Mean-variance hedging in the presence of estimation risk

Review of Derivatives Research - The mean-variance hedging (MVH) with a significant risk-aversion coefficient is approximately equal to the minimum-variance (MV) hedge. However, how large the...     

Selective hedging strategies for crude oil futures based on market state expectations

This paper studies the ex-ante selective hedging strategies of crude oil futures contracts based on market state expectations and compares the hedging performances to the traditional minimum variance routine hedging strategies. The main advantage of the proposed method is that it achieves a trade-off between return and risk, rather than hedges risk at all costs. Specifically, we first use a multi-input Hidden Markov Model(HMM) to identify the market state, assess the market’s herding impact, and then integrate the findings of identification and measurement to forecast the price trend. We offer an adjustment criterion for the hedge ratios driven by GARCH²-type models based on the anticipated market state. We conducted an empirical analysis to examine the hedging effect of WTI and Brent crude oil futures, the results indicate that the proposed state-dependent hedging strategies are superior to the traditional model-driven hedging strategies concerning the hedged portfolio based on four criteria. The robustness check reveals that the proposed hedging strategies still outperform in different market situation. The findings can help traders in the crude oil markets, and the methodology can be applied to other energy markets.     

Commodity futures hedging,risk aversion and the hedging horizon

This paper examines the impact of management preferences on optimal futures hedging strategy and associated performance. Applying an expected utility hedging objective, the optimal futures hedge ratio is determined for a range of preferences on risk aversion, hedging horizon and expected returns. Empirical results reveal substantial hedge ratio variation across distinct management preferences and are supportive of the hedging policies of real firms. Hedging performance is further shown to be strongly dependent on underlying preferences. In particular, hedgers with high risk aversion and short horizon reduce hedge portfolio risk but achieve inferior utility in comparison to those with low aversion.     

Mean-variance hedging for continuous processes: New proofs and examples

Let be a special semimartingale of the form and denote by the mean-variance tradeoff process of . Let be the space of predictable processes for which the stochastic integral is a square-integrable semimartingale. For a given constant and a given square-integrable random variable , the mean-variance optimal hedging strategy by definition minimizes the distance in between and the space . In financial terms, provides an approximation of the contingent claim by means of a self-financing trading strategy with minimal global risk. Assuming that is bounded and continuous, we first give a simple new proof of the closedness of in and of the existence of the F?llmer-Schweizer decomposition. If moreover is continuous and satisfies an additional condition, we can describe the mean-variance optimal strategy in feedback form, and we provide several examples where it can be computed explicitly. The additional condition states that the minimal and the variance-optimal martingale measures for should coincide. We provide examples where this assumption is satisfied, but we also show that it will typically fail if is not deterministic and includes exogenous randomness which is not induced by .     

Optimal futures hedging under jump switching dynamics

The article develops a Markov regime switching Generalized Orthogonal GARCH model with conditional jump dynamics (JSGO) for optimal futures hedging. To the author's knowledge, there is no existing study on dynamic futures hedging investigating both the effects of regime switching and conditional jumps. This might be the fact that there is no existing hedging model encompassing both of these features. The JSGO solves this problem by introducing a jump switching filtering algorithm to infer ex post both the distributions of jumps and state variables and a recombining procedure to solve the path-dependency problem. To justify the usefulness of the JSGO on dynamic futures hedging, hedging exercises are performed using FTSE 100 futures data traded in the London International Financial Futures and Options Exchange (LIFFE). JSGO exhibits good out-of-sample performance compared to its jump-free and state-independent counterparts in terms of both criteria of variance reductions and utility improvements.     

Optimal dynamic hedging in incomplete futures markets

This article derives optimal hedging demands for futures contracts from an investor who cannot freely trade his portfolio of primitive assets in the context of either a CARA or a logarithmic utility function. Existing futures contracts are not numerous enough to complete the market. In addition, in the case of CARA, the nonnegativity constraint on wealth is binding, and the optimal hedging demands are not identical to those that would be derived if the constraint were ignored. Fictitiously completing the market, we can characterize the optimal hedging demands for futures contracts. Closed-form solutions exist in the logarithmic case but not in the CARA case, since then a put (insurance) written on his wealth is implicitly bought by the investor. Although solutions are formally similar to those that obtain under complete markets, incompleteness leads in fact to second-best optima.     

Dynamic hedging with futures: a copula-based GARCH model with high-frequency data

Modeling the joint distribution of spot and futures returns is crucial for establishing optimal hedging strategies. This paper proposes a new class of dynamic copula-GARCH models that exploits information from high-frequency data for hedge ratio estimation. The copula theory facilitates constructing a flexible distribution; the inclusion of realized volatility measures constructed from high-frequency data enables copula forecasts to swiftly adapt to changing markets. By using data concerning equity index returns, the estimation results show that the inclusion of realized measures of volatility and correlation greatly enhances the explanatory power in the modeling. Moreover, the out-of-sample forecasting results show that the hedged portfolios constructed from the proposed model are superior to those constructed from the prevailing models in reducing the (estimated) conditional hedged portfolio variance. Finally, the economic gains from exploiting high-frequency data for estimating the hedge ratios are examined. It is found that hedgers obtain additional benefits by including high-frequency data in their hedging decisions; more risk-averse hedgers generate greater benefits.     

Cross-commodity hedging for illiquid futures: Evidence from China's base metal futures market

This paper evaluates the effectiveness of cross-commodity hedging between China's base metal spot and futures markets, using daily data of metal spot and futures prices in the Shanghai Futures Exchange. The main findings suggest that, compared to unhedged spot portfolios, a naïve hedge increases risk exposure, while static and dynamic hedges can significantly reduce the risk of holding spot assets. Zinc futures and nickel futures outperform other base metal futures in individually hedging lead spot and tin spot respectively, while copper futures constitute a moderately optimal instrument to hedge both lead and tin spot assets.     

Cross hedging single stock with American Depositary Receipt and stock index futures

This paper investigates the cross hedging effectiveness of individual stock in a market that does not have single stock futures traded using American Depositary Receipt (ADR) and stock index futures. We apply Caporin and Billio’s Multivariate regime switching GARCH to capture the state-dependent covariance structure of underlying stock, ADR and stock index futures. Empirical results indicate that in general simultaneous hedging with both ADR and index futures creates hedging gains and incorporating regime switching effects further increases the hedging performances.     

Stock index futures hedging in the emerging Malaysian market

The paper investigates hedging effectiveness of dynamic and constant models in the emerging market of Malaysia where trading information is not readily available and market liquidity is lower compared to the developed equity markets. Using daily data from December 1995 to April 2001 and bivariate GARCH(1,1) and TGARCH models, the paper uses differing variance–covariance structures to obtain hedging ratios. Performance of models is compared in terms of variance reduction and expected utility levels for the full sample period as well as the three sub-periods which encompass the Asian financial crisis and introduction of new capital control measures in Malaysia. Findings show that rankings of the hedging models change for the in-sample period depending on evaluation criteria used. TGARCH based models provide better hedging performance but only in the period of higher information asymmetry following the imposition of capital controls in Malaysia. Overall, despite the structural breaks caused by the Asian financial crisis and new capital control regulations, out of sample hedging performance of dynamic GARCH models in the Malaysian emerging market is as good as the one reported for the highly developed markets in the previous literature. The findings suggest that changes in the composition of market agents caused by large scale retreat of foreign investors following the imposition of capital control regulations do not seem to have any material impact on the volatility characteristics of the Malaysian emerging market.     

Determination of stock closing prices and hedging performance with stock indices futures

This paper examines the impact of the determination of stock closing prices on futures price efficiency and hedging effectiveness with stock indices futures. The empirical results indicate that the increase in the length of the batching period of the stock closing call improves price efficiency in the futures closing prices and then enhances hedging performance in terms of the hedging risks. Additionally, from a utility‐maximization point of view, hedging performance does not improve after the introduction of the 5 min stock closing call, which can be explained by an improvement in price efficiency at the futures market close.     

Evaluating the performance of futures hedging using factors-driven realized volatility

The complexity and uncertainty of the financial market mainly stem from the rich market internal transaction information and a wide range effect of external factors. To this end, this paper proposes the combination factors-driven forecasting method to predict realized volatilities of the CSI 300 index and index futures. Based on the volatilities predicted by the proposed method, we further evaluate the ex-ante hedging performance in comparison to the conventional HAR model as well as GARCH-type models. The empirical results indicate that the factors-driven realized volatility model significantly dominates the other commonly used models in terms of hedging effectiveness. Furthermore, the superiority of the proposed method is robust in different market conditions, including significant rising or falling and abnormal market fluctuations in the COVID-19 pandemic, and in different index markets. Therefore, this paper improves the prediction accuracy of volatility by integrating market internal transaction information and external factor information, and the proposed method in this paper can be used by investors to obtain an excellent hedging effect.     

A regime-switching real-time copula GARCH model for optimal futures hedging

A regime-switching real-time copula GARCH (RSRTCG) model is suggested for optimal futures hedging. The specification of RSRTCG is to model the margins of asset returns with state-dependent real-time GARCH and the dependence structure of asset returns with regime switching copula functions. RSRTCG is faster in adjusting to the new level of volatility under different market regimes which is a regime-switching multivariate generalization of the state-independent univariate real-time GARCH. RSRTCG is applied to cross hedge the price risk of S&P 500 sector indices with crude oil futures. The empirical results show that RSRTCG possesses superior hedging performance compared to its nested non-real-time or state-independent copula GARCH models based on the criterion of percentage variance reduction, utility gain, model confidence set, model combination strategy, risk-adjusted return and reward-to-semivariance ratio.     

Production flexibility, stochastic separation, hedging, and futures prices

We study a dynamic model where uncertainty about interim outputadjustments causes producers to face price, cost and outputuncertainty. Stochastically separable production decisions areindependent of the producer's risk preferences and expectationsand are based on the prevailing futures price as a certain outputprice. Conditions under which futures contracts achieve stochasticseparation are established. Optimal hedging and maturity structureof futures contracts, equilibrium futures prices, and the effectsof futures trading on output are studied. The systematic riskpremium depends on the product of the futures beta and the covarianceof the market return with production revenues.     

Multivariate GARCH hedge ratios and hedging effectiveness in Australian futures markets

We use the All Ordinaries Index and the corresponding Share Price Index futures contract written against the All Ordinaries Index to estimate optimal hedge ratios, adopting several specifications: an ordinary least squares‐based model, a vector autoregression, a vector error‐correction model and a diagonal‐vec multivariate generalized autoregressive conditional heteroscedasticity model. Hedging effectiveness is measured using a risk‐return comparison and a utility maximization method. We find that time‐varying generalized autoregressive conditional heteroscedasticity hedge ratios perform better than constant hedge ratios in terms of minimizing risks, but when return effects are also considered, the utility‐based measure prefers the ordinary least squares method in the in‐sample hedge, whilst both approaches favour the conditional time‐varying multivariate generalized autoregressive conditional heteroscedasticity hedge ratio estimates in out‐of‐sample analyses.     

Systematic risk, hedging pressure, and risk premiums in futures markets

I examine the uniformity of risk pricing in futures and assetmarkets. Tests against a general alternative do not reflectcomplete integration of futures and asset markets. As predicted,estimates of the 'zero-beta' rate for futures are close to zero,and premiums for systematic risk do not differ significantlyacross assets and futures. There is, however, evidence consistentwith a specific alternative model presented by Hirshleifer (1988).Returns in foreign currency and agricultural futures vary withthe net holdings of hedgers, after controlling for systematicrisk. These results imply a degree of market segmentation andsupport hedging pressure as a determinant of futures premiums.     

Capturing the risk premium of commodity futures: The role of hedging pressure

We construct long–short factor mimicking portfolios that capture the hedging pressure risk premium of commodity futures. We consider single sorts based on the open interests of hedgers or speculators, as well as double sorts based on both positions. The long–short hedging pressure portfolios are priced cross-sectionally and present Sharpe ratios that systematically exceed those of long-only benchmarks. Further tests show that the hedging pressure risk premiums rise with the volatility of commodity futures markets and that the predictive power of hedging pressure over cross-sectional commodity futures returns is different from the previously documented forecasting power of past returns and the slope of the term structure.     

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.     

Returns and volatilities of energy futures markets: Roles of speculative and hedging sentiments

This study examines how speculative and hedging sentiments influence the returns and volatilities of energy futures markets. We construct speculative and hedging sentiment indices based on the weekly data of fund and commercial positions of four energy futures: crude oil, heating oil, gasoline, and natural gas, traded on the New York Mercantile Exchange (NYMEX) from 15 January 2013 to 5 February 2019. Our study demonstrates that speculative sentiment generates greater market fluctuations in the energy futures markets than hedging sentiment; and, further, speculative sentiment stimulates a reversal effect on the returns of crude oil futures. Moreover, speculative sentiment exerts positive systemic risk compensation on the four futures' returns, whereas hedging sentiment alleviates volatilities in the energy futures markets. Most notably, distinguishing it from the leverage effect in stock markets, the speculative sentiment in the energy futures markets is influenced more by good than by bad news; while hedging sentiment exhibits emotional neutrality, as opposed to its impact on stock markets as reported in the literature. Additionally, the positive hedging sentiment in crude oil futures demonstrates significant systemic risk compensation, whereas the three other futures do not have an influence, confirming the prevalence of speculation in hedging transactions in crude oil futures. Our further analysis shows cross-market volatility spillover effects, among which speculative sentiment inherent in crude oil futures causes volatility spillovers to the three other futures, while hedging sentiment has no such effect. Our study has implications for overseeing international energy futures markets, providing regulators with evidence that will facilitate the development of effective strategies to strengthen market supervision.     

Empirical performance of multifactor term structure models for pricing and hedging Eurodollar futures options

This article compares two one-factor, two two-factor, two three-factor models in the HJM class and Black's [Black, F. (1976). The pricing of commodity contracts. Journal of Financial Economics, 3, 167-179.] implied volatility function in terms of their pricing and hedging performance for Eurodollar futures options across strikes and maturities from 1 Jan 2000 to 31 Dec 2002. We find that three-factor models perform the best for 1-day and 1-week prediction, as well as for 5-day and 20-day hedging. The moneyness bias and the maturity bias appear for all models, but the three-factor models produce lower bias. Three-factor models also outperform other models in hedging, in particular for away-from-the-money and long-dated options. Making Black's volatility a square root or exponential function performs similar to one-factor HJM models in pricing, but not in hedging. Correctly specified and calibrated multifactor models are thus important and cannot be replaced by one-factor models in pricing or hedging interest rate contingent claims.