Measuring Hedging Effectiveness of Index Futures Contracts: Do Dynamic Models Outperform Static Models? A Regime‐Switching Approach

Salvador, Enrique; Aragó, Vicent

doi:10.1002/fut.21598

Cited by 19 publications

(10 citation statements)

References 50 publications

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“…Salvador and Arago estimated optimal hedge ratio using linear and non-linear GARCH models. Conclusion derived is that non-linear GARCH models provide better hedge ratios for in and out sample data [16]. Choudhary and Zhang forecasted hedge ratios using GARCH, BEKK GARCH, GARCH-X, BEKK-X, Q-GARCH, and GJR-GARCH.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Let and are the returns of spot and future portfolio at any time t based on the information up to time t-1. According to Johnson (1960), Baillie, and Myers (1991) ( [2], [19]), the minimum variance hedge ratio β ( [6][7][8], [11], [16]) is given by (1) Where numerator in (1) is the covariance between spot and future portfolio. Denominator is the variance of future portfolio.…”

Section: A Hedge Ratiomentioning

confidence: 99%

“…After that two series r +,t = r s,t + r f,t and r -,t =r s,t -r f,t are created and GARCH (1,1) is used to find the conditional variance ( of these two series similar to model (6) and (7). Then the covariance between spot and future return [21] can be calculated as (8) The minimum variance hedge ratio ( [6][7][8], [11], [16], [21]) is then obtained as 9The measure of hedging effectiveness is then given by (4)…”

Section: Model 1: Sgarch (Simplified Multivariate Generalised Auto Rementioning

confidence: 99%

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Forecasting Daily Dynamic Hedge Ratios of Indian Index Future Contracts

Gupta¹,

Jha²,

Srivastava³

2019

IJRTE

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Forecasting plays a crucial role in determining the direction of future trends and in making necessary investment decisions. This research presents the forecasting performance of three multivariate GARCH models: SGARCH, EGARCH, and GJR-GARCH based on Gaussian and Student’s t-distribution. The forecasting ability of the models is evaluated on the basis of forecasting performance measures: MAE, SSE, MSE, and RMSE. This is done by examining the hedged portfolios of three indices of NSE: NIFTY50, BANKNIFTY, and NIFTYIT. Daily data from Jan 2006 to Dec 2017 is taken and forecasts are conducted using out of sample data from Jan 2016-Dec 2017. Minimum mean square error (MMSE) forecasting method is used to generate conditional variance and covariance forecasts which in turn generate hedge ratios and corresponding hedged portfolio. Minimum variance hedge ratio framework of Ederington (1979) is used for hedging. The in-sample analysis shows that SGARCH with both the distribution performed better than the other models while out-of-sample analysis provides mixed results. EGARCH model assigns the lowest hedge ratio to NIFTY50 and BANKNIFTY while SGARCH model assigns the lowest hedge ratio to NIFTYIT. Forecasting performance measures show the least value for SGARCH and EGARCH model. In future these models are able to reduce maximum risk from the spot market. The results of this research has important implications for financial decision and policy makers.

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Section: Literature Reviewmentioning

confidence: 99%

Section: A Hedge Ratiomentioning

confidence: 99%

Section: Model 1: Sgarch (Simplified Multivariate Generalised Auto Rementioning

confidence: 99%

See 1 more Smart Citation

Forecasting Daily Dynamic Hedge Ratios of Indian Index Future Contracts

Gupta¹,

Jha²,

Srivastava³

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…Therefore, it seems that there exists a trade‐off between the construction of flexible models and the hedging effectiveness of such models. Salvador and Arogó () show that a regime‐switching bivariate GARCH model outperforms static models in both an in‐sample and an out‐of‐sample analysis. In this study, we propose a new method of managing the high degree of volatility of the time‐varying optimal hedge ratio.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Hedging Effectiveness of Time‐Varying Hedge Ratio: Nonparametric Approaches

Fan

Park

2015

Journal of Futures Markets

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Many studies have estimated the optimal time-varying hedge ratio using futures, with most employing a bivariate generalized autoregressive conditional heteroscedasticity (BGARCH) model or a random coefficient model to estimate the time-varying hedge ratio. However, it has been argued that when the variability of the estimated time-varying hedge ratio is large, this ratio's hedging performance is not as good as that of the unconditional (constant) hedge ratio. This study proposes a nonparametric estimation approach to estimate and evaluate the optimal conditional hedge ratio. This method produces a time-varying hedge ratio with less volatility than those obtained from the BGARCH and random coefficient models. We evaluate the hedging performance of the various models using soybean oil, corn, S&P 500, and Hang Seng futures indices. The empirical results support the proposed nonparametric approach in terms of both in-sample and out-of-sample performance.

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“…They find that the CSI 300 stock index futures can be an effective hedging tool and the question whether time-varying ratios outperform constant ratios depends on the length of the hedging horizon. Salvador and Aragó (2014) use nonlinear GARCH models to estimate optimal hedge ratios with futures contracts and obtain more efficient hedge ratios and superior hedging performance compared to the other methodologies.…”

Section: Introductionmentioning

confidence: 99%

Dependence Structure and Hedging of U.S. Spot and Futures Markets in Financial Crisis

Chai

2015

AFR

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The main objective of this study is to measure appropriately the dependence structure and optimal hedge ratio of U.S. spot and futures markets in financial crisis. In much empirical literature it has been demonstrated that linear Pearson correlation is not an appropriate dependence measure for non-normal distributions. This inadequacy of correlation requires an appropriate dependence measure: the copula. Copula modeling has become an increasingly popular tool in finance to model assets returns dependency as it can overcome the limitations of correlation when extreme losses occurred. The contribution of this paper is in two aspects. First, an appropriate copula function is discovered to capture the dependence structure of S&P 500 spot and futures in financial crisis adequately. Second, Gumbel copula function is exploited, with threshold GARCH model as marginals, to construct a Gumbel copula-threshold-GARCH model to estimate the optimal hedge ratio, simultaneously capturing asymmetric nonlinear behaviour in univariate returns of spot and futures markets and bivariate dependency.

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Measuring Hedging Effectiveness of Index Futures Contracts: Do Dynamic Models Outperform Static Models? A Regime‐Switching Approach

Cited by 19 publications

References 50 publications

Forecasting Daily Dynamic Hedge Ratios of Indian Index Future Contracts

Forecasting Daily Dynamic Hedge Ratios of Indian Index Future Contracts

Estimation and Hedging Effectiveness of Time‐Varying Hedge Ratio: Nonparametric Approaches

Dependence Structure and Hedging of U.S. Spot and Futures Markets in Financial Crisis

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