On portfolio optimization: How and when do we benefit from high-frequency data?

Journal of Applied Econometrics

Volumn 24, Issue 4, 2009, Pages 560-582

  Liu, Qianqiu ^a  

^a UNIVERSITY OF HAWAII   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 67651117949     PISSN: 08837252     EISSN: 10991255     Source Type: Journal    
DOI: 10.1002/jae.1062     Document Type: Article

References (42)

1. Ait-Sahalia Y, Mykland P, Zhang L. 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 TG, Bollerslev T, Lange S. 1999. Forecasting financial market volatility: sample frequency vis-ávis forecast horizon. Journal of Empirical Finance 6: 457-477.
3. Andersen TG, Bollerslev T, Diebold FX, Ebens H. 2001a. The distribution of stock return volatility. Journal of Financial Economics 61: 43-76.
4. Andersen TG, Bollerslev T, Diebold FX, Labys P. 2001b. The distribution of exchange rate volatility. Journal of the American Statistical Association 96: 42-55.
5. Andersen TG, Bollerslev T, Diebold FX, Labys P. 2003. Modeling and forecasting realized volatility. Econometrica 71: 579-625.
6. Andreou E, Ghysels E. 2002. Rolling-sample volatility estimators: some new theoretical, simulation and empirical results. Journal of Business and Economic Statistics 20: 363-376.
7. Areal N, Taylor SJ. 2002. The realized volatility of FTSE-100 futures prices. Journal of Futures Markets 22: 627-648.
8. Bai X, Russell JR, Tiao GC. 2004. Effects of non-normality and dependence on the precision of variance estimates using high-frequency financial data. Working paper, University of Chicago.
9. Bandi FM, Russell JR. 2006. Separating microstructure noise from volatility. Journal of Financial Economics 79: 655-692.
10. Bandi FM, Russell JR. 2007. Volatility. In Handbook of Financial Engineering, Birge JR, Linetski V (eds). Elsevier: Amsterdam; 183-222.
11. Bandi FM, Russell JR. 2008. Microstructure noise, realized variance, and optimal sampling. Review of Economic Studies 75: 339-369.
12. Bandi FM, Russell JR, Zhu Y. 2008. Using high-frequency data in dynamic portfolio choice. Econometric Reviews 27: 163-198.
13. Barndorff-Nielsen OE, Shephard N. 2002. Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society, Series B 64: 253-280.
14. Barndorff-Nielsen OE., Shephard N. 2004. Econometric analysis of realized covariation: high frequency covariance, regression and correlation in financial economics. Econometrica 72: 885-925.
15. Barndorff-Nielsen OE, Shephard N. 2007. Variation, jumps, market frictions and high-frequency data in financial econometrics. In Advances in Economics and Econometrics: Theory and Applications, Blundell R, Torsten P, Newey WK (eds). Ninth World Congress, Econometric Society Monographs, Cambridge University Press: Cambridge, UK; 328-372.
16. Basak GK, Jagannathan R, Ma T. 2004. Assessing the risk in sample minimum risk portfolios. Working paper. Northwestern University: Evanston, IL.
17. Chan LKC, Karceski J, Lakonishok J. 1999. On portfolio optimization: forecasting covariances and choosing the risk model. Review of Financial Studies 12: 937-974.
18. Cohen KJ, Hawawini GA, Maier SF, Schwartz RA, Whitcomb DK. 1983. Friction in the trading process and the estimation of systematic risk. Journal of Financial Economics 12: 263-278.
19. De Pooter M, Martens M, Van Dijk D. 2008. Predicting the daily covariance matrix of S&P 100 stocks using intraday data-but which frequency to use? Econometric Reviews 27: 199-229.
20. Ebens H. 2000. On modeling and forecasting stock volatility. PhD dissertation, Johns Hopkins University. Engle RF. 2000. The econometrics of ultra-high frequency data. Econometrica 68: 1-22.
21. Fleming J, Kirby C, Ostdiek B. 2001. The economic value of volatility timing. Journal of Finance 56: 329-352.
22. Fleming J, Kirby C, Ostdiek B. 2003. The economic value of volatility timing using 'realized' volatility. Journal of Financial Economics 67: 473-509.
23. Foster DP, Nelson DB. 1996. Continuous record asymptotics for rolling sample variance estimators. Econometrica 64: 139-174.
24. French KR, Schwert GW, Stambaugh RF. 1987. Expected stock returns and volatility. Journal of Financial Economics 19: 3-29.
25. Hansen PR, Lunde A. 2006. Realized variance and market microstructure noise (with discussion). Journal of Business and Economic Statistics 24: 127-161.
26. Hayashi T, Yoshida N. 2005. On covariance estimation of non-synchronously observed diffusion processes. Bernoulli 11: 359-379.
27. Hayashi T, Yoshida N. 2006. Estimating correlations with nonsynchronous observations in continuous diffusion models. Working paper. Keio University: Tokyo, Japan.
28. Jagannathan R, Ma T. 2003. Risk reduction in large portfolios: a role for portfolio weight constraints. Journal of Finance 58: 1651-1683.
29. Ledoit O, Wolf M. 2003. Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance 10: 603-621.
30. Lo A, MacKinlay AC. 1990. An econometric analysis of nonsynchronous-trading. Journal of Econometrics 45: 181-212.
31. Martens M. 2002. Measuring and forecasting S&P 500 index-futures volatility using high-frequency data. Journal of Futures Markets 22: 497-518.
32. Martens M, Chang YC, Taylor SJ. 2002. Intraday volatility forecasts using different seasonality adjustment methods. Journal of Financial Research 25: 283-299.
33. McAleer M, Medeiros M. 2008. Realized volatility: a review. Econometric Reviews 27: 10-45.
34. Merton RC. 1980. On estimating the expected return on the market: an exploratory investigation. Journal of Financial Economics 8: 323-361.
35. Nelson DB. 1992. Filtering and forecasting with misspecified ARCH models I: Getting the right variance with the wrong model. Journal of Econometrics 52: 61-90.
36. Newey WK, West KD. 1987. A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55: 703-708.
37. Roll R. 1984. A simple implicit measure of the effective bid-ask spread in an efficient market. Journal of Finance 39: 1127-1140.
38. Scholes M, Williams J. 1977. Estimating Betas from nonsynchronous data. Journal of Financial Economics 5: 309-327.
39. Sheppard K. 2006. Realized covariance and scrambling. Working paper, University of Oxford.
40. West KD, Edison HJ, Cho D. 1993. A utility-based comparison of some models of exchange rate volatility. Journal of International Economics 35: 23-45.
41. Zhang L. 2006. Estimating covariation: Epps effect and microstructure noise. Working paper, University of illinois at Chicago.
42. Zhang L, Mykland P, Ait-Sahalia Y. 2005. A tale of two time scales: determining integrated volatility with noisy high-frequency data. Journal of the American Statistical Association 100: 1394-1411.