scholarly journals Carry Cost Rate Regimes and Futures Hedge Ratio Variation

2019 ◽  
Vol 12 (2) ◽  
pp. 78 ◽  
Author(s):  
Dean Leistikow ◽  
Ren-Raw Chen
Keyword(s):  
Hedge Ratio ◽  
Bias Adjustment ◽  
Cost Rate ◽  
Ratio Variation ◽  
Hedge Ratios ◽  
Ex Ante ◽  
Hedging Performance

This paper tests whether the traditional futures hedge ratio (hT) and the carry cost rate futures hedge ratio (hc) vary in accordance with the Sercu and Wu (2000) and Leistikow et al. (2019) “hc” theory. It does so, both within and across high and low spot asset carry cost rate (c) regimes. The high and low c regimes are specified by asset across time and across currency denominations. The findings are consistent with the theory. Within and across c regimes, hT is inefficient and hc is biased. Across c regimes, hc’s Bias Adjustment Multiplier (BAM) does not vary significantly. Even though hc’s bias-adjusted variant’s BAM is restricted to old data that is from a different c regime, the hedging performance of hc and its bias-adjusted variant (=hc × BAM), are superior to that for hT. Variation in c may account for the hT variation noted in the literature and variation in c should be incorporated into ex ante hedge ratios.

scholarly journals Dynamic Conditional Bias-Adjusted Carry Cost Rate Futures Hedge Ratios

2022 ◽  
Vol 15 (1) ◽  
pp. 12
Author(s):  
Dean Leistikow ◽  
Yi Tang ◽  
Wei Zhang
Keyword(s):  
The Other ◽  
Conditional Correlation ◽  
Hedge Ratio ◽  
Cost Rate ◽  
Dynamic Conditional Correlation ◽  
Hedging Effectiveness ◽  
Hedge Ratios ◽  
Rolling Window ◽  
Futures Price ◽  
Asset Classes

This paper proposes new dynamic conditional futures hedge ratios and compares their hedging performances along with those of common benchmark hedge ratios across three broad asset classes. Three of the hedge ratios are based on the upward-biased carry cost rate hedge ratio, where each is augmented in a different bias-mitigating way. The carry cost rate hedge ratio augmented with the dynamic conditional correlation between spot and futures price changes generally: (1) provides the highest hedging effectiveness and (2) has a statistically significantly higher hedging effectiveness than the other hedge ratios across assets, sub-periods, and rolling window sizes.

Examining the Hedging Effectiveness of Futures Contracts over Pre and Post Financial Crisis Period: Evidence from National Stock Exchange of India

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Kapil Gupta ◽  
Mandeep Kaur
Keyword(s):  
Financial Crisis ◽  
Stock Exchange ◽  
Futures Contracts ◽  
Price Risk ◽  
Hedge Ratio ◽  
Crisis Period ◽  
Hedging Effectiveness ◽  
Hedge Ratios ◽  
Time Invariant

Present study examines the efficiency of futures contracts in hedging unwanted price risk over highly volatile period i.e. June 2000 - December 2007 and January 2008 – June 2014, pre and post-financial crisis period, by using S&PC NXNIFTY, CNXIT and BANKNIFTY for near month futures contracts. The hedge ratios have been estimated by using five methods namely Ederingtons Model, ARMA-OLS, GARCH (p,q), EGARCH (p,q) and TGARCH (p,q). The study finds that hedging effectiveness increased during post crisis period for S&PC NXNIFTY and BANKNIFTY. However, for CNXIT hedging effectiveness was better during pre-crisis period than post crisis. The study also finds that time-invariant hedge ratio is more efficient than time-variant hedge ratio.

Risk Management with KOSDAQ50 Index Futures Markets

2003 ◽  
Vol 11 (2) ◽  
pp. 51-79
Author(s):  
Gyu Hyeon Mun ◽  
Jeong Hyo Hong
Keyword(s):  
Garch Model ◽  
Minimum Variance ◽  
Price Risk ◽  
Risk Minimization ◽  
Index Futures ◽  
Hedging Strategies ◽  
Hedging Effectiveness ◽  
Hedge Ratios ◽  
Sample Data ◽  
Hedging Performance

This paper studies hedging strategies that use the KOSDAQ50 index futures to hedge the price risk of the KOSDAQ50 index spot portfolio. This study uses the minimum variance hedge model and bivariate ECT-GARCH (1,1) model as hedging models, and analyzes their hedging performances. The sample period covers from January 31, 2001 to December 31, 2002. The most important findings may be summarized as follows. First, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios of the risk-minimization tend to be higher than those of GARCH model. Second, for the in-sample data, hedging effectiveness of GARCH model is higher than that of the risk-minimization, while for the out-of-sample data, hedging effectiveness of the risk-minimization with constant hedge ratios is not far behind the GARCH model in its hedging performance. Third, the hedging performance of KOSDAQ50 index futures is lower than that of KOSPI200 index futures, but higher than that of KTB futures. In conclusion, in the KOSDAQ50 index market, investors are encouraged to use the simple risk-minimization model to hedge the price risk of KOSDAQ50 spot portfolios.

scholarly journals ESTIMATING THE HEDGE RATIOS

2016 ◽  
Vol 4 ◽  
pp. 229-234
Author(s):  
Mária Bohdalová ◽  
Michal Greguš
Keyword(s):  
Quantile Regression ◽  
Likelihood Estimation ◽  
Equal Weight ◽  
Minimal Risk ◽  
Hedge Ratio ◽  
Portfolio Returns ◽  
Hedge Ratios ◽  
Hedging Portfolio ◽  
T Copula ◽  
T Distribution

This paper examines the problem of hedging portfolio returns. Many practitioners and academicians endeavor to solve the problem of how to calculate the optimal hedge ratio accurately. In this paper we compare estimates of the hedge ratio from a classical approach of a linear quantile regression, based on selected quantiles as medians, with that of a non-linear quantile regression. To estimate the hedge ratios, we have used a calibrated Student t distribution for the marginal densities and a Student t copula of the portfolio returns using a maximum likelihood estimation. We created two portfolios of the assets, one for equal weight and another for optimal weight in respect of minimal risk. Our findings show that an assumption of Student t marginal leads to a better estimation of the hedge ratio.

scholarly journals Forecasting Daily Dynamic Hedge Ratios of Indian Index Future Contracts

2019 ◽  
Vol 8 (3) ◽  
pp. 485-493
Keyword(s):  
Performance Measures ◽  
Minimum Mean Square Error ◽  
Multivariate Garch ◽  
Sample Analysis ◽  
Hedge Ratio ◽  
Hedge Ratios ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Egarch Model ◽  
T Distribution

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.

scholarly journals Análise da Efetividade de Políticas de Hedge no Mercado de Dólar Futuro no Brasil

2011 ◽  
Vol 9 (3) ◽  
pp. 365
Author(s):  
Marcelo Cabus Klotzle ◽  
Antonio Carlos Figueiredo Pinto ◽  
Mario Domingues Simões ◽  
Leonardo Lima Gomes
Keyword(s):  
Least Squares ◽  
Ordinary Least Squares ◽  
Fast Growth ◽  
Future Market ◽  
Conditional Heteroscedasticity ◽  
Hedge Ratio ◽  
Hedge Ratios ◽  
Dollar Price ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Naive Approach

In recent years, one could observe a very definite surge in dollar prices in Brazil. Many Brazilian Companies, especially those with large amounts of dollar denominated debt incurred substantial losses due to the strong and fast growth of the dollar. The subsequent dollar price collapse from 2002 to 2008 caused great losses to exporters. In the context of hedge being a form of protection against currency oscillations, this paper aimed to study its effectiveness using the dollar future market in the BM&FBovespa. Specifically, four alternatives for calculating the optimum hedge ratio were compared: a) the so called naïve approach, where opposite positions are taken in the spot and future markets; b) OLS – Ordinary Least Squares c) symmetric bi-variate GARCH (Generalized Autoregressive Conditional Heteroscedasticity); d) asymmetric bi-variate GARCH. The results showed that both GARCH supported hedge ratios presented higher effectiveness when compared to OLS, with in turn surpassed the naïve one.

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.

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