1. Hansen, P. and J. Lund (2005). A FORECAST COMPARISON OF VOLATILITY MODELS: DOES ANYTHING BEAT A GARCH(1,1)?, Journal of Applied Econometrics, 20, 873-889.

2. Blair, B.j., S.H. Poon, S.J. Taylor (2001). Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns, Journal of Econometrics, 105, 5-26.

3. Hyung, N, S.J. Poon, S.J. and C. Granger (2006) A Source of Long Memory in Volatility, Working Paper, Department of Economics, University of Seoul.

4. Kuester, K, S. Mittnik, and M.S. Paolella (2006). Value-at-Risk Prediction: A Comparison of Alternative Strategies, Journal of Financial Econometrics, 4(1), 53-89. 

Stochastic Volatility

High Frequency

1. Ait-Sahalia, Y, P. Mykland, and L. Zhang (2005). How Often to Sample a Continuous-time Process in the Presence of Market Microstructure Noise. Review of Financial Studies 18, 351-416.

2. Andersen, T.G., T. Bollerslev, and F.X. Diebold (2007). Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility, NBER WP 11775, forthcoming in Review of Economics and Statistics.

3. Andersen, T.G., T. Bollerslev, F.X Diebold, and C. Vega (2003). Micro Effects of Macro Announcements: Real-time Price Discovery in Foreign Exchange. American Economic Review 93, 38-62.

4. Koopman, S. J., B. Jungbacker, and E. Hol. (2005). ��Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realized and Implied Volatility Measurements.�� Journal of Empirical Finance 12:445�475.

5. Hwang, and G. Tauchen (2005). The Relative Contribution of Jumps to Total Price Variation. Journal of Financial Econometrics, 3(4), 456-499.

6. Barndorff-Neilsen, O. And N. Shephard (2006). The Econometrics of Testing for Jumps in Financial Economics Using Bi-Power Variation, Journal of Financial Econometrics, 4, 1-30.

Continuous Time Models