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Intraday portfolio risk management using VaR and CVaR:A CGARCH-EVT-Copula approach

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  • Karmakar, Madhusudan
  • Paul, Samit

Abstract

The study forecast intraday portfolio VaR and CVaR using high frequency data of three pairs of stock price indices taken from three different markets. For each pair we specify both the marginal models for the individual return series and a joint model for the dependence between the paired series. We have used CGARCH-EVT-Copula model, and compared its forecasting performance with three other competing models. Backtesting evidence shows that the CGARCH-EVT-Copula type model performs relatively better than other models. Once the best performing model is identified for each pair, we develop an optimal portfolio selection model for each market, separately.

Suggested Citation

  • Karmakar, Madhusudan & Paul, Samit, 2019. "Intraday portfolio risk management using VaR and CVaR:A CGARCH-EVT-Copula approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 699-709.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:2:p:699-709
    DOI: 10.1016/j.ijforecast.2018.01.010
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    1. Tauchen, George, 2001. "Notes on financial econometrics," Journal of Econometrics, Elsevier, vol. 100(1), pages 57-64, January.
    2. Beatriz Vaz de Melo Mendes, 2005. "Asymmetric extreme interdependence in emerging equity markets," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(6), pages 483-498, November.
    3. Dionne, Georges & Duchesne, Pierre & Pacurar, Maria, 2009. "Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 777-792, December.
    4. Wagner Piazza Gaglianone & Luiz Renato Lima & Oliver Linton & Daniel R. Smith, 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 150-160, January.
    5. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    6. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    7. Deo, Rohit & Hurvich, Clifford & Lu, Yi, 2006. "Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 29-58.
    8. Bollerslev, Tim & Ghysels, Eric, 1996. "Periodic Autoregressive Conditional Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 139-151, April.
    9. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836.
    10. Christoffersen, Peter & Jacobs, Kris & Ornthanalai, Chayawat & Wang, Yintian, 2008. "Option valuation with long-run and short-run volatility components," Journal of Financial Economics, Elsevier, vol. 90(3), pages 272-297, December.
    11. Goodhart, Charles A. E. & O'Hara, Maureen, 1997. "High frequency data in financial markets: Issues and applications," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 73-114, June.
    12. Taylor, Stephen J. & Xu, Xinzhong, 1997. "The incremental volatility information in one million foreign exchange quotations," Journal of Empirical Finance, Elsevier, vol. 4(4), pages 317-340, December.
    13. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    14. Post, Thierry & van Vliet, Pim, 2006. "Downside risk and asset pricing," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 823-849, March.
    15. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    16. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    17. Satheesh Aradhyula & A. Tolga Ergun, 2004. "Trading collar, intraday periodicity and stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 14(13), pages 909-913.
    18. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    19. Martin Martens & Yuan‐Chen Chang & Stephen J. Taylor, 2002. "A Comparison of Seasonal Adjustment Methods When Forecasting Intraday Volatility," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 25(2), pages 283-299, June.
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    3. Wang, Jianzhou & Lv, Mengzheng & Wang, Shuai & Gao, Jialu & Zhao, Yang & Wang, Qiangqiang, 2024. "Can multi-period auto-portfolio systems improve returns? Evidence from Chinese and U.S. stock markets," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    4. Jorge V. Pérez-Rodríguez, 2020. "Another look at the implied and realised volatility relation: a copula-based approach," Risk Management, Palgrave Macmillan, vol. 22(1), pages 38-64, March.
    5. Zhi, Bangdong & Wang, Xiaojun & Xu, Fangming, 2022. "Managing inventory financing in a volatile market: A novel data-driven copula model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    6. Zhou, Xinmiao & Qian, Huanhuan & Pérez-Rodríguez, Jorge. V. & González López-Valcárcel, Beatriz, 2020. "Risk dependence and cointegration between pharmaceutical stock markets: The case of China and the USA," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    7. Jiang, Yifu & Olmo, Jose & Atwi, Majed, 2024. "Dynamic robust portfolio selection under market distress," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).
    8. Harish Kamal & Samit Paul, 2024. "Liquidity‐adjusted value‐at‐risk using extreme value theory and copula approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1747-1769, September.
    9. Wei, Zhengyuan & He, Qingxia & Zhou, Qili & Wang, Ge, 2023. "Measuring dependence structure and extreme risk spillovers in stock markets: An APARCH-EVT-DMC approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    10. Shafique Ur Rehman & Touqeer Ahmad & Wu Dash Desheng & Amirhossein Karamoozian, 2024. "Analyzing selected cryptocurrencies spillover effects on global financial indices: Comparing risk measures using conventional and eGARCH-EVT-Copula approaches," Papers 2407.15766, arXiv.org.
    11. Zhao, Jing & Cui, Luansong & Liu, Weiguo & Zhang, Qiwen, 2023. "Extreme risk spillover effects of international oil prices on the Chinese stock market: A GARCH-EVT-Copula-CoVaR approach," Resources Policy, Elsevier, vol. 86(PB).
    12. Arian, Hamid & Moghimi, Mehrdad & Tabatabaei, Ehsan & Zamani, Shiva, 2022. "Encoded Value-at-Risk: A machine learning approach for portfolio risk measurement," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 500-525.
    13. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.

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