Dependence Structure of and Co-Movement Between Thai Currency and International Currencies After Introduction of Quantitative Easing

Pastpipatkul, Pathairat; Yamaka, Woraphon; Sriboonchitta, Songsak

doi:10.1007/978-3-319-27284-9_36

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…This indicates that the relationship between house prices and exchange rates of OECD countries is not stable in either normal or extreme market situations. However, the degree of dependence in the normal market is stronger than in the extreme markets [40].…”

Section: The DCC Copula Garch Resultsmentioning

confidence: 94%

Analyzing the Causality and Dependence between Exchange Rate and Real Estate Prices in Boom-and-Bust Markets: Quantile Causality and DCC Copula GARCH Approaches

Yamaka

Liu

et al. 2022

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Unlike most previous studies examining the causal relationship and dependence between exchange rates and real estate prices, this study aims to investigate the causal relationship and dependence between these two variables in a boom-and-bust market setting using the panel quantile Granger causality and dynamic conditional correlation (DCC) copula GARCH approaches, respectively. In the panel quantile Granger causality test, quantiles 0.1 and 0.9 are considered to represent extreme markets (bust and boom, respectively). Our first results showed the causal effects at extreme quantiles to be very different from those at the median quantile. We also found a greater causality between house prices and exchange rates in the boom market compared to the bust market. In the second model, we explored the relationship between exchange rates and real estate prices, taking boom-and-bust dynamics into account by measuring the tail dependence through the DCC copula GARCH method. Our findings confirm the strong time-varying tail dependence between house prices and exchange rates. The degree of tail dependence was quite stable over the sample period, except for the periods around 1997–1998 and 2008–2009, when the degree of tail dependence was stronger and less persistent. These two periods correspond to the two great financial crises in Asia and the USA, respectively.

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Section: The DCC Copula Garch Resultsmentioning

confidence: 94%

Analyzing the Causality and Dependence between Exchange Rate and Real Estate Prices in Boom-and-Bust Markets: Quantile Causality and DCC Copula GARCH Approaches

Yamaka

Liu

et al. 2022

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“…In this subsection, the Markov switching dynamic copula (MSDC) GARCH model is explained. The Student's-t copula function is assumed for this model as it has been commonly and successfully used for fitting financial data [17]. This model is different from the MS-CCC-GARCH and MS-DCC-GARCH in two aspects.…”

Section: Markov Switching Dynamic Copula Garchmentioning

confidence: 99%

“…The present study takes into consideration the probable existence of structural change, which demarcates the upturns and downturns episodes in the currency market. It, thus, employs the Markov switching CCC-GARCH model of Pelletier [15]; the Markov switching DCC-GARCH model of Billio and Caporin [16]; and the recent Markov switching dynamic copula GARCH models of Pastpipatkul, Yamaka, and Sriboonchitta [17] as a means to obtain the variance and covariance of spot and future returns of the international currency for calculating the hedge ratio and hedging effectiveness.…”

Section: Introductionmentioning

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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Value at Risk Analysis and Investment Portfolio Optimization of Asian Stocks

Habkhonglek¹

2022

Credible Asset Allocation, Optimal Transport Methods, and Related Topics

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Dependence Structure of and Co-Movement Between Thai Currency and International Currencies After Introduction of Quantitative Easing

Cited by 7 publications

References 11 publications

Analyzing the Causality and Dependence between Exchange Rate and Real Estate Prices in Boom-and-Bust Markets: Quantile Causality and DCC Copula GARCH Approaches

Analyzing the Causality and Dependence between Exchange Rate and Real Estate Prices in Boom-and-Bust Markets: Quantile Causality and DCC Copula GARCH Approaches

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

Value at Risk Analysis and Investment Portfolio Optimization of Asian Stocks

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