://dx.doi.org/10.1093/rfs/hhp041.

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13

Yoon, Jungah, Xinfeng Ruan, and Jin E. Zhang. "The Skewness Risk in the Energy Market." Journal of Risk and Financial Management 14, no. 12 (2021): 620. http://dx.doi.org/10.3390/jrfm14120620.

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In this paper, we study the skewness risk and its return predictability in the energy market. Skewness risk is often used to measure the possibility of market crash. We study both physical skewness (market skewness and cross-sectional average realized skewness) estimated from underlying stock returns and risk-neutral skewness evaluated from the options market. We find a significant positive relationship between one-month-ahead market return and average realized skewness in the energy market. This unique feature should be noted by investors and carefully considered by energy policymakers.

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14

Loperfido, Nicola. "On a Vector-Valued Measure of Multivariate Skewness." Symmetry 13, no. 10 (2021): 1817. http://dx.doi.org/10.3390/sym13101817.

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The canonical skewness vector is an analytically simple function of the third-order, standardized moments of a random vector. Statistical applications of this skewness measure include semiparametric modeling, independent component analysis, model-based clustering, and multivariate normality testing. This paper investigates some properties of the canonical skewness vector with respect to representations, transformations, and norm. In particular, the paper shows its connections with tensor contraction, scalar measures of multivariate kurtosis and Mardia’s skewness, the best-known scalar measure

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15

DeLisle, R. Jared, and Nathan Walcott. "The Role of Skewness in Mergers and Acquisitions." Quarterly Journal of Finance 07, no. 01 (2017): 1740001. http://dx.doi.org/10.1142/s2010139217400018.

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Investors prefer stocks with idiosyncratic skewness in their returns, which may be evidence of behavioral biases. Previous research suggests that skewness is related to the choice of target in corporate acquisitions, which may reflect CEOs’ behavioral biases. However, if the acquiring firms’ stock returns are also skewed, then the acquirer CEOs may rationally use their stock as currency in these deals. We investigate the skewness of the acquiring firm and the method of payment to determine if takeovers involving high skewness stocks are consistent with shareholder wealth maximization. We find

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16

Li, Zheng, and Jhon Galdames-Retamal. "On IoT-Friendly Skewness Monitoring for Skewness-Aware Online Edge Learning." Applied Sciences 11, no. 16 (2021): 7461. http://dx.doi.org/10.3390/app11167461.

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Machine learning techniques generally require or assume balanced datasets. Skewed data can make machine learning systems never function properly, no matter how carefully the parameter tuning is conducted. Thus, a common solution to the problem of high skewness is to pre-process data (e.g., log transformation) before applying machine learning to deal with real-world problems. Nevertheless, this pre-processing strategy cannot be employed for online machine learning, especially in the context of edge computing, because it is barely possible to foresee and store the continuous data flow on IoT dev

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17

Kim, Sol. "Skewness of Kurtosis?: Using Corrado and Su (1996)‘s Model." Journal of Derivatives and Quantitative Studies 16, no. 1 (2008): 1–20. http://dx.doi.org/10.1108/jdqs-01-2008-b0001.

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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 consid

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18

Adcock, C. J., and K. Shutes. "An analysis of skewness and skewness persistence in three emerging markets." Emerging Markets Review 6, no. 4 (2005): 396–418. http://dx.doi.org/10.1016/j.ememar.2005.09.004.

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19

Withers, Christopher S., and Saralees Nadarajah. "Bias-reduced estimates for skewness, kurtosis, L-skewness and L-kurtosis." Journal of Statistical Planning and Inference 141, no. 12 (2011): 3839–61. http://dx.doi.org/10.1016/j.jspi.2011.06.024.

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20

Bali, Turan G., and Scott Murray. "Does Risk-Neutral Skewness Predict the Cross-Section of Equity Option Portfolio Returns?" Journal of Financial and Quantitative Analysis 48, no. 4 (2013): 1145–71. http://dx.doi.org/10.1017/s0022109013000410.

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AbstractWe investigate the pricing of risk-neutral skewness in the stock options market by creating skewness assets comprised of two option positions (one long and one short) and a position in the underlying stock. The assets are created such that exposure to changes in the underlying stock price (delta), and exposure to changes in implied volatility (vega) are removed, isolating the effect of skewness. We find a strong negative relation between risk-neutral skewness and the skewness asset returns, consistent with a positive skewness preference. The returns are not explained by well-known mark

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21

Del Viva, Luca, Eero Kasanen, and Lenos Trigeorgis. "Real Options, Idiosyncratic Skewness, and Diversification." Journal of Financial and Quantitative Analysis 52, no. 1 (2017): 215–41. http://dx.doi.org/10.1017/s0022109016000703.

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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. We also find that expectation on idiosyncratic skewness is associated with lower Sharpe ratios. This suggests investors are willing to sacrifice mean-variance portfolio efficiency for greater skewness de

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22

García, V., M. Martel-Escobar, and F. Vázquez-Polo. "A Note on Ordering Probability Distributions by Skewness." Symmetry 10, no. 7 (2018): 286. http://dx.doi.org/10.3390/sym10070286.

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This paper describes a complementary tool for fitting probabilistic distributions in data analysis. First, we examine the well known bivariate index of skewness and the aggregate skewness function, and then introduce orderings of the skewness of probability distributions. Using an example, we highlight the advantages of this approach and then present results for these orderings in common uniparametric families of continuous distributions, showing that the orderings are well suited to the intuitive conception of skewness and, moreover, that the skewness can be controlled via the parameter value

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23

Gao, Xiang, Kees G. Koedijk, and Zhan Wang. "Volatility-Dependent Skewness Preference." Journal of Portfolio Management 48, no. 1 (2021): 43–58. http://dx.doi.org/10.3905/jpm.2021.1.295.

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24

Hong, Chong Sun, and Jae Hyun Sung. "Multivariate skewness and kurtosis." Journal of the Korean Data And Information Science Sociaty 29, no. 1 (2018): 71–81. http://dx.doi.org/10.7465/jkdi.2018.29.1.71.

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25

Rayner, J. C. W., D. J. Best, and K. L. Mathews. "Interpreting the skewness coefficient." Communications in Statistics - Theory and Methods 24, no. 3 (1995): 593–600. http://dx.doi.org/10.1080/03610929508831509.

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26

Wu, Zhang. "Detection of skewness shift." International Journal of Quality & Reliability Management 12, no. 7 (1995): 65–74. http://dx.doi.org/10.1108/02656719510093574.

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27

Geng, Xiurui, Lingbo Meng, Lin Li, Luyan Ji, and Kang Sun. "Momentum Principal Skewness Analysis." IEEE Geoscience and Remote Sensing Letters 12, no. 11 (2015): 2262–66. http://dx.doi.org/10.1109/lgrs.2015.2465814.

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28

XU, JIANGUO. "Price Convexity and Skewness." Journal of Finance 62, no. 5 (2007): 2521–52. http://dx.doi.org/10.1111/j.1540-6261.2007.01283.x.

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29

Juszkiewicz, R., F. R. Bouchet, and S. Colombi. "Skewness induced by gravity." Astrophysical Journal 412 (July 1993): L9. http://dx.doi.org/10.1086/186927.

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30

Levi, Federico. "The discovery of skewness." Nature Physics 14, no. 2 (2018): 108. http://dx.doi.org/10.1038/nphys4354.

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31

Holgersson, H. E. T. "Robust Testing for Skewness." Communications in Statistics - Theory and Methods 36, no. 3 (2007): 485–98. http://dx.doi.org/10.1080/03610920601001774.

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32

Lehnert, Thorsten, and Yuehao Lin. "Skewness Term-Structure Tests." Applied Mathematical Finance 23, no. 6 (2016): 484–504. http://dx.doi.org/10.1080/1350486x.2017.1310624.

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33

Sulewski, P. "On Differently Defined Skewness." Computational Methods in Science and Technology 14, no. 1 (2008): 39–46. http://dx.doi.org/10.12921/cmst.2008.14.01.39-46.

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34

Peiró, Amado. "Skewness in financial returns." Journal of Banking & Finance 23, no. 6 (1999): 847–62. http://dx.doi.org/10.1016/s0378-4266(98)00119-8.

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35

Martin, George A., and Richard B. Spurgin. "Skewness in Asset Returns." Journal of Alternative Investments 1, no. 2 (1998): 66–75. http://dx.doi.org/10.3905/jai.1998.407854.

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36

Pedro Brito, Rui, Hélder Sebastião, and Pedro Godinho. "Efficient skewness/semivariance portfolios." Journal of Asset Management 17, no. 5 (2016): 331–46. http://dx.doi.org/10.1057/jam.2016.9.

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37

Dertwinkel-Kalt, Markus, and Mats Köster. "Salience and Skewness Preferences." Journal of the European Economic Association 18, no. 5 (2019): 2057–107. http://dx.doi.org/10.1093/jeea/jvz035.

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Abstract Whether people seek or avoid risks on gambling, insurance, asset, or labor markets crucially depends on the skewness of the underlying probability distribution. In fact, people typically seek positively skewed risks and avoid negatively skewed risks. We show that salience theory of choice under risk can explain this preference for positive skewness, because unlikely, but outstanding payoffs attract attention. In contrast to alternative models, however, salience theory predicts that choices under risk not only depend on the absolute skewness of the available options, but also on how sk

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38

Zhen, Fang. "Asymmetric signals and skewness." Economic Modelling 90 (August 2020): 32–42. http://dx.doi.org/10.1016/j.econmod.2020.04.026.

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39

Seglen, Per O. "The skewness of science." Journal of the American Society for Information Science 43, no. 9 (1992): 628–38. http://dx.doi.org/10.1002/(sici)1097-4571(199210)43:9<628::aid-asi5>3.0.co;2-0.

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40

Iriyadi, Deni. "EQUATING METHOD FOR LEARNING OUTCOMES OF ELEMENTARY SCHOOL/MADRASAH STUDENTS." JISAE: Journal of Indonesian Student Assessment and Evaluation 8, no. 1 (2022): 45–54. http://dx.doi.org/10.21009/jisae.v8i1.25361.

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This study aims to determine the method of equalizing a good score on a small number of items that are often found at the elementary/madrasah level. This research is a simulation study conducted by comparing capabilities in terms of three distributions. The sample size used in this study was 50 responses for the distribution of the ability of each group (normal, positive skewness, and negative skewness). Replication was carried out in 50 for each ability group using the help of Wingen3. The indicator used to evaluate the results of the equation is the Root mean squared error. The results showe

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41

Kim, Jungmu, and Yuen Jung Park. "Individual Investors, Average Skewness, and Market Returns." Sustainability 12, no. 20 (2020): 8357. http://dx.doi.org/10.3390/su12208357.

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Understanding individual investors’ short-term behavior toward skewness is essential for the management and investment of corporate social responsibility because the skewness-seeking behavior of individual investors, which causes a bubble in the market, makes the market as a whole more vulnerable, and it is difficult for the market to be sustainable. In the Korean stock market, we investigated whether average skewness can predict future market returns at the market level and whether the mispricing is associated with demand for the skewness of individual noise traders. Measuring the demand for

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42

Bali, Turan G., Luca Del Viva, Neophytos Lambertides, and Lenos Trigeorgis. "Growth Options and Related Stock Market Anomalies: Profitability, Distress, Lotteryness, and Volatility." Journal of Financial and Quantitative Analysis 55, no. 7 (2019): 2150–80. http://dx.doi.org/10.1017/s0022109019000619.

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We provide new evidence on the economic role of growth options behind the profitability, distress, lotteryness, and volatility anomalies. We use idiosyncratic skewness to measure growth options and estimate expected idiosyncratic skewness capturing investors’ expectations about the firm’s mix of growth options versus assets-in-place. We find that investors require a positive premium to hold stocks of inflexible firms with low growth options and negative expected skewness and that a newly proposed skewness factor based on growth options explains the aforementioned anomalies. Thus, the new measu

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43

SARKAR, BIKASH KUMAR, INDRANIL BHATTACHARYYA, ANANDA SARKAR, and PARTHA SARATHI MAJUMDAR. "Determination of the Order of Kinetics of a DTA Curve by Using the Concept of Skewness." Oriental Journal Of Chemistry 36, no. 6 (2020): 1235–39. http://dx.doi.org/10.13005/ojc/360632.

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In the present paper, a novel way of finding out the order of kinetics of differential thermal analysis (DTA) curves by using the concept of skewness (Sk) has been investigated. It is found that for a particular DTA peak, skewness is a function of both the order of kinetics and the quantity, First order DTA peaks are characterized by negative skewness whereas those for the second order are characterized by positive skewness. Therefore, skewness can be used as an indicator of the order of kinetics of a DTA peak. We have evaluated and compared the orders of kinetics of some reported DTA peaks us

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44

Yang, Taoyi. "Skewness in the Cryptocurrency Market." BCP Business & Management 21 (July 20, 2022): 425–32. http://dx.doi.org/10.54691/bcpbm.v21i.1268.

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The cryptocurrency market is generally accepted in the world, and its price has soared and plummeted sharply. Meanwhile, skewness is an index reflecting the rapid rise and fall of asset prices in a short period. Studying the relationship between the skewness of cryptocurrency and its returns can help risk evaders expect bad news and provide a reference for risk enthusiasts to make investment decisions. Therefore, through univariate combination analysis, this paper groups cryptocurrency according to the skewness of the previous month before buying and holding it in the next week. Moreover, the

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45

Zhang, Yaojie, Yu Wei, and Benshan Shi. "The pricing of loan insurance based on the Gram-Charlier option model." China Finance Review International 8, no. 4 (2018): 425–40. http://dx.doi.org/10.1108/cfri-10-2017-0210.

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PurposeThe purpose of this paper is to develop a loan insurance pricing model allowing for the skewness and kurtosis existing in underlying asset returns.Design/methodology/approachUsing the theory of Gram-Charlier option, the authors first derive a closed-form solution of the Gram-Charlier pricing model. To address the difficulties in implementing the pricing model, the authors subsequently propose an iterative method to estimate skewness and kurtosis in practical application, which shows a relatively fast convergence rate in the empirical test.FindingsNot only the theoretical analysis but al

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46

Prasanna, G. S. Shai, J. L. Joshi, and Ajish Muraleedharan. "Utilizing Genetic Traits Distributions to Enhance Rice Breeding Programs: A Study of Skewness and Kurtosis in Segregating Generations." Journal of Experimental Agriculture International 46, no. 9 (2024): 741–60. http://dx.doi.org/10.9734/jeai/2024/v46i92871.

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This study analyzed the genetic basis of skewness and kurtosis in segregating generations of six rice crosses. Skewness and kurtosis values were calculated for ten biometrical traits across the populations. The traits examined included plant height, number of productive tillers, panicle length, grain yield, and grain dimensions. Results showed diverse patterns of skewness and kurtosis across traits, crosses and generations. Platykurtic distributions were predominant for most traits, suggesting flatter distributions with lighter tails compared to normal. Skewness varied between positive and neg

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47

Kim, Sol, Hye-Hyun Park, and Ki-Jung Eom. "Information Flow between Stock Skew and Options Risk Neutral Skew: Evidence from KOSPI200 Options." Journal of Derivatives and Quantitative Studies 21, no. 1 (2013): 97–133. http://dx.doi.org/10.1108/jdqs-01-2013-b0004.

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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 t

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48

Eltehiwy, Mahmoud A., and Abu-Bakr A. AbdulMotaal. "A New coefficient of Skewness for grouped data." Biometrics & Biostatistics International Journal 9, no. 2 (2020): 54–59. http://dx.doi.org/10.15406/bbij.2020.09.00300.

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The primary objective of this paper is to introduce a new measure for detecting skewness for grouped data, which is simpler than the current measures in its application. The new proposed coefficient of skewness based on the cumulative frequency data and hence uses more information from the tails of the distribution and thus will be more appropriate to detect asymmetry in the data. Another advantage of the new statistic is that it is bounded by -1 and +1; hence, the coefficients of skewness can be interpreted easily. Simulation study is employed to assess the performance of the proposed coeffic

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49

Tarikere, Kirankumar, and Akhil Dattatraya Joshi. "Relationship between Karl Pearson’s Coefficient of Skewness (Sk) and Ginni’s Coefficient of Concentration (G) under the purview of Digital Payments in India." Asian Journal of Probability and Statistics 23, no. 1 (2023): 28–45. http://dx.doi.org/10.9734/ajpas/2023/v23i1495.

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Aims: Statistics is one of the most important fields. It carries out observation, collection, analysis and interpretation of collected data with necessary techniques and tools for analysis. There are many parameters which help in analyzing the data. Amongst them, Karl Pearson’s Coefficient of Skewness and Ginni’ Coefficient of Concentration are very important. Karl Pearson’s Coefficient of Skewness plays an important role in measuring the symmetry. Symmetry and its measure help in knowing the manner of distribution carried out by the population. Karl Pearson’s Coefficient of Skewness is repres

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50

Guo, Tong. "Extreme Precipitation Strongly Impacts the Interaction of Skewness and Kurtosis of Annual Precipitation Distribution on the Qinghai–Tibetan Plateau." Atmosphere 13, no. 11 (2022): 1857. http://dx.doi.org/10.3390/atmos13111857.

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Characterizing extreme precipitation precisely is crucial for predicting vegetation response to drought or storms. However, current precipitation generators in vegetation models do not simulate the occurrence and amount of extreme precipitation well. This study examined the effects of extreme precipitation on the skewness, kurtosis, and skewness–kurtosis interaction of annual precipitation distribution. The examination was based on theoretical calculations and monitoring data from 78 meteorological stations on the Qinghai–Tibetan Plateau (QTP). The results showed that extreme precipitation gen

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