Estimation Methods for Value at Risk

Using extreme value analysis, we investigate the tail risk behavior of the high‐frequency (hourly) log returns of four most popular cryptocurrencies. The analysis is conducted on high‐frequency returns data, estimating value at risk and expected shortfall with varying thresholds. We find that Ripple is the most risky cryptocurrency exhibiting the largest potential gain or loss for both positive and negative (hourly) log returns at every percentile and threshold. Bitcoin is the least risky cryptocurrency.

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“…Comprehensive reviews of estimation methods for VaR and ES are given in Nadarajah and Chan () and Nadarajah, Zhang, and Chan (), respectively.…”

Section: Modelmentioning

confidence: 99%

Extreme value analysis of high‐frequency cryptocurrencies

2019

Self Cite

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“…The definition of the Value-at-Risk risk measure, properties and drawbacks have been discussed in various research papers and books by several researchers. We list a few of them here; (McNeil, 1997; Embrechts et al , 1997; Ju and Pearson, 1999; McNeil and Frey, 2000; Alexander, 2001; Yamai and Yoshiba, 2002a, 2002b; Jorian, 2002; Krause, 2002; Acerbi, 2002; Acerbi and Tasche, 2002a, 2002b; Fernandez, 2003; Giorgio, 2004; Harmantzis et al , 2006; Lonnbark et al , 2011; Nadarajah and Chan, 2016 and Taylor, 2017). However, Value-at-Risk is often criticized, as it is not a coherent risk measure.…”

Section: Introductionmentioning

confidence: 99%

Portfolio optimization based on modified expected shortfall

Jadhav

Ramanathan

2019

SEF

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Purpose An investor is expected to analyze the market risk while investing in equity stocks. This is because the investor has to choose a portfolio which maximizes the return with a minimum risk. The mean-variance approach by Markowitz (1952) is a dominant method of portfolio optimization, which uses variance as a risk measure. The purpose of this paper is to replace this risk measure with modified expected shortfall, defined by Jadhav et al. (2013). Design/methodology/approach Modified expected shortfall introduced by Jadhav et al. (2013) is found to be a coherent risk measure under univariate and multivariate elliptical distributions. This paper presents an approach of portfolio optimization based on mean-modified expected shortfall for the elliptical family of distributions. Findings It is proved that the modified expected shortfall of a portfolio can be represented in the form of expected return and standard deviation of the portfolio return and modified expected shortfall of standard elliptical distribution. The authors also establish that the optimum portfolio through mean-modified expected shortfall approach exists and is located within the efficient frontier of the mean-variance portfolio. The results have been empirically illustrated using returns from stocks listed in National Stock Exchange of India, Shanghai Stock Exchange of China, London Stock Exchange of the UK and New York Stock Exchange of the USA for the period February 2005-June 2018. The results are found to be consistent across all the four stock markets. Originality/value The mean-modified expected shortfall portfolio approach presented in this paper is new and is a natural extension of the Markowitz’s mean-variance and mean-expected shortfall portfolio optimization discussed by Deng et al. (2009).

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Comparing the Estimations of Value-at-Risk Using Artificial Network and Other Methods for Business Sectors

Cheung

Chen

2019

Proceedings of the International Neural Networks Society

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Estimation Methods for Value at Risk

Cited by 10 publications

References 143 publications

Extreme value analysis of high‐frequency cryptocurrencies

Extreme value analysis of high‐frequency cryptocurrencies

Portfolio optimization based on modified expected shortfall

Comparing the Estimations of Value-at-Risk Using Artificial Network and Other Methods for Business Sectors

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