Minimum variance hedge ratios for stock index futures: Duration and expiration effects

1992 ◽  
Vol 12 (1) ◽  
pp. 33-53 ◽  
Author(s):  
Mary Lindahl
Keyword(s):  
Minimum Variance ◽  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures ◽  
Hedge Ratios

Hedging and Optimal Hedge Ratios for International Index Futures Markets

2009 ◽  
Vol 12 (04) ◽  
pp. 593-610 ◽  
Author(s):  
Cheng-Few Lee ◽  
Kehluh Wang ◽  
Yan Long Chen
Keyword(s):  
Futures Markets ◽  
Minimum Variance ◽  
Stock Index ◽  
Stock Index Futures ◽  
Hedge Ratio ◽  
Index Futures ◽  
Static Hedging ◽  
Hedge Ratios ◽  
Optimal Hedge ◽  
Mean Variance

This empirical study utilizes four static hedging models (OLS Minimum Variance Hedge Ratio, Mean-Variance Hedge Ratio, Sharpe Hedge Ratio, and MEG Hedge Ratio) and one dynamic hedging model (bivariate GARCH Minimum Variance Hedge Ratio) to find the optimal hedge ratios for Taiwan Stock Index Futures, S&P 500 Stock Index Futures, Nikkei 225 Stock Index Futures, Hang Seng Index Futures, Singapore Straits Times Index Futures, and Korean KOSPI 200 Index Futures. The effectiveness of these ratios is also evaluated. The results indicate that the methods of conducting optimal hedging in different markets are not identical. However, the empirical results confirm that stock index futures are effective direct hedging instruments, regardless of hedging schemes or hedging horizons.

scholarly journals Hedge Ratios in South African Stock Index Futures

2010 ◽  
Vol 9 (3) ◽  
pp. 285-304 ◽  
Author(s):  
Stavros Degiannakis ◽  
Christos Floros
Keyword(s):  
South African ◽  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures ◽  
Hedge Ratios

Do higher order moments of return distribution provide better decisions in minimum-variance hedging? Evidence from US stock index futures

2019 ◽  
Vol 45 (2) ◽  
pp. 240-265
Author(s):  
Yang (Greg) Hou ◽  
Mark Holmes
Keyword(s):  
Minimum Variance ◽  
Jel Classification ◽  
Higher Order ◽  
Stock Index ◽  
Stock Index Futures ◽  
Return Distribution ◽  
Higher Order Moments ◽  
Index Futures ◽  
Hedging Strategies ◽  
Skewness And Kurtosis

Using daily S&P 500 spot index and index futures data, this article examines the effects of conditional skewness and kurtosis parameters of a skew-Student density function on dynamic minimum-variance hedging strategies. We find an important role for autoregressive marginal skewness and joint kurtosis in risk management. While static higher order moments improve reductions in variance of hedged portfolios over the case of normality, the inclusion of an autoregressive component significantly extends these improvements. This occurs in both tranquil and tumultuous periods. Furthermore, when transaction costs are considered, taking into account variations of higher order moments retains the best performance. JEL Classification: G11, G13

Hedge ratios in Greek stock index futures market

2004 ◽  
Vol 14 (15) ◽  
pp. 1125-1136 ◽  
Author(s):  
Christos Floros * ◽  
Dimitrios V. Vougas
Keyword(s):  
Futures Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures ◽  
Hedge Ratios

scholarly journals The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in India

2011 ◽  
Vol 6 (1) ◽  
pp. 111-123 ◽  
Author(s):  
Kailash Pradhan
Keyword(s):  
Least Square ◽  
Stock Index ◽  
Stock Index Futures ◽  
Vector Error Correction ◽  
Index Futures ◽  
Portfolio Returns ◽  
Hedging Effectiveness ◽  
Hedge Ratios ◽  
Out Of Sample ◽  
Optimal Hedge

The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in IndiaThis study evaluates optimal hedge ratios and the hedging effectiveness of stock index futures. The optimal hedge ratios are estimated from the ordinary least square (OLS) regression model, the vector autoregression model (VAR), the vector error correction model (VECM) and multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) models such as VAR-GARCH and VEC-GARCH using the S&P CNX Nifty index and its futures index. Hedging effectiveness is measured in terms of within sample and out of sample risk-return trade-off at various forecasting horizons. The analysis found that the VEC-GARCH time varying hedge ratio provides the greatest portfolio risk reduction and generates the highest portfolio returns.

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