Nonparametric Estimation for Risk in Value-at-Risk Estimator

Chang, Yi‐Ping; Hung, Ming-Chin; Wu, Yi-Ming

doi:10.1081/sac-120023877

Cited by 12 publications

(3 citation statements)

References 15 publications

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“…With probability one, Var k ( CTE 1−p,k ) converges to Var( CTE 1−p,k ) (Hong (2006)). Weak convergence results for the kernel density estimatef k (9) are given by Silverman (1978) and Chang et al (2003). Hence, is a consistent estimator of and by the converging-together lemma of Durrett (1996) an asymptotically valid (1 − α) confidence region for VaR 1−p and CTE 1−p , is an elliptical region centered at ( VaR 1−p,k , CTE 1−p,k ) and is given by:…”

Section: Binomial Confidence Intervals For Value-at-riskmentioning

confidence: 99%

“…The most straightforward point estimator of VaR, which is a quantile, is a sample quantile. There is a large literature on quantile estimation which shows that more complicated estimators, such as kernel estimators and the Harrell-Davis estimator, can outperform the sample quantile (Chang et al (2003); Sheather and Marron (1990)).…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

See 1 more Smart Citation

Empirical likelihood for value-at-risk and expected shortfall

Baysal¹,

Staum

2008

JOR

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When estimating risk measures, whether from historical data or by Monte Carlo simulation, it is helpful to have confidence intervals that provide information about statistical uncertainty. We provide asymptotically valid confidence intervals and confidence regions involving value-at-risk (VaR), conditional tail expectation and expected shortfall (conditional VaR), based on three different methodologies. One is an extension of previous work based on robust statistics, the second is a straightforward application of bootstrapping, and we derive the third using empirical likelihood. We then evaluate the small-sample coverage of the confidence intervals and regions in simulation experiments using financial examples. We find that the coverage probabilities are approximately nominal for large sample sizes, but are noticeably low when sample sizes are too small (roughly, less than 500 here). The new empirical likelihood method provides the highest coverage at moderate sample sizes in these experiments.

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Section: Binomial Confidence Intervals For Value-at-riskmentioning

confidence: 99%

Section: Conclusion and Future Researchmentioning

confidence: 99%

Empirical likelihood for value-at-risk and expected shortfall

Baysal¹,

Staum

2008

JOR

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show abstract

“…The latter can be obtained by taking the required quantile of this distribution for a given history-window. The main disadvantage of this method is that it fails to capture unseen fluctuations that are not present in the utilized history-window (Chang et al 2003 ).…”

Section: Introductionmentioning

confidence: 99%

DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks

et al. 2022

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Determining and minimizing risk exposure pose one of the biggest challenges in the financial industry as an environment with multiple factors that affect (non-)identified risks and the corresponding decisions. Various estimation metrics are utilized towards robust and efficient risk management frameworks, with the most prevalent among them being the Value at Risk (VaR). VaR is a valuable risk-assessment approach, which offers traders, investors, and financial institutions information regarding risk estimations and potential investment insights. VaR has been adopted by the financial industry for decades, but the generated predictions lack efficiency in times of economic turmoil such as the 2008 global financial crisis and the COVID-19 pandemic, which in turn affects the respective decisions. To address this challenge, a variety of well-established variations of VaR models are exploited by the financial community, including data-driven and data analytics models. In this context, this paper introduces a probabilistic deep learning approach, leveraging time-series forecasting techniques with high potential of monitoring the risk of a given portfolio in a quite efficient way. The proposed approach has been evaluated and compared to the most prominent methods of VaR calculation, yielding promising results for VaR 99% for forex-based portfolios.

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Estimation Methods for Value at Risk

Chan¹

2016

Extreme Events in Finance

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Nonparametric Estimation for Risk in Value-at-Risk Estimator

Cited by 12 publications

References 15 publications

Empirical likelihood for value-at-risk and expected shortfall

Empirical likelihood for value-at-risk and expected shortfall

DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks

Estimation Methods for Value at Risk

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