Abstract
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.
Highlights
As this study took into consideration the probable structural change causing the occurrence of upturn and downturn episodes in the currency market, we proposed using the Markov switching constant conditional correlation (CCC)-Generalized autoregressive conditional heteroskedasticity (GARCH), Markov switching dynamic conditional correlation (DCC)-GARCH, and Markov switching dynamic copula GARCH models as the mechanisms to find out the covariance of the spot and future returns of all currencies that were used for calculating the hedge ratio and the hedging effectiveness
This study introduces various bivariate Markov switching (MS)-GARCH models for histogram-valued data to quantify hedge ratio (HR) and hedging effectiveness (HE) for five major international currencies
This study finds that the Markov switching dynamic copula (MSDC)-GARCH model outperforms the others because it provides the highest
Summary
In building the currency hedging strategies, we considered the hedge ratio and hedging effectiveness to determine the optimal number of spot and futures contracts in an investment portfolio, as well as the risk that can be reduced. The MS-GARCH model is still not suitable for the estimation to obtain the HR and the optimal portfolio containing spot and futures contracts because it cannot provide the variance and covariance of the returns of the hedging instruments, which are key variables in the hedging equation for risk management. To the best of our knowledge, this was the first attempt ever to investigate and determine currency hedging strategies using histogram-valued data and the Markov switching dynamic copula GARCH model. We explain the procedure to compute the hedge ratio and hedging effectiveness for the currency markets
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