Hedge Ratio (hedge + ratio)

Distribution by Scientific Domains

Kinds of Hedge Ratio

  • optimal hedge ratio
  • time-varying hedge ratio


  • Selected Abstracts


    On a Mean,Generalized Semivariance Approach to Determining the Hedge Ratio

    THE JOURNAL OF FUTURES MARKETS, Issue 6 2001
    Sheng-Syan Chen
    A new mean-risk hedge ratio based on the concept of generalized semivariance (GSV) is proposed. The proposed mean-GSV (M-GSV) hedge ratio is consistent with the GSV-based risk,return model developed by Fishburn (1977), Bawa (1975, 1978), and Harlow and Rao (1989). The M-GSV hedge ratio can also be considered an extension of the GSV-minimizing hedge ratio considered by De Jong, De Roon, and Veld (1997) and Lien and Tse (1998, 2000). The M-GSV hedge ratio is estimated for Standard & Poor's (S&P) 500 futures and compared to six other widely used hedge ratios. Because all the hedge ratios considered are known to converge to the minimum-variance (Johnson) hedge ratio under joint normality and martingale conditions, tests for normality and martingale conditions are carried out. The empirical results indicate that the joint normality and martingale hypotheses do not hold for the S&P 500 futures. The M-GSV hedge ratio varies less than the GSV hedge ratio for low and relevant levels of risk aversion. Furthermore, the M-GSV hedge ratio converges to a value different from the values of the other hedge ratios for higher values of risk aversion. 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21: 581,598, 2001 [source]


    Hedge Ratio Stability and Hedging Effectiveness of Time-Varying Hedge Ratios in Volatile Index Futures Markets: Evidence from the Asian Financial Crisis,

    ASIA-PACIFIC JOURNAL OF FINANCIAL STUDIES, Issue 5 2010
    Janchung Wang
    C10; G13; G15 Abstract Hedge ratio stability is especially important because hedgers are likely to use the estimate of historical hedge ratios to hedge future positions of their portfolios. One main purpose of the present study is to examine hedge ratio stability during the Asian financial crisis and post-crisis, periods characterized by high price volatility, using the Nikkei 225, Hang Seng, and KOSPI 200 index futures contracts. Empirical results from the Hang Seng and the KOSPI 200 futures markets indicate that during the two periods of high price volatility, hedge ratios appeared to be unstable. Additionally, both in-sample and out-of-sample evidences indicate that, for hedging effectiveness, the time-varying hedge ratios clearly outperform the constant hedge ratios for the Hang Seng and the KOSPI 200 index futures, consistent with the findings of hedge ratio instability. The comparison results of different time-varying hedge ratios support the conclusion that the bivariate error correction generalized autoregressive conditional heteroskedastic (1,1) model enhances hedging effectiveness compared to other time-varying hedge ratios. Finally, this study examines the impact of hedge duration on hedging effectiveness and hedge ratios. The empirical results indicate that hedging effectiveness improves with increasing hedge duration. [source]


    Some Recent Developments in Futures Hedging

    JOURNAL OF ECONOMIC SURVEYS, Issue 3 2002
    Donald Lien
    The use of futures contracts as a hedging instrument has been the focus of much research. At the theoretical level, an optimal hedge strategy is traditionally based on the expected,utility maximization paradigm. A simplification of this paradigm leads to the minimum,variance criterion. Although this paradigm is quite well accepted, alternative approaches have been sought. At the empirical level, research on futures hedging has benefited from the recent developments in the econometrics literature. Much research has been done on improving the estimation of the optimal hedge ratio. As more is known about the statistical properties of financial time series, more sophisticated estimation methods are proposed. In this survey we review some recent developments in futures hedging. We delineate the theoretical underpinning of various methods and discuss the econometric implementation of the methods. [source]


    OPTIMAL CONTINUOUS-TIME HEDGING WITH LEPTOKURTIC RETURNS

    MATHEMATICAL FINANCE, Issue 2 2007

    We examine the behavior of optimal mean,variance hedging strategies at high rebalancing frequencies in a model where stock prices follow a discretely sampled exponential Lvy process and one hedges a European call option to maturity. Using elementary methods we show that all the attributes of a discretely rebalanced optimal hedge, i.e., the mean value, the hedge ratio, and the expected squared hedging error, converge pointwise in the state space as the rebalancing interval goes to zero. The limiting formulae represent 1-D and 2-D generalized Fourier transforms, which can be evaluated much faster than backward recursion schemes, with the same degree of accuracy. In the special case of a compound Poisson process we demonstrate that the convergence results hold true if instead of using an infinitely divisible distribution from the outset one models log returns by multinomial approximations thereof. This result represents an important extension of Cox, Ross, and Rubinstein to markets with leptokurtic returns. [source]


    The incremental value of a futures hedge using realized volatility

    THE JOURNAL OF FUTURES MARKETS, Issue 9 2010
    Yu-Sheng Lai
    A number of prior studies have developed a variety of multivariate volatility models to describe the joint distribution of spot and futures, and have applied the results to form the optimal futures hedge. In this study, the authors propose a new class of multivariate volatility models encompassing realized volatility (RV) estimates to estimate the risk-minimizing hedge ratio, and compare the hedging performance of the proposed models with those generated by return-based models. In an out-of-sample context with a daily rebalancing approach, based on an extensive set of statistical and economic performance measures, the empirical results show that improvement can be substantial when switching from daily to intraday. This essentially comes from the advantage that the intraday-based RV potentially can provide more accurate daily covariance matrix estimates than RV utilizing daily prices. Finally, this study also analyzes the effect of hedge horizon on hedge ratio and hedging effectiveness for both the in-sample and the out-of-sample data. 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:874,896, 2010 [source]


    Estimation and hedging effectiveness of time-varying hedge ratio: Flexible bivariate garch approaches

    THE JOURNAL OF FUTURES MARKETS, Issue 1 2010
    Sung Yong Park
    Bollerslev's (1990, Review of Economics and Statistics, 52, 5,59) constant conditional correlation and Engle's (2002, Journal of Business & Economic Statistics, 20, 339,350) dynamic conditional correlation (DCC) bivariate generalized autoregressive conditional heteroskedasticity (BGARCH) models are usually used to estimate time-varying hedge ratios. In this study, we extend the above model to more flexible ones to analyze the behavior of the optimal conditional hedge ratio based on two (BGARCH) models: (i) adopting more flexible bivariate density functions such as a bivariate skewed- t density function; (ii) considering asymmetric individual conditional variance equations; and (iii) incorporating asymmetry in the conditional correlation equation for the DCC-based model. Hedging performance in terms of variance reduction and also value at risk and expected shortfall of the hedged portfolio are also conducted. Using daily data of the spot and futures returns of corn and soybeans we find asymmetric and flexible density specifications help increase the goodness-of-fit of the estimated models, but do not guarantee higher hedging performance. We also find that there is an inverse relationship between the variance of hedge ratios and hedging effectiveness. 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:71,99, 2010 [source]


    Liquidity and hedging effectiveness under futures mispricing: International evidence

    THE JOURNAL OF FUTURES MARKETS, Issue 11 2009
    A. Andani
    We analyze the hedging effectiveness of positions that replicate stock indexes using corresponding futures contracts through the application of a dynamic, stochastic hedging strategy proposed by Lafuente, J. A. and Novales, A. (2003). Conclusive gains do not emerge in any of the markets analyzed over the period considered, relative to the use of a constant unit hedge ratio. These findings are consistent with the trend observed in the IBEX 35 futures market study of Lafuente, J. A. and Novales, A. (2003). Our empirical evidence suggests that, contrary to what happens in less liquid markets, the discrepancy between theoretical and quoted prices in index futures contracts in fully developed markets does not represent a noise factor that can be successfully exploited for hedging. 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:1050,1066, 2009 [source]


    A copula-based regime-switching GARCH model for optimal futures hedging

    THE JOURNAL OF FUTURES MARKETS, Issue 10 2009
    Hsiang-Tai LeeArticle first published online: 27 JUL 200
    The article develops a regime-switching Gumbel,Clayton (RSGC) copula GARCH model for optimal futures hedging. There are three major contributions of RSGC. First, the dependence of spot and futures return series in RSGC is modeled using switching copula instead of assuming bivariate normality. Second, RSGC adopts an independent switching Generalized Autoregressive Conditional Heteroscedasticity (GARCH) process to avoid the path-dependency problem. Third, based on the assumption of independent switching, a formula is derived for calculating the minimum variance hedge ratio. Empirical investigation in agricultural commodity markets reveals that RSGC provides good out-of-sample hedging effectiveness, illustrating importance of modeling regime shift and asymmetric dependence for futures hedging. 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:946,972, 2009 [source]


    Minimum variance cross hedging under mean-reverting spreads, stochastic convenience yields, and jumps: Application to the airline industry

    THE JOURNAL OF FUTURES MARKETS, Issue 8 2009
    Mark Bertus
    Exchange traded futures contracts often are not written on the specific asset that is a source of risk to a firm. The firm may attempt to manage this risk using futures contracts written on a related asset. This cross hedge exposes the firm to a new risk, the spread between the asset underlying the futures contract and the asset that the firm wants to hedge. Using the specific case of the airline industry as motivation, we derive the minimum variance cross hedge assuming a two-factor diffusion model for the underlying asset and a stochastic, mean-reverting spread. The result is a time-varying hedge ratio that can be applied to any hedging horizon. We also consider the effect of jumps in the underlying asset. We use simulations and empirical tests of crude oil, jet fuel cross hedges to demonstrate the hedging effectiveness of the model. 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:736,756, 2009 [source]


    Dynamic hedging with futures: A copula-based GARCH model

    THE JOURNAL OF FUTURES MARKETS, Issue 11 2008
    Chih-Chiang Hsu
    In a number of earlier studies it has been demonstrated that the traditional regression-based static approach is inappropriate for hedging with futures, with the result that a variety of alternative dynamic hedging strategies have emerged. In this study the authors propose a class of new copula-based GARCH models for the estimation of the optimal hedge ratio and compare their effectiveness with that of other hedging models, including the conventional static, the constant conditional correlation (CCC) GARCH, and the dynamic conditional correlation (DCC) GARCH models. With regard to the reduction of variance in the returns of hedged portfolios, the empirical results show that in both the in-sample and out-of-sample tests, with full flexibility in the distribution specifications, the copula-based GARCH models perform more effectively than other dynamic hedging models. 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1095,1116, 2008 [source]


    Canonical valuation and hedging of index options

    THE JOURNAL OF FUTURES MARKETS, Issue 8 2007
    Philip Gray
    Canonical valuation is a nonparametric method for valuing derivatives proposed by M. Stutzer (1996). Although the properties of canonical estimates of option price and hedge ratio have been studied in simulation settings, applications of the methodology to traded derivative data are rare. This study explores the practical usefulness of canonical valuation using a large sample of index options. The basic unconstrained canonical estimator fails to outperform the traditional Black,Scholes model; however, a constrained canonical estimator that incorporates a small amount of conditioning information produces dramatic reductions in mean pricing errors. Similarly, the canonical approach generates hedge ratios that result in superior hedging effectiveness compared to Black,Scholes-based deltas. The results encourage further exploration and application of the canonical approach to pricing and hedging derivatives. 2007 Wiley Periodicals, Inc. Jnl Fut Mark 27: 771,790, 2007 [source]


    A simplified approach to modeling the co-movement of asset returns

    THE JOURNAL OF FUTURES MARKETS, Issue 6 2007
    Richard D. F. Harris
    The authors propose a simplified multivariate GARCH (generalized autoregressive conditional heteroscedasticity) model (the S-GARCH model), which involves the estimation of only univariate GARCH models, both for the individual return series and for the sum and difference of each pair of series. The covariance between each pair of return series is then imputed from these variance estimates. The proposed model is considerably easier to estimate than existing multivariate GARCH models and does not suffer from the convergence problems that characterize many of these models. Moreover, the model can be easily extended to include more complex dynamics or alternative forms of the GARCH specification. The S-GARCH model is used to estimate the minimum-variance hedge ratio for the FTSE (Financial Times and the London Stock Exchange) 100 Index portfolio, hedged using index futures, and compared to four of the most widely used multivariate GARCH models. Using both statistical and economic evaluation criteria, it was found that the S-GARCH model performs at least as well as the other models that were considered, and in some cases it was better. 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:575,598, 2007 [source]


    An empirical analysis of the relationship between hedge ratio and hedging horizon using wavelet analysis

    THE JOURNAL OF FUTURES MARKETS, Issue 2 2007
    Donald Lien
    In this article, optimal hedge ratios are estimated for different hedging horizons for 23 different futures contracts using wavelet analysis. The wavelet analysis is chosen to avoid the sample reduction problem faced by the conventional methods when applied to non-overlapping return series. Hedging performance comparisons between the wavelet hedge ratio and error-correction (EC) hedge ratio indicate that the latter performs better for more contracts for shorter hedging horizons. However, the performance of the wavelet hedge ratio improves with the increase in the length of the hedging horizon. This is true for both within-sample and out-of-sample cases. 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:127,150, 2007 [source]


    A note on the superiority of the OLS hedge ratio

    THE JOURNAL OF FUTURES MARKETS, Issue 11 2005
    Donald Lien
    Suppose that spot and futures prices are generated from an error-correction model. This note demonstrates that, although the OLS model is misspecified, it provides a hedge ratio that usually outperforms the hedge ratio derived from the correct error-correction model. The opposite result is possible only when the postsample incurs a major structural change from the estimation sample. 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:1121,1126, 2005 [source]


    Estimating the optimal hedge ratio with focus information criterion

    THE JOURNAL OF FUTURES MARKETS, Issue 10 2005
    Donald Lien
    In recent years, the error-correction model without lags has been used in estimating the minimum-variance hedge ratio. This article proposes the use of the same error-correction model, but with lags in spot and futures returns in estimating the hedge ratio. In choosing the lag structure, use of the Akaike information criterion (AIC) and recently proposed focus information criterion (FIC) by G. Claeskens and N. L. Hjort (2003) is suggested. The proposed methods are applied to 24 different futures contracts. Even though the FIC hedge ratio is expected to perform better in terms of mean-squared error, the AIC hedge ratio is found to perform as well as the FIC and better than the simple hedge ratios in terms of hedging effectiveness. 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:1011, 1024, 2005 [source]


    The use of term structure information in the hedging of mortgage-backed securities

    THE JOURNAL OF FUTURES MARKETS, Issue 7 2005
    Jason Fink
    This article examines the importance of term structure variables in the hedging of mortgage-backed securities (MBS) with Treasury futures. Koutmos, G., Kroner, K., and Pericli, A. (1998) find that the optimal hedge ratio is time varying; we determine the effect of yield levels and slopes on this variation. As these variables are closely tied with mortgage refinancing, intuition suggests them to be relevant determinants of the hedge ratio. It was found that a properly specified model of the time varying hedge ratio that excludes the level and slope of the yield curve from the information set would provide similar out-of-sample hedging results to a model in which term structure information is included. Thus, both the level of interest rates and the slope of the yield curve are unimportant variables in determining the empirically optimal hedge ratio between MBS and Treasury futures contracts. 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:661,678, 2005 [source]


    Conditional OLS minimum variance hedge ratios

    THE JOURNAL OF FUTURES MARKETS, Issue 10 2004
    Jolle Miffre
    The paper presents a new methodology to estimate time dependent minimum variance hedge ratios. The so-called conditional OLS hedge ratio modifies the static OLS approach to incorporate conditioning information. The ability of the conditional OLS hedge ratio to minimize the risk of a hedged portfolio is compared to conventional static and dynamic approaches, such as the nave hedge, the roll-over OLS hedge, and the bivariate GARCH(1,1) model. The paper concludes that, both in-sample and out-of-sample, the conditional OLS hedge ratio reduces the basis risk of an equity portfolio better than the alternatives conventionally used in risk management. 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:945,964, 2004 [source]


    A Markov regime switching approach for hedging stock indices

    THE JOURNAL OF FUTURES MARKETS, Issue 7 2004
    Amir Alizadeh
    In this paper we describe a new approach for determining time-varying minimum variance hedge ratio in stock index futures markets by using Markov Regime Switching (MRS) models. The rationale behind the use of these models stems from the fact that the dynamic relationship between spot and futures returns may be characterized by regime shifts, which, in turn, suggests that by allowing the hedge ratio to be dependent upon the "state of the market," one may obtain more efficient hedge ratios and hence, superior hedging performance compared to other methods in the literature. The performance of the MRS hedge ratios is compared to that of alternative models such as GARCH, Error Correction and OLS in the FTSE 100 and S&P 500 markets. In and out-of-sample tests indicate that MRS hedge ratios outperform the other models in reducing portfolio risk in the FTSE 100 market. In the S&P 500 market the MRS model outperforms the other hedging strategies only within sample. Overall, the results indicate that by using MRS models market agents may be able to increase the performance of their hedges, measured in terms of variance reduction and increase in their utility. 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:649,674, 2004 [source]


    An empirical analysis of the relationship between the hedge ratio and hedging horizon: A simultaneous estimation of the short- and long-run hedge ratios

    THE JOURNAL OF FUTURES MARKETS, Issue 4 2004
    Sheng-Syan Chen
    This article analyzes the effects of the length of hedging horizon on the optimal hedge ratio and hedging effectiveness using 9 different hedging horizons and 25 different commodities. We discuss the concept of short- and long-run hedge ratios and propose a technique to simultaneously estimate them. The empirical results indicate that the short-run hedge ratios are significantly less than 1 and increase with the length of hedging horizon. We also find that hedging effectiveness increases with the length of hedging horizon. However, the long-run hedge ratio is found to be close to the nave hedge ratio of unity. This implies that, if the hedging horizon is long, then the nave hedge ratio is close to the optimum hedge ratio. 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:359,386, 2004 [source]


    Optimum futures hedge in the presence of clustered supply and demand shocks, stochastic basis, and firm's costs of hedging

    THE JOURNAL OF FUTURES MARKETS, Issue 12 2003
    Carolyn W. Chang
    In a doubly stochastic jump-diffusion economy with stochastic jump arrival intensity and proportional transaction costs, we develop a five-factor risk-return asset pricing inequality to model optimum futures hedge in the presence of clustered supply and demand shocks, stochastic basis, and firm's costs of hedging. Concave risk-return tradeoff dictates a hedge ratio to be substantially less than the traditional risk-minimization one. The ratio now comprises a positive diffusion, a positive jump, and a negative hedging cost component. The faster jumps arrive, and the more hedging costs, the more pronounced are the respective jump and hedging cost effects. Empirical validation confirms that actual industry hedge ratios vary significantly across firm's costs of and efficiency in hedging and are significantly lower than what risk-minimization dictates. The model also can be used to compute a threshold production level for determining if a firm should hedge. 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:1209,1237, 2003 [source]


    A note on the derivation of Black-Scholes hedge ratios

    THE JOURNAL OF FUTURES MARKETS, Issue 11 2003
    Tie Su
    An option hedge ratio is the sensitivity of an option price with respect to price changes in the underlying stock. It measures the number of shares of stocks to hedge an option position. This article presents a simple derivation of the hedge ratios under the Black-Scholes option-pricing framework. The proof is succinct and easy to follow. 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:1119,1122, 2003 [source]


    Robust estimation of the optimal hedge ratio

    THE JOURNAL OF FUTURES MARKETS, Issue 8 2003
    Richard D. F. Harris
    When using derivative instruments such as futures to hedge a portfolio of risky assets, the primary objective is to estimate the optimal hedge ratio (OHR). When agents have mean-variance utility and the futures price follows a martingale, the OHR is equivalent to the minimum variance hedge ratio,which can be estimated by regressing the spot market return on the futures market return using ordinary least squares. To accommodate time-varying volatility in asset returns, estimators based on rolling windows, GARCH, or EWMA models are commonly employed. However, all of these approaches are based on the sample variance and covariance estimators of returns, which, while consistent irrespective of the underlying distribution of the data, are not in general efficient. In particular, when the distribution of the data is leptokurtic, as is commonly found for short horizon asset returns, these estimators will attach too much weight to extreme observations. This article proposes an alternative to the standard approach to the estimation of the OHR that is robust to the leptokurtosis of returns. We use the robust OHR to construct a dynamic hedging strategy for daily returns on the FTSE100 index using index futures. We estimate the robust OHR using both the rolling window approach and the EWMA approach, and compare our results to those based on the standard rolling window and EWMA estimators. It is shown that the robust OHR yields a hedged portfolio variance that is marginally lower than that based on the standard estimator. Moreover, the variance of the robust OHR is as much as 70% lower than the variance of the standard OHR, substantially reducing the transaction costs that are associated with dynamic hedging strategies. 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:799,816, 2003 [source]


    On a Mean,Generalized Semivariance Approach to Determining the Hedge Ratio

    THE JOURNAL OF FUTURES MARKETS, Issue 6 2001
    Sheng-Syan Chen
    A new mean-risk hedge ratio based on the concept of generalized semivariance (GSV) is proposed. The proposed mean-GSV (M-GSV) hedge ratio is consistent with the GSV-based risk,return model developed by Fishburn (1977), Bawa (1975, 1978), and Harlow and Rao (1989). The M-GSV hedge ratio can also be considered an extension of the GSV-minimizing hedge ratio considered by De Jong, De Roon, and Veld (1997) and Lien and Tse (1998, 2000). The M-GSV hedge ratio is estimated for Standard & Poor's (S&P) 500 futures and compared to six other widely used hedge ratios. Because all the hedge ratios considered are known to converge to the minimum-variance (Johnson) hedge ratio under joint normality and martingale conditions, tests for normality and martingale conditions are carried out. The empirical results indicate that the joint normality and martingale hypotheses do not hold for the S&P 500 futures. The M-GSV hedge ratio varies less than the GSV hedge ratio for low and relevant levels of risk aversion. Furthermore, the M-GSV hedge ratio converges to a value different from the values of the other hedge ratios for higher values of risk aversion. 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21: 581,598, 2001 [source]


    Hedging Performance and Stock Market Liquidity: Evidence from the Taiwan Futures Market

    ASIA-PACIFIC JOURNAL OF FINANCIAL STUDIES, Issue 3 2010
    Hsiu-Chuan Lee
    G14; G15; G18 Abstract This paper examines the impact of stock market liquidity on the hedging performance of stock index futures, and extends the conditional OLS model described by Miffre [Journal of Futures Markets 24 (2004) 945] by including stock market liquidity in the regression model. The empirical results indicate that information regarding stock market liquidity is useful in predicting the optimal hedge ratio under different market conditions. In a bear market, the conditional OLS model with stock market liquidity provides the best hedging performance for the out-of-sample period. Although the OLS model outperforms the generalized autoregressive conditional heteroskedasticity and conditional OLS models for the out-of-sample period in a bull market, the conditional OLS model with stock market liquidity outperforms the conditional OLS model without stock market liquidity in terms of downside risks (lower partial moment). [source]


    Negative Market Volatility Risk Premium: Evidence from the LIFFE Equity Index Options,

    ASIA-PACIFIC JOURNAL OF FINANCIAL STUDIES, Issue 5 2009
    Bing-Huei Lin
    Abstract We provide non-parametric empirical evidence regarding negative volatility risk premium using LIFFE equity index options. In addition, we incorporate the moment-adjusted option delta hedge ratio to mitigate the effect of model misspecification. From the results, we observe several interesting phenomena. First, the delta-hedged gains are negative. Second, with a correction for model misspecification, higher-order moments measures show less significance and the volatility risk premium still plays a key role in affecting delta-hedged gains. All empirical evidence supports the existence of negative volatility risk premium in LIFFE equity index options. [source]


    Ex Ante Hedging Effectiveness of UK Stock Index Futures Contracts: Evidence for the FTSE 100 and FTSE Mid 250 Contracts

    EUROPEAN FINANCIAL MANAGEMENT, Issue 4 2000
    Darren Butterworth
    Ex ante hedging effectiveness of the FTSE 100 and FTSE Mid 250 index futures contracts is examined for a range of portfolios, consisting of stock market indexes and professionally managed portfolios (investment trust companies). Previous studies which focused on ex post hedging performance using spot portfolios that mirror market indexes are shown to overstate the risk reduction potential of index futures. Although ex ante hedge ratios are found to be characterised by intertemporal instability, ex ante hedging performance of direct hedges and cross hedges approaches that of the ex post benchmark when hedge ratios are estimated using a sufficient window size. [source]


    Hedging and value at risk: A semi-parametric approach

    THE JOURNAL OF FUTURES MARKETS, Issue 8 2010
    Zhiguang Cao
    The non-normality of financial asset returns has important implications for hedging. In particular, in contrast with the unambiguous effect that minimum-variance hedging has on the standard deviation, it can actually increase the negative skewness and kurtosis of hedge portfolio returns. Thus, the reduction in Value at Risk (VaR) and Conditional Value at Risk (CVaR) that minimum-variance hedging generates can be significantly lower than the reduction in standard deviation. In this study, we provide a new, semi-parametric method of estimating minimum-VaR and minimum-CVaR hedge ratios based on the Cornish-Fisher expansion of the quantile of the hedged portfolio return distribution. Using spot and futures returns for the FTSE 100, FTSE 250, and FTSE Small Cap equity indices, the Euro/US Dollar exchange rate, and Brent crude oil, we find that the semiparametric approach is superior to the standard minimum-variance approach, and to the nonparametric approach of Harris and Shen (2006). In particular, it provides a greater reduction in both negative skewness and excess kurtosis, and consequently generates hedge portfolios that in most cases have lower VaR and CVaR. 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:780,794, 2010 [source]


    Estimation and hedging effectiveness of time-varying hedge ratio: Flexible bivariate garch approaches

    THE JOURNAL OF FUTURES MARKETS, Issue 1 2010
    Sung Yong Park
    Bollerslev's (1990, Review of Economics and Statistics, 52, 5,59) constant conditional correlation and Engle's (2002, Journal of Business & Economic Statistics, 20, 339,350) dynamic conditional correlation (DCC) bivariate generalized autoregressive conditional heteroskedasticity (BGARCH) models are usually used to estimate time-varying hedge ratios. In this study, we extend the above model to more flexible ones to analyze the behavior of the optimal conditional hedge ratio based on two (BGARCH) models: (i) adopting more flexible bivariate density functions such as a bivariate skewed- t density function; (ii) considering asymmetric individual conditional variance equations; and (iii) incorporating asymmetry in the conditional correlation equation for the DCC-based model. Hedging performance in terms of variance reduction and also value at risk and expected shortfall of the hedged portfolio are also conducted. Using daily data of the spot and futures returns of corn and soybeans we find asymmetric and flexible density specifications help increase the goodness-of-fit of the estimated models, but do not guarantee higher hedging performance. We also find that there is an inverse relationship between the variance of hedge ratios and hedging effectiveness. 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:71,99, 2010 [source]


    Pricing and hedging illiquid energy derivatives: An application to the JCC index

    THE JOURNAL OF FUTURES MARKETS, Issue 5 2008
    Elisa Scarpa
    In this paper a simple strategy for pricing and hedging a swap on the Japanese crude oil cocktail (JCC) index is discussed. The empirical performance of different econometric models is compared in terms of their computed optimal hedge ratios, using monthly data on the JCC over the period January 2000,January 2006. An explanation to how to compute a bid/ask spread and to construct the hedging position for the JCC swap contract with variable oil volume is provided. The swap pricing scheme with backtesting and rolling regression techniques is evaluated. The empirical findings show that the price-level regression model permits one to compute more precise optimal hedge ratios relative to its competing alternatives. 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:464,487, 2008 [source]


    Multi-period hedge ratios for a multi-asset portfolio when accounting for returns co-movement

    THE JOURNAL OF FUTURES MARKETS, Issue 2 2008
    Viviana Fernandez
    This study presents a model to select the optimal hedge ratios of a portfolio composed of an arbitrary number of commodities. In particular, returns dependency and heterogeneous investment horizons are accounted for by copulas and wavelets, respectively. A portfolio of London Metal Exchange metals is analyzed for the period July 1993,December 2005, and it is concluded that neglecting cross correlations leads to biased estimates of the optimal hedge ratios and the degree of hedge effectiveness. Furthermore, when compared with a multivariate-GARCH specification, our methodology yields higher hedge effectiveness for the raw returns and their short-term components. 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:182,207, 2008 [source]