Index-Exciting CAViaR: A New Empirical Time-Varying Risk Model

Value‐at‐risk (VaR) is a standard measure of market risk in financial markets. This paper proposes a novel, adaptive and efficient method to forecast both volatility and VaR. Extending existing exponential smoothing as well as GARCH formulations, the method is motivated from an asymmetric Laplace distribution, where skewness and heavy tails in return distributions, and their potentially time‐varying nature, are taken into account. The proposed volatility equation also involves novel time‐varying dynamics. Back‐testing results illustrate that the proposed method offers a viable, and more accurate, though conservative, improvement in forecasting VaR compared to a range of popular alternatives. Copyright © 2013 John Wiley & Sons, Ltd.

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“…In practice, the coefficients of the above CAViaR models may not be constant, but varying with some market regimes (cf. Huang et al , ).…”

Section: Methods For Var Forecastingmentioning

confidence: 99%

Exponentially Smoothing the Skewed Laplace Distribution for Value‐at‐Risk Forecasting

Gerlach

Huang

2013

Journal of Forecasting

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“…To assess the post-sample predictive performance of the VaR methods, we used the hit percentage and dynamic quantile (DQ) test statistic, which are two measures employed by Engle and Manganelli (2004). The hit percentage As in the empirical studies of Engle and Manganelli (2004) and Huang et al, (2010), we included four lags of the variable H t in the test's regression to deliver a DQ test statistic, which, under the null hypothesis of perfect conditional coverage, is distributed 2 with six degrees of freedom.…”

Section: Post-sample Forecasting Resultsmentioning

confidence: 99%

“…This test uses a regression framework to test whether the variable H t , defined earlier, has zero unconditional and conditional expectations. As in the empirical studies of Engle and Manganelli () and Huang et al , (), we included four lags of the variable H t in the test's regression to deliver a DQ test statistic, which, under the null hypothesis of perfect conditional coverage, is distributed χ 2 with six degrees of freedom.…”

Section: Empirical Studymentioning

confidence: 99%

Using CAViaR Models with Implied Volatility for Value‐at‐Risk Estimation

Jeon

Taylor

2011

Journal of Forecasting

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This paper proposes value-at risk (VaR) estimation methods that are a synthesis of conditional autoregressive value at risk (CAViaR) time series models and implied volatility. The appeal of this proposal is that it merges information from the historical time series and the different information supplied by the market's expectation of risk. Forecast-combining methods, with weights estimated using quantile regression, are considered. We also investigate plugging implied volatility into the CAViaR models-a procedure that has not been considered in the VaR area so far. Results for daily index returns indicate that the newly proposed methods are comparable or superior to individual methods, such as the standard CAViaR models and quantiles constructed from implied volatility and the empirical distribution of standardised residuals. We find that the implied volatility has more explanatory power as the focus moves further out into the left tail of the conditional distribution of S&P 500 daily returns.

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“…Using market data on WTI daily spot oil prices the improved CAViaR specifications performed well in a battery of evaluation criteria. Huang et al (2010) introduce a time-varying CAViaR model and using data to construct size weighted portfolios from the NYSE, AMEX and NASDAQ they show that the time-varying model provides better VaR forecasts than the constant parameter CAViaR when there are spillover effects from one market segment to other markets or market segments. Yu et al (2010) extend the CAViaR specifications using two approaches, namely the threshold and mixture type indirect-GARCH CAViaR models.…”

Section: Value-at-risk-models and Methodsmentioning

confidence: 99%

Predicting Conditional Autoregressive Value-at-Risk for Stock Markets during Tranquil and Turbulent Periods

Drakos¹,

Κουρέτας²,

Zarangas³

2015

JFRM

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This paper analyzes the predictive performance of the Conditional Autoregressive Value at Risk (CAViaR) developed by Engle & Manganelli (2004) for major equity markets during tranquil and turbulent periods. The CAViaR model shifts the focus of attention from the distribution of returns directly to the behaviour of the quantile. We compare the predictive performance of four alternative CAViaR specifications, namely Adaptive, Symmetric Absolute Value, Asymmetric Slope and Indirect GARCH(1,1) models due to Engle & Manganelli (2004) along with the improved asymmetric CAViaR (I-CAViaR) model due to Huang et al. (2009). We employ daily returns for six stock markets indices, namely S & P500, FTSE100, NIKKEI225, DAX30, CAC40 and Athens Exchange General index for the period January 2, 1995 to August 23, 2013. We compare the predictive performance of the alternative specifications for three subperiods: before, during and after the recent 2007-2009 financial crisis. The comparison is done with the use of a battery of tests which includes unconditional and conditional coverage tests, the Dynamic Quantile high-order independence test and the White (2000) empirical coverage probability and predictive quantile loss tests. The main findings of the present analysis is that the CAViaR quantile regression models and the I-CAViaR model have shown significant success in predicting the VaR measure for various periods although this performance varies over the three periods before, during and after the 2007-2009 financial crisis.

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Index-Exciting CAViaR: A New Empirical Time-Varying Risk Model

Cited by 8 publications

References 11 publications

Exponentially Smoothing the Skewed Laplace Distribution for Value‐at‐Risk Forecasting

Exponentially Smoothing the Skewed Laplace Distribution for Value‐at‐Risk Forecasting

Using CAViaR Models with Implied Volatility for Value‐at‐Risk Estimation

Predicting Conditional Autoregressive Value-at-Risk for Stock Markets during Tranquil and Turbulent Periods

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