WTI crude oil option implied VaR and CVaR: An empirical application

Barone‐Adesi, Giovanni; Finta, Marinela Adriana; Legnazzi, Chiara; Sala, Carlo

doi:10.1002/for.2580

Cited by 13 publications

(4 citation statements)

References 39 publications

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“…It is widely used to assess the risk exposure of investments and is a measure for market risks especially after 1990s. The definition of VaR (Barone-Adesi, Finta, Legnazzi, & Sala, 2019;Dees, Mauro, Pesaran, & Smith, 2007) is described as the maximum expected loss of an investment at a prespecified confidence level and holding time period. The historical simulation, variance-covariance and Monte Carlo simulation methods are three universal ways to calculate a VaR.…”

Section: Var-value At Riskmentioning

confidence: 99%

Risk Analysis of China Stock Market During Economic Downturns–Based on GARCH-VaR and Wavelet Transformation Approaches

Cui

Tang

2021

Asian Economic and Financial Review

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Accurately measuring the risk of stock market is important for improving the risk management level of stock market and reducing losses for the investors, especially during economic downturns. In this paper, to study the stock market risk in economic downturns, we first adopted wavelet transformation analysis to detect and define those phases. This paper selected the Shanghai Composite Index and eleven representative sector indexes from January 1, 2008 to January 31, 2020, established GARCH models based on student-t and GED distributions, and found the volatility aggregation and the leverage effect of the stock market. This paper measured the risk of stock market by the parameter method, using conditional volatility estimated by the GARCH models, and carried out back-testing analysis and the Kupiec failure rate test on measurement accuracy of VaR. To study the stock market risk during economic downturns, this paper calculated and compared the risks of different sectors during the corresponding phases, and the results showed that the risks in real estate, industrial, telecom service sectors are much higher during financial downturns. Finally, this paper summarized the conclusion and put forward suggestions for investors from the perspective of risk management. Contribution/ Originality:This study is one of few to have investigated the stock market risks based on the division of economic period. By combining GARCH and VAR models, we studied the commonalities of China stock market risks during economic downturns, which were detected by Wavelet transformation analysis. INTRODUCTIONSince the market economy system reform in 1990s, China's economy transformed from an old-fashioned and hermetic economy to a modernized global participator. Due to the deep development of economy's globalization and intergradation, the financial system of China including the stock market has also been adversely affected by the risks all over the world (Qureshi, Kutan, Khan, & Qureshi, 2019; Zhao, Chen, & Zhang, 2019). Thus, how to prevent and control the financial risks have become critical concerns of the government (Bieszk-Stolorz & Markowicz, 2017;Huang, 2019) and analysis of the fluctuations and risks of the stock market has become one of the most important concerns of researchers (Nordin, 2020).Extant research on the analysis of the financial market risks is diverse. Some research tries to use the sensitive analysis to estimate the risks (Banda, 2019;Inanoglu & Jacobs, 2009) while some researchers use the VaR (Value at Risk), which is one of the most popular methods. With the development of models that estimate volatility, more and Asian Economic and Financial Review

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Section: Var-value At Riskmentioning

confidence: 99%

Risk Analysis of China Stock Market During Economic Downturns–Based on GARCH-VaR and Wavelet Transformation Approaches

Cui

Tang

2021

Asian Economic and Financial Review

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“…In the literature, there are various studies that use the above mentioned approach to estimate the implied VaR and the implied TVaR. We list a few of these: (a) Ahlawat ( 2012 ) extracts the probability distribution of the underlying asset price from option prices and then uses Monte Carlo simulations; (b) Barone-Adesi ( 2016 ) uses the calculation of the first derivative of the European put option with respect to its strike price (see Barone-Adesi et al, 2019a , b for the empirical analysis); and (c) Mitra ( 2015 ), after showing the link with the Breeden and Litzenberger ( 1978 ) relation, derives a closed-form relationship between option prices, VaR, and TVaR.…”

Section: Introductionmentioning

confidence: 99%

Implied value-at-risk and model-free simulation

2022

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We propose a novel model-free approach for extracting the risk-neutral quantile function of an asset using options written on this asset. We develop two applications. First, we show how for a given stochastic asset model our approach makes it possible to simulate the underlying terminal asset value under the risk-neutral probability measure directly from option prices. Specifically, our approach outperforms existing approaches for simulating asset values for stochastic volatility models such as the Heston, the SVI, and the SABR models. Second, we estimate the option implied value-at-risk (VaR) and the option implied tail value-at.risk (TVaR) of a financial asset in a direct manner. We also provide an empirical illustration in which we use S &P 500 Index options to construct an implied VaR Index and we compare it with the VIX Index.

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“…Recently, there has been much interest also in other functionals of the risk neutral distribution. Barone Adesi (2016) suggested to consider implicit Value at Risk and implicit Expected Shortfall, and extensive empirical analysis on S&P500 Index and WTI crude oil options have been provided in Barone Adesi et al (2016a) and Barone Adesi et al (2016b). Elyasiani et al (2016) introduced a risk-asymmetry index based on the normalized difference of positive and negative corridor implied volatilities and studied its empirical properties on FTSE MIB index options.…”

Section: Introductionmentioning

confidence: 99%

Implicit Expectiles and Measures of Implied Volatility

2017

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We show how to compute the expectiles of the risk neutral distribution from the prices of European call and put options. Empirical properties of these implicit expectiles are studied on a dataset of closing daily prices of FTSE MIB index options. We introduce the interexpectile difference ∆τ (X) := eτ (X) − e1−τ (X), for τ ∈ (1/2, 1], and suggest that it is a natural measure of the variability of the risk neutral distribution. We investigate its theoretical and empirical properties and compare it with the VIX index computed by CBOE. We also discuss a theoretical comparison with implicit VaR and CVaR introduced in Barone Adesi (2016).

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WTI crude oil option implied VaR and CVaR: An empirical application

Cited by 13 publications

References 39 publications

Risk Analysis of China Stock Market During Economic Downturns–Based on GARCH-VaR and Wavelet Transformation Approaches

Risk Analysis of China Stock Market During Economic Downturns–Based on GARCH-VaR and Wavelet Transformation Approaches

Implied value-at-risk and model-free simulation

Implicit Expectiles and Measures of Implied Volatility

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