Nonparametric Inference of Value-at-Risk for Dependent Financial Returns

Song Xi Chen

148 citations0/0 files storedJournal of Financial Econometrics

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Risk and Portfolio Optimization

Abstract

The article considers nonparametric estimation of value-at-risk (VaR) and associated standard error estimation for dependent financial returns. Theoretical properties of the kernel VaR estimator are investigated in the context of dependence. The presence of dependence affects the variance of the VaR estimates and has to be taken into consideration in order to obtain adequate assessment of their variation. An estimation procedure of the standard errors is proposed based on kernel estimation of the spectral density of a derived series. The performance of the VaR estimators and the proposed standard error estimation procedure are evaluated by theoretical investigation, simulation of commonly used models for financial returns, and empirical studies on real financial return series.

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References

1986Generalized autoregressive conditional heteroskedasticity 1999Coherent Measures of Risk 2002Correlation and Dependence in Risk Management: Properties and Pitfalls 2000Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach 1974Residual Life Time at Great Age 1997An Overview of Value at Risk 1992An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator 2001Value at Risk

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2008Developing a stress testing framework based on market risk models 2008Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall 2005A Selective Overview of Nonparametric Methods in Financial Econometrics 2016Frontiers in VaR forecasting and backtesting 2008Nonparametric estimation of conditional VaR and expected shortfall 2012Mean–CVaR portfolio selection: A nonparametric estimation framework

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