"Modeling and Backtesting of Liquidity-Adjusted Value at Risk- A Quantile Regression Approach"
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2019-08-12
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en
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Value-at-Risk (VaR) has received far-reaching attention in internal risk management practice as a measure of market risk in the capital markets environment as well as credit business. Different methods are used in the literature to determine VaR, of which "Historical Simulation" and the "Variance-Covariance Approach" are the most commonly used approaches (Hong et al., 2014)). This thesis ties on recent research testing the accuracy of Quantile Regressions to estimate VaR. Via Quantile Regressions the conditional quantile in question can be modeled directly without making any assumptions about the distribution of the return series (Hagoum et al., 2014). We analyze three different Quantile Regression models, HAR-QREG, Symmetric CaViaR-QREG and Asymmetric CaViaR-QREG. The HAR-QREG model isolates the effect of short-, mid- and long-term volatility in order to asses market risk. Symmetric CaViaR-QREG and Asymmetric CaViaR-QREG include an autoregressive term to capture volatility clusters also in the tails (Rubia & Sanchis-Marco, 2013). We modify the models incorporating liquidity measures to estimate the impact of liquidity on market risk. The results provide evidence for Quantile Regressions as a proper tool to forecast VaR. Our findings also support that liquidity costs drive market risk.
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Faculteit der Managementwetenschappen