The Hedge Ratio and the Empirical Relationship between the Stock and Futures Markets: A New Approach Using Wavelet Analysis*

In, Francis Haeuck; Kim, Sangbae

doi:10.1086/499138

Cited by 160 publications

(99 citation statements)

References 22 publications

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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

120

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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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Section: Introductionmentioning

confidence: 99%

Gold, oil, and stocks: Dynamic correlations

Baruník

Kočenda

Vácha

2016

International Review of Economics & Finance

120

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“…Since the points in time t i andt j are measured in seconds since 1st January 1970, we just put these values into (2), and the machinery continues working unchanged. Note that the different timezones for the price data need to be considered because some stock exchanges are in the United States while others are in Europe.…”

Section: Remarkmentioning

confidence: 99%

“…One approach is to decompose the time series of two markets on a scale-by-scale basis into components with different frequencies using wavelets. The lead-lag relationship is studied by comparing the components of one selected level of the wavelet transformation for two markets, see e.g., [1][2][3][4][5]. More on wavelet methods in finance can be found in the book of Gençay, Selçuk and Whitcher [6].…”

Section: Introductionmentioning

confidence: 99%

Lead–Lag Relationship Using a Stop-and-Reverse-MinMax Process

Maier-Paape

Platen

2016

Risks

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Abstract:The intermarket analysis, in particular the lead-lag relationship, plays an important role within financial markets. Therefore, a mathematical approach to be able to find interrelations between the price development of two different financial instruments is developed in this paper. Computing the differences of the relative positions of relevant local extrema of two charts, i.e., the local phase shifts of these price developments, gives us an empirical distribution on the unit circle. With the aid of directional statistics, such angular distributions are studied for many pairs of markets. It is shown that there are several very strongly correlated financial instruments in the field of foreign exchange, commodities and indexes. In some cases, one of the two markets is significantly ahead with respect to the relevant local extrema, i.e., there is a phase shift unequal to zero between them.

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“…The wavelet transform approach is particularly applicable to financial and economic time series and has been widely documented in previous studies (In and Kim, 2006;Gençay et al, 2001;Percival and Walden, 2000). The pioneering work on wavelet multi-scale analysis in finance is documented in Ramsey et al (1995), where they examine the contribution of the wavelet approach in detecting self-similarity in US stock prices, whilst Ramsey and Lampart (1998) study the money, income and expenditure link using the wavelet-based scaling method.…”

Section: (2004)mentioning

confidence: 99%