Dynamic semiparametric models for expected shortfall (and Value-at-Risk)

Patton, Andrew J.; Ziegel, Johanna F.; Chen, Rui

doi:10.1016/j.jeconom.2018.10.008

Cited by 207 publications

(211 citation statements)

References 32 publications

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“…Drawing on their results, we propose a novel ES (and VaR) forecast combination approach. 4 Based on a global multi-asset data set consisting of stock, bond, commodity and currency indices, our empirical study documents a clear superiority of the proposed forecast combination approach over both sophisticated and more naive standalone models using a state-of-the-art ES and VaR backtesting framework (Bayer & Dimitriadis, 2018;Berkowitz, Christoffersen, & Pelletier, 2011;Christoffersen, 1998;Christoffersen & Pelletier, 2004;Engle & Manganelli, 2004;Kupiec, 1995;McNeil & Frey, 2000;Nolde & Ziegel, 2017;Patton, Ziegel, & Chen, 2019). In the combination of ES (and VaR) forecasts, complexity seems to actually pay off as the proposed forecast combination approach outperforms a simple average forecast.…”

mentioning

confidence: 81%

“…a risk measure) is said to be "elicitable" if there exists a loss function such that the correct forecast of the functional is the solution to minimizing the expected loss (cf. Fissler & Ziegel, 2016;Gneiting, 2011;Patton et al, 2019). For example, the mean is elicitable using the quadratic loss function, and VaR is elicitable using the piecewise linear or "tick" loss function.…”

Section: Gas Modelsmentioning

confidence: 99%

“…One-Factor GAS model As a simpler variant, Patton et al (2019) introduce the one-factor GAS model, where both VaR and ES are driven only by a single variable, κ t :…”

Section: Gas Modelsmentioning

confidence: 99%

“…Similarly to Patton et al 2019, we choose the parameters of the function class in such a way that the loss differences of two forecasts are homogeneous of degree zero, given that VaR and ES are strictly negative. While Patton et al (2019) use the FZ loss function to create standalone risk models, we use it for the purpose of risk forecast combination. The FZ loss function is given by…”

Section: Fz Loss Forecast Combinationmentioning

confidence: 99%

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Estimating portfolio risk for tail risk protection strategies

Happersberger

Lohre

Nolte

2020

Euro Fin Management

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We forecast portfolio risk for managing dynamic tail risk protection strategies, based on extreme value theory, expectile regression, copula‐GARCH and dynamic generalized autoregressive score models. Utilizing a loss function that overcomes the lack of elicitability for expected shortfall, we propose a novel expected shortfall (and value‐at‐risk) forecast combination approach, which dominates simple and sophisticated standalone models as well as a simple average combination approach in modeling the tail of the portfolio return distribution. While the associated dynamic risk targeting or portfolio insurance strategies provide effective downside protection, the latter strategies suffer less from inferior risk forecasts, given the defensive portfolio insurance mechanics.

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mentioning

confidence: 81%

Section: Gas Modelsmentioning

confidence: 99%

“…One-Factor GAS model As a simpler variant, Patton et al (2019) introduce the one-factor GAS model, where both VaR and ES are driven only by a single variable, κ t :…”

Section: Gas Modelsmentioning

confidence: 99%

Section: Fz Loss Forecast Combinationmentioning

confidence: 99%

See 2 more Smart Citations

Estimating portfolio risk for tail risk protection strategies

Happersberger

Lohre

Nolte

2020

Euro Fin Management

View full text Add to dashboard Cite

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“…A P is based on Powell (1986), and similar estimators are used in Engle and Manganelli (2004) and Patton et al (2019). In the definition ofÂ P the sequenceĉ P is a potentially stochastic sequence that converges in probability to zero at a slower rate than P −1 2 .…”

Section: Theorymentioning

confidence: 99%

Backtesting Value-at-Risk and Expected Shortfall in the Presence of Estimation Error

2019

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We investigate the effect of estimation error on backtests of (multi-period) expected shortfall (ES) forecasts. These backtests are based on first order conditions of a recently introduced family of jointly consistent loss functions for Value-at-Risk (VaR) and ES. We provide explicit expressions for the additional terms in the asymptotic covariance matrix that result from estimation error, and propose robust tests that account for it. Monte Carlo experiments show that the tests that ignore these terms suffer from size distortions, which are more pronounced for higher ratios of outof-sample to in-sample observations. Robust versions of the backtests perform well, although this also depends on the choice of conditioning variables. In an application to VaR and ES forecasts for daily FTSE 100 index returns as generated by AR-GARCH, AR-GJR-GARCH, and AR-HEAVY models, we find that estimation error substantially impacts the outcome of the backtests.

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Forecasting volatility with outliers in Realized GARCH models

Cai

Peng

2020

Journal of Forecasting

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The Realized generalized autoregressive conditional heteroskedasticity (GARCH) model proposed by Hansen is often applied to forecast volatility in high-frequency financial data. It is frequently found, however, that the distribution of the estimated residuals from Realized GARCH models has peak fat-tail characteristics. Considering this feature may be a result of neglected additive outliers (AOs) and innovative outliers (IOs), this paper proposes the Realized GARCH model with additive outlier and innovative outlier (Realized GARCH-AI model) for forecasting volatility. This model can detect and correct abnormal returns and realized volatility by estimating the coefficients of volatility models and calculating the outlier test statistics. In the process of simulation, this paper considers different outlier cases in the GARCH model and the Realized GARCH model, and evaluates the performance of the proposed procedure through the accuracy of parameter estimation under different critical values. We find that the critical values will affect the results of outlier detection and correction. When the value is in a suitable range, the proposed procedure based on high-frequency data can obtain unbiased parameter estimation, and the estimation result is close to those of the intervention model containing outlier information. Finally, we use the MCS test proposed by Hansen et al. (Econometrica, 2011, 79(2), 453-497) to study the volatility prediction accuracy, value at risk, and expected shortfall prediction ability of the new model. The empirical analysis demonstrates that the proposed model produces better prediction effects than the traditional Realized GARCH model.

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Dynamic semiparametric models for expected shortfall (and Value-at-Risk)

Cited by 207 publications

References 32 publications

Estimating portfolio risk for tail risk protection strategies

Estimating portfolio risk for tail risk protection strategies

Backtesting Value-at-Risk and Expected Shortfall in the Presence of Estimation Error

Forecasting volatility with outliers in Realized GARCH models

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