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On the use of power transformations in CAViaR models

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  • Georgios Tsiotas

Abstract

Value at risk (VaR) is a risk measure widely used by financial institutions in allocating risk. VaR forecast estimation involves the conditional evaluation of quantiles based on the currently available information. Recent advances in VaR evaluation incorporate a proxy for conditional variance, yielding the conditional autoregressive VaR (CAViaR) models. However, early work in finance literature has shown that the introduction of power transformations has resulted in improvements in volatility forecasting. Having a direct association between volatility and conditional VaR, we adopt power‐transformed CAViaR models. We investigate whether the flexible conditional VaR dynamics associated with power‐transformed CAViaR models can result in better forecasting results than those assumed by the nontransformed CAViaR models. Estimation in CAViaR models is based on an early‐rejection Markov chain Monte Carlo algorithm. We illustrate our forecasting evaluation results using simulated and financial daily return data series. The results demonstrate that there is strong evidence that supports the use of power‐transformed CAViaR models when forecasting VaR.

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  • Georgios Tsiotas, 2020. "On the use of power transformations in CAViaR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 296-312, March.
    • Handle: RePEc:wly:jforec:v:39:y:2020:i:2:p:296-312
    DOI: 10.1002/for.2627
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    1. Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models," Biometrika, Biometrika Trust, vol. 95(1), pages 169-186.
    2. J. A. John & N. R. Draper, 1980. "An Alternative Family of Transformations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 190-197, June.
    3. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    4. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    5. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    6. James W. Taylor, 2019. "Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 121-133, January.
    7. Mu, Yunming & He, Xuming, 2007. "Power Transformation Toward a Linear Regression Quantile," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 269-279, March.
    8. Higgins, Matthew L & Bera, Anil K, 1992. "A Class of Nonlinear ARCH Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 33(1), pages 137-158, February.
    9. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    10. Georgios Tsiotas, 2018. "A Bayesian encompassing test using combined value-at-risk estimates," Quantitative Finance, Taylor & Francis Journals, vol. 18(3), pages 395-417, March.
    11. Yu, Jun & Yang, Zhenlin & Zhang, Xibin, 2006. "A class of nonlinear stochastic volatility models and its implications for pricing currency options," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2218-2231, December.
    12. Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
    13. Tingguo Zheng & Tao Song, 2014. "A Realized Stochastic Volatility Model With Box-Cox Transformation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 593-605, October.
    14. Philip Heidelberger & Peter D. Welch, 1983. "Simulation Run Length Control in the Presence of an Initial Transient," Operations Research, INFORMS, vol. 31(6), pages 1109-1144, December.
    15. Huixia Judy Wang & Deyuan Li, 2013. "Estimation of Extreme Conditional Quantiles Through Power Transformation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1062-1074, September.
    16. Nelson, Harold Jr. & Granger, C. W. J., 1979. "Experience with using the Box-Cox transformation when forecasting economic time series," Journal of Econometrics, Elsevier, vol. 10(1), pages 57-69, April.
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    1. Puneet Prakash & Vikas Sangwan & Kewal Singh, 2021. "Transformational Approach to Analytical Value-at-Risk for near Normal Distributions," JRFM, MDPI, vol. 14(2), pages 1-19, January.

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