Modelling and forecasting unbiased extreme value volatility estimator: A study based on exchange rates with economic significance analysis

Main Article Content

Dilip Kumar

Abstract

This study proposes the use of an alternate approach to generate more accurate forecasts of an unbiased AddRS estimator. The study also devises and implements trading strategies based on the volatility forecasts to highlight its economic significance. The findings indicate that the proposed framework provides more accurate forecasts of daily volatility in comparison to returns based and range based alternative volatility models. The findings based on economic significance analysis indicate that the risk averse investor can earn substantial gain by using the volatility forecasts of the proposed framework than by using the volatility forecasts of the alternative models.

Article Details

Section
Articles

References

Alizadeh, S., Brandt, M. W., & Diebold, F. X. (2002). Range‐based estimation of stochastic volatility models. The Journal of Finance, 57(3), 1047-1091.

Andersen, T. G., & Bollerslev, T. (1997). Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns. The Journal of Finance, 52(3), 975-1005.

Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and Forecasting Realized Volatility. Econometrica, 71(2), 579-625.

Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3-30.

Brandt, M. W., & Jones, C. S. (2006). Volatility Forecasting With Range-Based EGARCH Models. Journal of Business & Economic Statistics, 24(4), 470-486.

Chou, R. Y. (2005). Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model. Journal of Money, Credit and Banking, 37(3), 561-582.

Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174-196.

Ding, Z., Granger, C. W., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of empirical finance, 1(1), 83-106.

Driesprong, G., Jacobsen, B., & Maat, B. (2008). Striking oil: Another puzzle? Journal of Financial Economics, 89(2), 307-327.

Engle, R. F., & Russell, J. R. (1998). Autoregressive conditional duration: a new model for irregularly spaced transaction data. Econometrica, 1127-1162.

Garman, M. B., & Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. The Journal of Business, 53(1), 67-78.

Hansen, P. R. (2005). A Test for Superior Predictive Ability. Journal of Business & Economic Statistics, 23(4), 365-380.

Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453-497.

Kumar, D., & Maheswaran, S. (2014a). Modeling and forecasting the additive bias corrected extreme value volatility estimator. International Review of Financial Analysis, 34, 166-176.

Kumar, D., & Maheswaran, S. (2014b). A reflection principle for a random walk with implications for volatility estimation using extreme values of asset prices. Economic Modelling, 38, 33-44.

Kumar, D., & 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.

Marquering, W., & Verbeek, M. (2004). The economic value of predicting stock index returns and volatility. Journal of Financial and Quantitative Analysis, 39(02), 407-429.

Narayan, P. K., & Sharma, S. S. (2014). Firm return volatility and economic gains: The role of oil prices. Economic Modelling, 38, 142-151.

Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. Journal of Business, 61-65.

Politis, D. N., & Romano, J. P. (1994). The stationary bootstrap. Journal of the American Statistical Association, 89(428), 1303-1313.

Robinson, P. M. (1991). Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression. Journal of Econometrics, 47(1), 67-84.

Rogers, L. C., & Satchell, S. E. (1991). Estimating Variance From High, Low and Closing Prices. The Annals of Applied Probability, 1(4), 504-512.

Rogers, L. C., & Zhou, F. (2008). Estimating correlation from high, low, opening and closing prices. The Annals of Applied Probability, 18(2), 813-823.

Shimotsu, K., & Phillips, P. C. (2005). Exact local Whittle estimation of fractional integration. The Annals of Statistics, 33(4), 1890-1933.

Yang, D., & Zhang, Q. (2000). Drift-independent volatility estimation based on high, low, open, and close prices. The Journal of Business, 73(3), 477-492.