Dynamic Conditional Bias-Adjusted Carry Cost Rate Futures Hedge Ratios

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.

Currency Hedging Strategies Using Histogram-Valued Data: Bivariate Markov Switching GARCH Models

Previous studies aimed at determining hedging strategies commonly used daily closing spot and futures prices for the analysis and strategy building. However, the daily closing price might not be the appropriate for price in some or all trading days. This is because the intraday data at various minute intervals, in our view, are likely to better reflect the information about the concrete behavior of the market returns and reactions of the market participants. Therefore, in this study, we propose using high-frequency data along with daily data in an attempt to determine hedging strategies, using five major international currencies against the American dollar. Specifically, in our study we used the 5-min, 30-min, 60-min, and daily closing prices of the USD/CAD (Canadian Dollar), USD/CNY (Chinese Yuan), USD/EUR (Euro), USD/GBP (British Pound), and USD/JPY (Japanese Yen) pairs over the 2018–2019 period. Using data at 5-min, 30-min, and 60-min intervals or high-frequency data, however, means the use of a relatively large number of observations for information extractions in general and econometric model estimations, making data processing and analysis a rather time-consuming and complicated task. To deal with such drawbacks, this study collected the high-frequency data in the form of a histogram and selected the representative daily price, which does not have to be the daily closing value. Then, these histogram-valued data are used for investigating the linear and nonlinear relationships and the volatility of the interested variables by various single- and two-regime bivariate GARCH models. Our results indicate that the Markov Switching Dynamic Copula-Generalized autoregressive conditional heteroskedasticity (GARCH) model performs the best with the lowest BIC and gives the highest overall value of hedging effectiveness (HE) compared with the other models considered in the present endeavor. Consequently, we can conclude that the foreign exchange market for both spot and futures trading has a nonlinear structure. Furthermore, based on the HE results, the best derivatives instrument is CAD using one-day frequency data, while GBP using 30-min frequency data is the best considering the highest hedge ratio. We note that the derivative with the highest hedging effectiveness might not be the one with the highest hedge ratio.

Optimal futures hedging strategies based on an improved kernel density estimation method

In this paper, we study the hedging effectiveness of crude oil futures on the basis of the lower partial moments (LPMs). An improved kernel density estimation method is proposed to estimate the optimal hedge ratio. We investigate crude oil price hedging by contributing to the literature in the following two-fold: first, unlike the existing studies which focus on univariate kernel density method, we use bivariate kernel density to calculate the estimated LPMs, wherein the two bandwidths of the bivariate kernel density are not limited to the same, which is our main innovation point. According to the criterion of minimizing the mean integrated square error, we derive the conditions that the optimal bandwidths satisfy. In the process of derivation, we make a distribution assumption “locally” in order to simplify calculation, but this type of “local” distribution assumption is far better than “global” distribution assumption used in parameter method theoretically and empirically. Second, in order to meet the requirement of bivariate kernel density for independent random variables, we adopt ARCH models to obtain the independent noises with related to the returns of crude oil spot and futures. Genetic algorithm is used to tune the parameters that maximize Quasi-likelihood. Empirical results reveal that, at first, the hedging strategy based on the improved kernel density estimation method is of highly efficiency, then it achieves better performance than the hedging strategy based on the traditional parametric method. We also compare the risk control effectiveness of static hedge ratio vs. time-varying hedge ratio, and find that static hedging has a better performance than time-varying hedging.

Research on the Hedge Ratio of China's Crude Oil Futures — Based on DCC-GARCH Model

Crude oil plays an important role in economic development. This paper chooses China’s crude oil futures and crude oil actuals as the research objects, and builds the DCC-GARCH model to study the hedge ratio under the risk minimization standard. The hedge ratios obtained from the DCC-GARCH model will be compared with those obtained from OLS, B-VAR and VECM models. The empirical results prove that: China’s crude oil futures and actuals have a significant reverse “leverage effect”; China’s crude oil futures have a variance reduction of more than 70% under all models; the DCC-GARCH model achieves the best hedging performance in the four models.

Modeling Electricity Price and Quantity Uncertainty: An Application for Hedging with Forward Contracts

Energy transactions in liberalized markets are subject to price and quantity uncertainty. This paper considers the spot price and energy generation to follow a bivariate semi-nonparametric distribution defined in terms of the Gram–Charlier expansion. This distribution allows us to jointly model not only mean, variance, and correlation but also skewness, kurtosis, and higher-order moments. Based on this model, we propose a static hedging strategy for electricity generators that participate in a competitive market where hedging is carried out through forward contracts that include a risk premium in their valuation. For this purpose, we use Monte Carlo simulation and consider information from the Colombian electricity market as the case study. The results show that the volume of energy to be sold under long-term contracts depends on each electricity generator and the risk assessment made by the market in the forward risk premium. The conditions of skewness, kurtosis, and correlation, as well as the type of the employed risk indicator, affect the hedging strategy that each electricity generator should implement. A positive correlation between the spot price and energy production tends to increase the hedge ratio; meanwhile, negative correlation tends to reduce it. The increase of forward risk premium, on the other hand, reduces the hedge ratio.