A Regime Switching for Dynamic Conditional Correlation and GARCH: Application to Agricultural Commodity Prices and Market Risks

Chodchuangnirun, Benchawanaree; Yamaka, Woraphon; Khiewngamdee, Chatchai

doi:10.1007/978-3-319-75429-1_24

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Billio and Caporin [16] and Chodchuangnirun, Yamaka, and Khiewngamdee [21] introduced the extension of the CCC-GARCH and DCC-GARCH of Engle [12] to the Markov switching model of Hamilton [22]. These two models have similar structures, with the only difference being that MS-CCC-GARCH assumes the regime-dependent correlation matrix R s t to be constant in each regime, while the regime-dependent correlation matrix is considered to be varying over time in each regime, R t,s t , for MS-DCC-GARCH.…”

Section: Markov Switching(ms)-ccc-garch and Markov Switching(ms)-dcc-garchmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Markov Switching(ms)-ccc-garch and Markov Switching(ms)-dcc-garchmentioning

confidence: 99%

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

et al. 2021

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Hedging Benefit of Safe-Haven Gold in Terms of Co-skewness and Covariance in Stock Market

Thongkairat

Yamaka

Sriboonchitta

2019

Lecture Notes in Computer Science

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Impact of Economic Policy Uncertainty on the Stock Exchange of Thailand: Evidence from the Industry-Level Stock Returns in Thailand

Punwong

Kaewsompong

Tansuchat

2020

Studies in Computational Intelligence

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A Regime Switching for Dynamic Conditional Correlation and GARCH: Application to Agricultural Commodity Prices and Market Risks

Cited by 4 publications

References 13 publications

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

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

Hedging Benefit of Safe-Haven Gold in Terms of Co-skewness and Covariance in Stock Market

Impact of Economic Policy Uncertainty on the Stock Exchange of Thailand: Evidence from the Industry-Level Stock Returns in Thailand

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