Minimum Variance Hedge Ratios Research Articles

Detrended minimum-variance hedge ratio: A new method for hedge ratio at different time scales

In this paper, based on the detrended fluctuation analysis (DFA) method and the detrended cross-correlation analysis (DCCA) method, we propose an improved method of minimum-variance (MV) hedge ratio, i.e., the detrended minimum-variance (D-MV) hedge ratio, which can measure the hedge ratio at different time scales. The proposed D-MV hedge ratio is defined as the detrended covariance function between spot and futures returns divided by the detrended variance function of futures returns. Through the simulated and empirical analysis, we find that (i) the outcomes of the hedge ratio and the corresponding hedging effectiveness for the D-MV hedge ratio are diverse at different time scales, which can meet needs of various hedging participants with different hedging horizons; (ii) our proposed D-MV hedge ratio has a better hedging performance and a greater potential to determine the hedge ratio because its results of hedging effectiveness at most of time scales are better than those of the traditional MV hedge ratio; and (iii) as for the method of D-MV hedge ratio for different polynomial orders m in the fitting procedure, the D-MV-1 hedge ratio (i.e., the linear polynomial in the fitting procedure) has the best hedging capability for determining the hedge ratio.

A Square-Root T Hedging Rule for Nonstorable Products

This note derives a hedging rule under the assumption of noisy forward prices. Forward prices are assumed to be noisy due to non-storability of the underlying product and the resulting lack of carry arbitrage. The minimum-variance hedge ratio increases nonlinearly with decreasing time to maturity of the forward contract, as more information about the expected spot price arrives and the forward price becomes less noisy. If the noise component follows a process similar to a random walk, the precision of the forward price increases as a square-root function of the remaining trading time. By setting the initial hedge ratio according to hypothetically efficient prices, a producer overhedges and the minimum-variance hedge is not achieved.

Hedging with Stock Index Options: A Mean-Extended Gini Approach

One of the more efficient methods to hedge portfolios of securities whose put options are not traded is to use stock index options. We use the mean-extended Gini (MEG) model to derive the optimal hedge ratios for stock index options. We calculate the MEG ratios for some main stocks traded on the Tel Aviv Stock Exchange and compare them to the minimum-variance hedge ratios. Computed for specific values of risk aversion, MEG hedge ratios combine systematic risk with basis risk. Our results show that increasing the risk aversion used in the computation reduces the size of the hedge ratio, implying that less put options are needed to hedge away each and every security.

Optimal Commodity and Cross-Currency Heding: The Case of Asean-5-Based Grain and Soft Commodity Traders

The changes in commodity prices and exchange rates leave the representative ASEAN-5-based international commodity traders exposed with multiple risks. Foreign exchange hedging ratios are simultaneously estimated alongside commodity ratios in a time-varying portfolio framework. The comparisons of the minimum variance hedge ratios (MVHR) performance reveal that the model with the time-varying variance-covariance matrix provides greater risk reduction than the conventional model, illustrating importance of time-varying variance-covariance effect when modeling joint dynamic of spot and futures returns and hence estimating hedging strategies. When transaction costs are taken into account, incorporating Student’s t significantly enhances the performance of dynamic strategies in wheat and soybeans markets. However, the conventional OLS shows superior performance in the cotton market in terms of utility gain.

Assessing the influence of spot price predictability on electricity futures hedging

A common feature of energy prices is that spot price changes are partially predictable due to weather and demand seasonalities. This paper follows the Ederington and Salas (2008) framework and considers the expected change in spot prices when minimum variance hedge ratios are computed. The poor effectiveness of hedging strategies obtained in previous studies on electricity was because the standard hedging approach underestimates the effectiveness of hedging. In the empirical study made in this paper, weekly spot price risk is hedged with weekly futures in the Nord Pool electricity market. In this case, the optimal selection of the futures contract may produce risk reductions whose values vary between 60% and 80% – depending on the hedging duration (one to three weeks) and the analysed sub-period (in-sample and out-of-sample sub-periods).

An Empirical Analysis of Dynamic Multiscale Hedging Using Wavelet Decomposition

This paper investigates the hedging effectiveness of a dynamic moving window OLS hedging model, formed using wavelet decomposed time-series. The wavelet transform is applied to calculate the appropriate dynamic minimum-variance hedge ratio for various hedging horizons for a number of assets. The effectiveness of the dynamic multiscale hedging strategy is then tested, both in- and out-of-sample, using standard variance reduction and expanded to include a downside risk metric, the time horizon dependent Value-at-Risk. Measured using variance reduction, the effectiveness converges to one at longer scales, while a measure of VaR reduction indicates a portion of residual risk remains at all scales. Analysis of the hedge portfolio distributions indicate that this unhedged tail risk is related to excess portfolio kurtosis found at all scales.

Using Regression Techniques to Estimate Futures Hedge Ratios, Some Results from Alternative Approaches Applied to Australian 10 Year Treasury Bond Futures

The paper uses Australian bond futures data from the Sydney Futures Exchange to critically assess some of the potential problems involved in the use of cointegration techniques in the calculation of minimum variance hedge ratios. Following Ghosh (1993a,b) there have been a number of papers which have made use of these techniques. Ghosh (1993), and Lien (1996) suggest that if spot and futures prices are cointegrated then the non-inclusion of an error correction term in the VAR model used to estimate the hedge ratio will lead to mis-specification problems and the under-estimation of the true optimal hedge ratio. We examine the use of such regression techniques in the calculation of hedge ratios. In particular we consider the extent to which the stacking of the data into a time series, which effectively constrains the estimated hedge ratio to a single value over the span of the data, influences the results of such techniques. If the hedge ratio differs by contract, the movement from one contract to the next is likely to lead to instability in the estimated regression coefficients. Tests for parameter instability in the estimated regression suggest that this is indeed the case and our conclusion is that it is preferable to consider the estimation of the hedge ratio in a panel setting with each individual contract considered as an observational unit. One problem in the past with such a move has been the lack of tests for cointegration and unit roots in such a setting, fortunately these are now available and we take advantage of them in this paper. In such a panel setting we find that the result that the spot and futures prices are cointegrated still holds but that the estimated hedge ratios are not constant between contracts, throwing doubt on the applicability of regression methods which make such an assumption.

Hedging and Optimal Hedge Ratios for International Index Futures Markets

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.

Recovery of hidden state participation effects on oil and gas asset values

Commodity price shocks are shown to cause shifts in both the quantity and timing of risk in natural resource assets. We provide evidence that static risk measures understate the periodicity of price risk implicit in depleting assets. Risk measurement is demonstrated to be asset specific and to vary heterogeneously in response to the combined effects of state participation and market factors. We use a global sample of oilfield assets to demonstrate that oilfield participation terms cause corporate asset cash flows, volatility horizons and minimum variance hedge ratios to vary in response to oil price. We provide additional insights into movements in the timing of physical oil and gas asset risk, a hidden effect not recoverable from market oil prices. Temporal variance for physical assets is shown to be a hidden dimensional outcome of the effects of market factors and state participation.

Hedging House Price Risk with CME Futures Contracts: The Case of Las Vegas Residential Real Estate

Until the recent introduction of real estate futures on the Chicago Mercantile Exchange (CME), there have been few opportunities to manage house price risk. This paper examines whether house price risk can be effectively hedged in Las Vegas, one of the CME contract cities. The analysis considers hedging from the viewpoint of real estate investment groups, mortgage portfolio investors, builder/developers and individual homeowners. For investment groups and mortgage holders holding a mix of new and existing home assets, CME futures would have reduced house price risk by more than 88% over the 1994–2006 period. Similarly, homeowners implicitly hedging price volatility of existing homes also would have fared well over the sample period. However, builder/developers worried about new home price appreciation would have been much less successful in managing their risk. One important caveat, minimum variance hedge ratios change over time and may cause hedge performance to suffer.

Multi-futures Hedging Decision Model Based on Capital Constrain

Abstract On the basis of the theory of relationship between futures and futures, the correlation between futures and spots, and the minimum variance hedge ratios, we build the multi-futures hedging decision model by considering capital constrain of multi-futures hedge. The multi-futures hedging model on the basis of capital constrain is the innovation of this article. We use the multiple GARCH model to forecast the hedging capital requirement. On the condition of grasping the capital requirement in future, it can avoid the failure to hedge as a result of lack of money. We use the Dalian Commodity Exchange historical data of soybean futuresbean cake futures and bean oil spots price to validate the decision model.

Basis Convergence and Long Memory in Volatility When Dynamic Hedging with Futures

AbstractWhen market returns follow a long memory volatility process, standard approaches to estimating dynamic minimum variance hedge ratios (MVHRs) are misspecified. Simulation results and an application to the S&P 500 index document the magnitude of the misspecification that results from failure to account for basis convergence and long memory in volatility. These results have important implications for the estimation of MVHRs in the S&P 500 example and other markets as well.

The Limits to Minimum-Variance Hedging

In this paper, we compare the estimated minimum-variance hedge ratios from a range of conditional hedging models with the ‘realized’ minimum variance hedge ratio constructed using intraday data. We show that the reduction in conditionally hedged portfolio variance falls far short of the ex post maximal reduction in variance obtained using the realized minimum variance hedge ratio. While this is partly due to systematic bias, correcting for this bias does little to improve hedging effectiveness. The poor performance of conditional hedging models is therefore more likely to be attributable to the unpredictability of the integrated hedge ratio.

Asymmetric effect of basis on dynamic futures hedging: Empirical evidence from commodity markets

The dynamic minimum variance hedge ratios (MVHRs) have been commonly estimated using the Bivariate GARCH model that overlooks the basis effect on the time-varying variance–covariance of spot and futures returns. This paper proposes an alternative specification of the BGARCH model in which the effect is incorporated for estimating MVHRs. Empirical investigation in commodity markets suggests that the basis effect is asymmetric, i.e., the positive basis has greater impact than the negative basis on the variance and covariance structure. Both in-sample and out-of-sample comparisons of the MVHR performance reveal that the model with the asymmetric effect provides greater risk reduction than the conventional models, illustrating importance of the asymmetric effect when modeling the joint dynamics of spot and futures returns and hence estimating hedging strategies.

A bivariate Markov regime switching GARCH approach to estimate time varying minimum variance hedge ratios

This article develops a new bivariate Markov regime switching BEKK-Generalized Autoregressive Conditional Heteroscedasticity (GARCH) (RS-BEKK-GARCH) model. The model is a state-dependent bivariate BEKK-GARCH model and an extension of Gray's univariate generalized regime-switching (GRS) model to the bivariate case. To solve the path-dependency problem inherent in the bivariate regime switching BEKK-GARCH model, we propose a recombining method for the covariance term in the conditional variance-covariance matrix. The model is applied to estimate time-varying minimum variance hedge ratios for corn and nickel spot and futures prices. Out-of-sample point estimates of hedging portfolio variance show that compared to the state-independent BEKK-GARCH model, the RS-BEKK-GARCH model improves out-of-sample hedging effectiveness for both corn and nickel data. We perform White's (2000) data-snooping reality check to test for predictive superiority of RS-BEKK-GARCH over the benchmark model and find that the difference in variance reduction between BEKK-GARCH and RS-BEKK-GARCH is not statistically significant for either data set at conventional confidence levels.

Model-free hedge ratios and scale-invariant models

A price process is scale-invariant if and only if the returns distribution is independent of the price measurement scale. We show that most stochastic processes used for pricing options on financial assets have this property and that many models not previously recognised as scale-invariant are indeed so. We also prove that price hedge ratios for a wide class of contingent claims under a wide class of pricing models are model-free. In particular, previous results on model-free price hedge ratios of vanilla options based on scale-invariant models are extended to any contingent claim with homogeneous pay-off, including complex, path-dependent options. However, model-free hedge ratios only have the minimum variance property in scale-invariant stochastic volatility models when price–volatility correlation is zero. In other stochastic volatility models and in scale-invariant local volatility models, model-free hedge ratios are not minimum variance ratios and our empirical results demonstrate that they are less efficient than minimum variance hedge ratios.

Asymmetric Effect of Basis on Dynamic Futures Hedging: Empirical Evidence from Commodity Markets

Dynamic minimum variance hedge ratios (MVHRs) have been commonly estimated using Bivariate GARCH model that overlooks basis effect on the time-varying variance-covariance of spot and futures returns. This paper proposes an alternative specification of the BGARCH model in which the basis effect is incorporated for estimating MVHRs. Empirical investigation in commodity markets suggests that basis effect is asymmetric, i.e., positive basis has greater impact than negative basis on the variance and covariance structure. Both in-sample and out-of-sample comparisons of the MVHR performance reveal that the model with the asymmetric effect provides greater risk reduction than the conventional models, illustrating importance of the asymmetric effect when modeling joint dynamics of spot and futures returns and hence estimating hedging strategies.

Recovery of Hidden Economic and State Variable Effects on Oil and Gas Asset Value and Risk Measures

Commodity price shocks are shown to cause shifts in both the quantum and timing of risk in natural resource assets. We provide evidence that static risk measures understate the periodicity of price risk implicit in depleting assets. Risk measurement is demonstrated to be asset specific and to vary heterogeneously in response to the combined effects state and economic variables. We use a global sample of oilfield assets to demonstrate that oilfield participation terms cause corporate asset cash flows, volatility horizons and minimum variance hedge ratios to vary in response to oil price. We provide additional insights into movements in the timing of physical O&G asset risk, a hidden effect not recoverable from market oil prices. Temporal variance for physical assets is shown to be a hidden dimensional outcome of the effects of economic and state variables.

Do the pure martingale and joint normality hypotheses hold for futures contracts?: Implications for the optimal hedge ratios

It is well known that the optimal hedge ratios derived based on the mean-variance approach, the expected utility maximizing approach, the mean extended-Gini approach, and the generalized semivariance approach will all converge to the minimum-variance hedge ratio if the futures price follows a pure martingale process and if the spot and futures returns are jointly normal. In this paper, we perform empirical tests to see if the pure martingale and joint normality hypotheses hold using 25 different futures contracts and five different hedging horizons. Our results indicate that the pure martingale hypothesis holds for all commodities and all hedging horizons except for three stock index futures contracts. As for joint normality, we propose two new tests based on the generalized method of moments, which allow for calculating multivariate test statistics that take account of the contemporaneous correlation across spot and futures returns. Our findings show that the joint normality hypothesis generally does not hold except for a few contracts and relatively long hedging horizons.

Is Minimum Variance Hedging Necessary for Equity Indices? A Study of Hedging and Cross-Hedging Exchange Traded Funds

This paper investigates the optimal short-term hedging of Exchange Traded Fund (ETF) portfolios with index futures. Using daily data from May 2000 to December 2004 on the four largest passive ETFs (the Spider, the Diamond, the Cubes and the Russell iShare) and their corresponding index futures we examine the performance of minimum variance hedges for efficient variance reduction and for investors with exponential utility. Our findings relate to daily hedging based on OLS regression, exponentially weighted moving averages and ECM-GARCH models and the utility-based performance evaluation criterion is adopted to capture an efficient reduction in skewness and kurtosis as well as the variance. The basis risk on US equity indices is now extremely low and as a result we find no evidence that minimum variance hedge ratios outperform a naive 1:1 futures hedge, either for individual ETFs or for portfolios of ETFs. Where minimum variance hedge ratios are useful is for the cross-hedging of ETFs, i.e., the netting of long-short positions prior to placing a futures hedge. We also find that hedging of an ETF portfolio with just one index future can be almost as effective as hedging with all the relevant index futures. Our results should be of interest to tax arbitrage investors in ETFs and their market makers, who often face large and heterogeneous creation and redemption demands on different ETFs. Both types of traders may consider hedging their positions overnight or over a few days.