Semi-nonparametric VaR forecasts for hedge funds during the recent crisis

Brío, Esther B. Del; Mora–Valencia, Andrés; Perote, Javier

doi:10.1016/j.physa.2014.01.037

Cited by 11 publications

(3 citation statements)

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“…In this paper, the approach proposed by Fissler et al (2016) is employed. Considering that ETF returns are characterized by heavy tails, we hypothesize that asymmetrical and heavy‐tailed distributions, such as the GC mixture, will exhibit better results in terms of ES backtesting than traditional approaches such as Gaussian or Student's t. This result would be coherent with other studies, which show an adequate performance of skewed‐t distribution (Aloui & Mabrouk, 2010; Giot & Laurent, 2003) and GC distribution (Del Brio, Mora‐Valencia, & Perote, 2014a, 2014b) for VaR quantification.…”

Section: Literature Overviewsupporting

confidence: 79%

Backtesting expected shortfall for world stock indexETFswith extreme value theory andGram–Charliermixtures

2020

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This paper analyses risk quantification for three main stock market index exchange‐traded funds in world financial markets. We compare the relative performance of a set of parametric and semi‐nonparametric models in terms of both value‐at‐risk and expected shortfall backtesting techniques. To this end, we explore the result of the jointly elicitability of these two risk measures. We provide a new mixture of Gram–Charlier distributions that have been used in this framework for the first time and derive a close formula for directly computing expected shortfall. This model is compared to the Gaussian (benchmark model), Student's t, generalized Pareto (a case of the extreme value theory) and mixtures of Gram–Charlier distributions. The results show that peaks‐over‐threshold (extreme value theory) and flexible Gram–Charlier approximations are suitable to quantify market risk and mitigate concerns about possible financial instabilities generated by misuse of exchange‐traded funds trading.

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Section: Literature Overviewsupporting

confidence: 79%

Backtesting expected shortfall for world stock indexETFswith extreme value theory andGram–Charliermixtures

2020

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“…In addition, our model could be extended to search on the impact of Google Trends data on the risk contagion not only through correlation but also through all moments of the distribution. For this purpose, it can be implemented by the model of Del Brio et al [45] and Del Brio et al [46]. Lastly, it is interesting for further research to examine the contagion effect of oil prices and the exchange rate on stock markets.…”

Section: Discussionmentioning

confidence: 99%

Predicting Contagion from the US Financial Crisis to International Stock Markets Using Dynamic Copula with Google Trends

Maneejuk

Yamaka

2019

Mathematics

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The accuracy of contagion prediction has been one of the most widely investigated and challenging problems in economic research. Much effort has been devoted to investigating the key determinant of contagion and enhancing more powerful prediction models. In this study, we aim to improve the prediction of the contagion effect from the US stock market to the international stock markets by utilizing Google Trends as a new leading indicator for predicting contagion. To improve this contagion prediction, the dynamic copula models are used to investigate the structure of dependence between international markets and the US market, before, during, and after the occurrence of the US financial crisis in 2008. We also incorporate the Google Trends data as the exogenous variables in the time-varying copula equation. Thus, the ARMAX process is introduced. To investigate the predictive power of Google Trends, we employ the likelihood ratio test. Our empirical findings support that Google Trends is a significant leading indicator for predicting contagion in seven out of 10 cases: SP-FTSE, SP-TSX, SP-DAX, SP-Nikkei, SP-BVSP, SP-SSEC, and SP-BSESN pairs. Our Google-based models seem to predict particularly well the effect of the US crisis in 2008. In addition, we find that the contribution of Google Trends to contagion prediction varies among the different stock market pairs. This finding leads to our observation that the more volatile the market time-varying correlation, the more useful Google Trends.

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“…Particularly when it is calculated on a portfolio basis and transactions are added to or removed from the portfolio on a continuous basis (BCBS and IOSCO, 2013). The IM requirements might be set and determined through the use of the parametric technique such as the Gaussian probability distribution applied by Duffie and Pan (1997), student t-distribution used by Del Brio et al (2014) and delta-approximated value at risk (VaR) used by Lou (2016), among many others. Alternatively nonparametric methods may be used as well, this includes but not limited to the techniques named, kernel density estimation, bootstrap method founded by Efron and Tibshirani (1986) and later improved by Swanepoel and De Beer (1993), financial risk measures known as value at risk ( V aR α ), expected shortfall and exponential spectral risk measures (Cotter and Dowd, 2006).…”

Section: Introductionmentioning

confidence: 99%

Valuation of initial margin using bootstrap method

Seitshiro

Mashele

2020

JRF

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Purpose The purpose of this paper is to propose the parametric bootstrap method for valuation of over-the-counter derivative (OTCD) initial margin (IM) in the financial market with low outstanding notional amounts. That is, an aggregate outstanding gross notional amount of OTC derivative instruments not exceeding R20bn. Design/methodology/approach The OTCD market is assumed to have a Gaussian probability distribution with the mean and standard deviation parameters. The bootstrap value at risk model is applied as a risk measure that generates bootstrap initial margins (BIM). Findings The proposed parametric bootstrap method is in favour of the BIM amounts for the simulated and real data sets. These BIM amounts are reasonably exceeding the IM amounts whenever the significance level increases. Research limitations/implications This paper only assumed that the OTCD returns only come from a normal probability distribution. Practical implications The OTCD IM requirement in respect to transactions done by counterparties may affect the entire financial market participants under uncleared OTCD, while reducing systemic risk. Thus, reducing spillover effects by ensuring that collateral (IM) is available to offset losses caused by the default of a OTCDs counterparty. Originality/value This paper contributes to the literature by presenting a valuation of IM for the financial market with low outstanding notional amounts by using the parametric bootstrap method.

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Semi-nonparametric VaR forecasts for hedge funds during the recent crisis

Cited by 11 publications

References 57 publications

Backtesting expected shortfall for world stock indexETFswith extreme value theory andGram–Charliermixtures

Backtesting expected shortfall for world stock indexETFswith extreme value theory andGram–Charliermixtures

Predicting Contagion from the US Financial Crisis to International Stock Markets Using Dynamic Copula with Google Trends

Valuation of initial margin using bootstrap method

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