The dissertation has presented the usefulness of intraday-based RV approach for estimating, evaluating, and investigating the one-period futures hedging problem. Firstly, Chapter 2 starts discussing the incremental value of a futures hedge using the RV approach. To do so, a new class of discrete-time multivariate volatility models encompassing the elements of realized covariance matrix for estimating the risk-minimizing hedge ratio is proposed. Then, the per-formance of the RV-based models is compared with those generated by return-based models under an out-of-sample context with daily rebalancing approach. The empirical results have indicated that the improvement of switching from daily to intraday can be substantial, based on an extensive set of statistical and economical performance measures.
Next, Chapter 3 has turn to the issue of performance evaluation on futures hedging. By ap-plying of the RV approach, it has presented alternative ex-post measures for assessing the forecasting ability of any ex-ante hedge ratio estimates. With some assumptions in the under-lying price processes, the realized (risk-minimizing) hedge ratio is shown to be consistent with the integrated (risk-minimizing) hedge ratio, and it can be estimated consistently using the realized regression of Barndorff-Nielsen and Shephard (2004). Meanwhile, the asymptotic distribution further provides insights into the precision of the hedge ratio. On the other hand, the realized hedging effectiveness offers an alternative ex-post estimate for the integrated hedging effectiveness. With these measures, hedgers may evaluate the performance of their hedging methods via some designed loss functions without only through the hedging effec-tiveness of Ederington (1979).
At last, but not the last, Chapter 4 has assessed the dynamics of the realized daily hedge ra-tio. A two-regime SETAR model is applied to detect the regime-switching feather of the hedge ratio. Empirical evidences on the two equity markets have shown that the threshold ef-fect does exist, and the ratio is likely to be positively autocorrelated and tends to be more
volatile in the low regime than in the high regime. The empirical finding then supports the re-gime-switching dynamic hedge; see, for example, Lee et al. (2006).
The availability of intraday high-frequency data for many financial assets has motivated the literature to develop methods for measuring, modeling, and forecasting daily volatility. Be-sides the well-known RV estimator, the realized range estimator has also been considered in the literature for this purpose. Building on the high-low range (RR) estimator of Parkinson (1980), Martens and Van Dijk (2007) and Christensen and Podolskij (2007) have developed alternative intraday-based realized range estimator for estimating daily volatility, as follows:
( ) 2 By means of sum of squared intraday price ranges, it has been shown that this RR estimator could provide more efficient estimate of daily volatility than the RV that only utilizes intraday price returns. Extending to the multivariate case, the estimate of covariance between assets using intraday data has been suggested by Bannouh et al. (2009), who combines the idea of Brandt and Diebold (2006) as well as the intraday high-low ranges to have the estimate. This so-called realized co-range estimator is formulated as follows:
( ) ( ) 2 ( ) 2 ( ) dy-namic futures hedge. For example, based on the empirical study of volatility-timing strategy using the realized (co-)ranges, Bannouh et al. (2009) have shown that the covariance
predic-tion using realized (co-)ranges outperforms the realized (co-)variances by about 60 basis points per annum after the transaction cost is taken into accounted. While utilizing realized (co-)variances for predicting the relevant (co-)variances in Chapter 2, the performance of a futures hedge that utilizes realized (co-)ranges is of much interest for further studies. The flexible CCC-GARCH error structure of Kroner and Sultan (1993) provides more flexible and easily ways to estimate the daily (co-)variances as compared with the rolling estimators of Fleming et al. (2003), Bandi et al. (2008), and De Pooter et al. (2008). Besides, the realized (co-)range estimators should provide alternative methods to evaluate the performance of a fu-tures hedge. It is not clear whether the evaluation results using realized (co-)ranges is consis-tent with the results that use realized (co-)variances as shown in Chapter 3.
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CURRICULUM VITAE
1. Yu-Sheng Lai and Her-Jiun Sheu, 2008, “An Application of Realized Regression to the Futures Hedging Problem”, Asia Pacific Management Review, Vol. 13, No. 4, 655-666.
2. Yu-Sheng Lai and Her-Jiun Sheu, “The Incremental Value of a Futures Hedge Using Realized Volatility”, Journal of Futures Markets, forthcoming. Finance & Banking Conference, Shangri-La Hotel, Sydney, Australia.
2. Yu-Sheng Lai and Her-Jiun Sheu, “Performance Evaluation of a Futures Hedge: A Generalized Approach”, Jun. 20-21, 2008, 2008 Annual Conference of Taiwan Finance Association, National Dong Hwa University, Hualien, Taiwan.
3. Yu-Sheng Lai and Her-Jiun Sheu, “Re-Evaluating Futures Hedge under Time-Varying Risks”, Dec. 16-18, 2008, 21st Australasian Finance & Banking Conference, Shangri-La Hotel, Sydney, Australia.
4. Yu-Sheng Lai and Her-Jiun Sheu, “Regime-Dependent Dynamics of the Optimal Futures Hedge:
Evidence from the S&P 500 and the NASDAQ 100 Indices”, Jul. 6-8, 2009, International Symposium on Finance and Accounting 2009, Parkroyal Hotel, Kuala Lumpur, Malaysia.