Volatility forecasting using threshold heteroskedastic models of the intra-day range
An effective approach for forecasting return volatility via threshold nonlinear heteroskedastic models of the daily asset price range is provided. The range is defined as the difference between the highest and lowest log intra-day asset price. A general model specification is proposed, allowing the intra-day high-low price range to depend nonlinearly on past information, or an exogenous variable such as US market information. The model captures aspects such as sign or size asymmetry and heteroskedasticity, which are commonly observed in financial markets. The focus is on parameter estimation, inference and volatility forecasting in a Bayesian framework. An MCMC sampling scheme is employed for estimation and shown to work well in simulation experiments. Finally, competing range-based and return-based heteroskedastic models are compared via out-of-sample forecast performance. Applied to six international financial market indices, the range-based threshold heteroskedastic models are well supported by the data in terms of finding significant threshold nonlinearity, diagnostic checking and volatility forecast performance under various volatility proxies.
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- Charles Corrado & Cameron Truong, 2004. "Forecasting Stock Index Volatility: The Incremental Information in the Intraday High-Low Price Range," Research Paper Series 127, Quantitative Finance Research Centre, University of Technology, Sydney.
- Chen, Cathy W.S. & So, Mike K.P., 2006. "On a threshold heteroscedastic model," International Journal of Forecasting, Elsevier, vol. 22(1), pages 73-89.
- Broto, Carmen & Ruiz, Esther, 2006.
"Unobserved component models with asymmetric conditional variances,"
Computational Statistics & Data Analysis,
Elsevier, vol. 50(9), pages 2146-2166, May.
- Carmen Broto & Esther Ruiz, 2003. "Unobserved Component Models With Asymmetric Conditional Variances," Statistics and Econometrics Working Papers ws032003, Universidad Carlos III, Departamento de Estadística y Econometría.
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993.
"On the relation between the expected value and the volatility of the nominal excess return on stocks,"
157, Federal Reserve Bank of Minneapolis.
- Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
- Sentana,E., 1995.
"Quadratic Arch Models,"
9517, Centro de Estudios Monetarios Y Financieros-.
- Chen, Cathy W. S. & Chiang, Thomas C. & So, Mike K. P., 2003. "Asymmetrical reaction to US stock-return news: evidence from major stock markets based on a double-threshold model," Journal of Economics and Business, Elsevier, vol. 55(5-6), pages 487-502.
- Mandelbrot, Benoit B, 1971. "When Can Price Be Arbitraged Efficiently? A Limit to the Validity of the Random Walk and Martingale Models," The Review of Economics and Statistics, MIT Press, vol. 53(3), pages 225-36, August.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- De Luca, Giovanni & Zuccolotto, Paola, 2006. "Regime-switching Pareto distributions for ACD models," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2179-2191, December.
- Chou, Ray Yeutien, 2005. "Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 561-82, June.
- Francis X. Diebold & Robert S. Mariano, 1994.
"Comparing Predictive Accuracy,"
NBER Technical Working Papers
0169, National Bureau of Economic Research, Inc.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
- Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
- Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
- Chen, Cathy W.S. & Gerlach, Richard & So, Mike K.P., 2006. "Comparison of nonnested asymmetric heteroskedastic models," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2164-2178, December.
- Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 1999. "Range-Based Estimation of Stochastic Volatility Models or Exchange Rate Dynamics are More Interesting Than You Think," Center for Financial Institutions Working Papers 00-28, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Gray, Stephen F., 1996.
"Modeling the conditional distribution of interest rates as a regime-switching process,"
Journal of Financial Economics,
Elsevier, vol. 42(1), pages 27-62, September.
- Tom Doan, . "RATS programs to replicate Gray's 1996 Regime Switching GARCH paper," Statistical Software Components RTZ00080, Boston College Department of Economics.
- Nicholas G. Polson & George C. Tiao (ed.), 1995. "Bayesian Inference," Books, Edward Elgar, volume 0, number 602.
- Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
- Brandt, Michael W. & Jones, Christopher S., 2006. "Volatility Forecasting With Range-Based EGARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 470-486, October.
- 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.
- Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
- Leeves, Gareth, 2007. "Asymmetric volatility of stock returns during the Asian crisis: Evidence from Indonesia," International Review of Economics & Finance, Elsevier, vol. 16(2), pages 272-286.
- Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
- Bali, Turan G., 2000. "Testing the Empirical Performance of Stochastic Volatility Models of the Short-Term Interest Rate," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(02), pages 191-215, June.
- Beckers, Stan, 1983. "Variances of Security Price Returns Based on High, Low, and Closing Prices," The Journal of Business, University of Chicago Press, vol. 56(1), pages 97-112, January.
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