Wavelet multiresolution analysis of high-frequency Asian FX rates, Summer 1997

Karuppiah, Jeyanthi; Los, Cornelis A.

doi:10.1016/j.irfa.2004.06.014

Cited by 69 publications

(27 citation statements)

References 76 publications

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“…Hence, market dynamics in the aggregate will be the result of the interaction of agents with heterogeneous time horizons. To model the behavior of financial series at different time spans, researchers have resorted to wavelet analysis, a mathematical tool developed in the early 1990s (see, e.g., Connor & Rossiter, 2005;Fernandez, 2005;Fernandez, 2006aFernandez, , 2006bFernandez & Lucey, 2007;Gençay, Whitcher, & Selçuk 2003In & Kim, 2006a, 2006bKaruppiah & Los, 2005;Lin & Stevenson 2001).…”

Section: Heterogeneous Investors and Wavelet Analysismentioning

confidence: 99%

Multi‐period hedge ratios for a multi‐asset portfolio when accounting for returns co‐movement

Fernández

2007

Journal of Futures Markets

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This study presents a model to select the optimal hedge ratios of a portfolio composed of an arbitrary number of commodities. In particular, returns dependency and heterogeneous investment horizons are accounted for by copulas and wavelets, respectively. A portfolio of London Metal Exchange metals is analyzed for the period July 1993-December 2005, and it is concluded that neglecting cross correlations leads to biased estimates of the optimal hedge ratios and the degree of hedge effectiveness. Furthermore, when compared with a multivariate-GARCH specification, our methodology yields higher hedge effectiveness for the raw returns and their short-term components. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:182-207, 2008 Financial support from SOC 06/01-2 Grant (Research Division of the University of Chile) and from an institutional grant of the Hewlett Foundation to CEA is greatly acknowledged. All remaining errors are the author's. INTRODUCTIONThe determination of the optimal hedge ratio is an issue of both practical and theoretical interests as it impacts hedging effectiveness. Recent contributions to the literature on heterogeneous investors and the selection of an optimal hedge ratio have focused on a single commodity in isolation (e.g., In & Kim, 2006a, 2006bLien & Shrestha, 2007). This study presents a generalization of such an approach, in which the selection of optimal hedge ratios is considered for a portfolio composed of several net positions in commodities (i.e., cash position minus a proportion of a futures contract). In particular, an analytical expression is obtained for an optimal vector of hedge ratios and a measure of the degree hedge effectiveness (HE) for such a portfolio. Toward that end, wavelet and copula analysis is used to accommodate for the existence of heterogeneous investors and asset returns dependency, respectively. The empirical application used in this study considers a portfolio composed of cash and futures positions on London Metal Exchange (LME) metals for the sample period July 1993-December 2005. The portfolio choice is based on the grounds of data availability on cash and futures prices at a high frequency. Indeed, wavelet analysis is a computationally intensive statistical tool, which requires long time series (e.g., Gençay, Whitcher, & Selçuk, 2002;Percival & Walden, 2000). In contrast, given that the LME data have been analyzed earlier (e.g., McMillan, 2005), our conclusions can be contrasted with those of previous studies.The simulations we carry out to characterize the distribution functions of the hedge ratios and the degree of HE are performed under two scenarios: with and without accounting for the cross correlations of returns on cash and futures contracts. As mentioned above, dependency is handled by copula analysis, a statistical tool that has recently gained ground in the finance field to extract the dependence structure from the joint probability distribution function of a set of random variables (e.g., Cherubini, Luciano, & Vecchiato, 2004).Indeed, earlier at...

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Section: Heterogeneous Investors and Wavelet Analysismentioning

confidence: 99%

Multi‐period hedge ratios for a multi‐asset portfolio when accounting for returns co‐movement

Fernández

2007

Journal of Futures Markets

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“…Gençay et al (2001) and Ramsey (2002) provide ample exposition on the use and versatility of wavelet techniques in economics and finance. During the past decade the methodology gained currency and relevant applications of wavelets include analyses of stocks (Fernandez, 2006(Fernandez, , 2008In and Kim, 2006;Rua and Nunes, 2009), commodities (Vacha and Barunik, 2012;Graham et al, 2013;Reboredo and Rivera-Castro, 2014a), exchange rates (Nekhili et al, 2002;Karuppiah and Los, 2005;Nikkinen et al, 2011), and other financial and economic variables or their interactions In, 2005, 2007;Faÿ et al, 2009;Rua, 2010;Aguiar-Conraria and Soares, 2011;Gallegati et al 2011;Aguiar-Conraria et al, 2012;Reboredo and Rivera-Castro, 2014b). By using wavelets we are able to test the hypothesis on the existence of homogeneity in dynamic correlations across various investment horizons among assets, an issue that so far has been largely overlooked in the literature.…”

Section: Introductionmentioning

confidence: 99%

Gold, oil, and stocks: Dynamic correlations

Baruník

Kočenda

Vácha

2016

International Review of Economics & Finance

116

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations across a number of investment horizons between pairs of assets is a dominant feature during times of economic downturn and financial turbulence for all three pairs of the assets under research. Heterogeneity prevails in correlations between gold and stocks. After the 2008 crisis, correlations among all three assets increase and become homogenous: the timing differs for the three pairs but coincides with the structural breaks that are identified in specific correlation dynamics. A strong implication emerges: during the period under research, and from a different-investment-horizons perspective, all three assets could be used in a well-diversified portfolio only during relatively short periods. Terms of use: Documents inJEL-Code: C010, C130, C580, F370, G110, G150.

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“…Financial time series such as the exchange rate time series are also affected by the news (Andersen et al, 2007). The existence of non-periodic cycles in the structurally complicated exchange time series is approved by the researches (Karuppiaha, & Los, 2005). As mentioned above there are much evidences, which indicate the complexity of predicting exchange rates as nonlinear trend.…”

Section: Introductionmentioning

confidence: 98%

Exchange rate prediction with multilayer perceptron neural network using gold price as external factor

Fathian¹,

Kia²

2012

MSL

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In this paper, the problem of predicting the exchange rate time series in the foreign exchange rate market is going to be solved using a time-delayed multilayer perceptron neural network with gold price as external factor. The input for the learning phase of the artificial neural network are the exchange rate data of the last five days plus the gold price in two different currencies of the exchange rate as the external factor for helping the artificial neural network improving its forecast accuracy. The five-day delay has been chosen because of the weekly cyclic behavior of the exchange rate time series with the consideration of two holidays in a week. The result of forecasts are then compared with using the multilayer peceptron neural network without gold price external factor by two most important evaluation techniques in the literature of exchange rate prediction. For the experimental analysis phase, the data of three important exchange rates of EUR/USD, GBP/USD, and USD/JPY are used.

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Wavelet multiresolution analysis of high-frequency Asian FX rates, Summer 1997

Cited by 69 publications

References 76 publications

Multi‐period hedge ratios for a multi‐asset portfolio when accounting for returns co‐movement

Multi‐period hedge ratios for a multi‐asset portfolio when accounting for returns co‐movement

Gold, oil, and stocks: Dynamic correlations

Exchange rate prediction with multilayer perceptron neural network using gold price as external factor

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