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Volatility Prediction: A Study with Structural Breaks
Indian Institute of Management Kashipur, Kashipur, India
- 1 Indian Institute of Management Kashipur, Kashipur, India
Theoretical Economics Letters·Volume 08 (2018)·Pages 1218–1231·Published 20 April 2018·DOI10.4236/tel.2018.86080
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Abstract
We incorporate the impact of structural breaks in the unbiased unconditional volatility as proposed by Kumar and Maheswaran with a conditional autoregressive range (CARR) model. The findings of the proposed framework are compared with the findings based on the volatility forecasts of the GARCH model with and without structural breaks in volatility. Our findings based on the analysis on S&P 500, FTSE 100, SZSE Composite and FBMKLCI indices indicate that the proposed framework effectively captures the dynamics of conditional volatility and provides better out-of-sample forecasts relative to GARCH models with and without structural breaks in volatility.
KeywordsCARR ModelAddRS EstimatorVolatility Forecast EvaluationGARCH Family of Models
- Kumar, D. and Maheswaran, S. (2014) A Reflection Principle for a Random Walk with Implications for Volatility Estimation Using Extreme Values of Asset Prices. Economic Modelling, 38, 33-44. https://doi.org/10.1016/j.econmod.2013.11.045
- Alizadeh, S., Brandt, M.W. and Diebold, F.X. (2002) Range-Based Estimation of Stochastic Volatility Models. The Journal of Finance, 57, 1047-1091. https://doi.org/10.1111/1540-6261.00454
- Parkinson, M. (1980) The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53, 61-65. https://doi.org/10.1086/296071
- Garman, M.B. and Klass, M.J. (1980) On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53, 67-78. https://doi.org/10.1086/296072
- Rogers, L.C.G. and Satchell, S.E. (1991) Estimating Variance from High, Low and Closing Prices. The Annals of Applied Probability, 1, 504-512. https://doi.org/10.1214/aoap/1177005835
- Yang, D. and Zhang, Q. (2000) Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices. The Journal of Business, 73, 477-492. https://doi.org/10.1086/209650
- Kumar, D. and Maheswaran, S. (2012) Modelling Asymmetry and Persistence under the Impact of Sudden Changes in the Volatility of the Indian Stock Market. IIMB Management Review, 24, 123-136. https://doi.org/10.1016/j.iimb.2012.04.006
- Babikir, A., Gupta, R., Mwabutwa, C. and Owusu-Sekyere, E. (2012) Structural Breaks and GARCH Models of Stock Return Volatility: The Case of South Africa. Economic Modelling, 29, 2435-2443. https://doi.org/10.1016/j.econmod.2012.06.038
- Rapach, D.E. and Strauss, J.K. (2008) Structural Breaks and GARCH Models of Exchange Rate Volatility. Journal of Applied Econometrics, 23, 65-90. https://doi.org/10.1002/jae.976
- Salisu, A.A. and Fasanya, I.O. (2013) Modelling Oil Price Volatility with Structural Breaks. Energy Policy, 52, 554-562. https://doi.org/10.1016/j.enpol.2012.10.003
- Ross, S.A. (1989) Information and Volatility: The No-Arbitrage Martingale Approach to Timing and Resolution Irrelevancy. The Journal of Finance, 44, 1-17. https://doi.org/10.1111/j.1540-6261.1989.tb02401.x
- Kumar, D. and Maheswaran, S. (2013) Evidence of Long Memory in the Indian Stock Market. Asia-Pacific Journal of Management Research and Innovation, 9, 9-21. https://doi.org/10.1177/2319510X13483504