Market Timing Can Work in the Real World

Larsen, Glen A.; Wozniak, Gregory

doi:10.3905/jpm.1995.409524

“…Generalizations to multiple asset classes, such as stock and bond markets, involve basing allocation strategies on forecasts of the sign of the return spread. Recent literature shows that asset return sign forecasting can often be done with surprising success; see, among others, Breen et al (1989), Leitch and Tanner (1991), Wagner et al (1992), Pesaran and Timmermann (1995), Kuan and Liu (1995), Larsen and Wozniak (1995), Womack (1996), Gencay (1998), Leung et al (2000), Elliott and Ito (1999), White (2000), Pesaran and Timmermann (2004), and Cheung et al (2005).…”

Section: Introductionmentioning

confidence: 99%

Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics

Christoffersen

¹

,

Diebold

²

2006

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W e consider three sets of phenomena that feature prominently in the financial economics literature: (1) conditional mean dependence (or lack thereof) in asset returns, (2) dependence (and hence forecastability) in asset return signs, and (3) dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated and explore the relationships in detail. Among other things, we show that (1) volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (2) it is statistically possible to have sign dependence without conditional mean dependence; (3) sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests because of the special nonlinear nature of sign dependence, so that traditional market timing tests are best viewed as tests for sign dependence arising from variation in expected returns rather than from variation in volatility or higher moments; (4) sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; and (5) the link between volatility dependence and sign dependence remains intact in conditionally non-Gaussian environments, for example, with time-varying conditional skewness and/or kurtosis.

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“…Interestingly, there is substantial evidence that sign forecasting can often be done with surprising success. Relevant literature includes Breen, Glosten and Jagannathan (1989), Leitch and Tanner (1991), Wagner, Shellans and Paul (1992), Pesaran and Timmermann (1995), Kuan and Liu (1995), Larsen and Wozniak (1995), Womack (1996), Gencay (1998), Leung, Daouk and Chen (1999), Elliott and Ito (1999), White (2000), Pesaran and Timmermann (2001), and Cheung, Chinn and Pascual (2003).…”

Section: Introductionmentioning

confidence: 99%

Financial Asset Returns, Direction-of-Change Forecasting and Volatility Dynamics

Christoffersen

¹

,

Diebold

²

2003

View full text Add to dashboard Cite

W e consider three sets of phenomena that feature prominently in the financial economics literature: (1) conditional mean dependence (or lack thereof) in asset returns, (2) dependence (and hence forecastability) in asset return signs, and (3) dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated and explore the relationships in detail. Among other things, we show that (1) volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (2) it is statistically possible to have sign dependence without conditional mean dependence; (3) sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests because of the special nonlinear nature of sign dependence, so that traditional market timing tests are best viewed as tests for sign dependence arising from variation in expected returns rather than from variation in volatility or higher moments; (4) sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; and (5) the link between volatility dependence and sign dependence remains intact in conditionally non-Gaussian environments, for example, with time-varying conditional skewness and/or kurtosis.

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“…-2-evidence that sign forecasting -again the key ingredient in market timing -can often be done with surprising success. Relevant literature includes Breen, Glosten and Jagannathan (1989), Leitch and Tanner (1991), Wagner, Shellans and Paul (1992), Pesaran and Timmermann (1995), Kuan and Liu (1995), Larsen and Wozniak (1995), Womack (1996), Gencay (1998), Leung, Daouk and Chen (1999), Elliott and Ito (1999), and White (2000), among others.…”

Section: Introductionmentioning

confidence: 99%

Financial Asset Returns, Market Timing, and Volatility Dynamics

Christoffersen

¹

,

Diebold

²

2002

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Ce document est publié dans l'intention de rendre accessibles les résultats préliminaires de la recherche effectuée au CIRANO, afin de susciter des échanges et des suggestions. Les idées et les opinions émises sont sous l'unique responsabilité des auteurs, et ne représentent pas nécessairement les positions du CIRANO ou de ses partenaires. This paper presents preliminary research carried out at CIRANO and aims at encouraging discussion and comment. The observations and viewpoints expressed are the sole responsibility of the authors. They do not necessarily represent positions of CIRANO or its partners.

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Market Timing Can Work in the Real World

Cited by 29 publications

References 1 publication

Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics

Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics

Financial Asset Returns, Direction-of-Change Forecasting and Volatility Dynamics

Financial Asset Returns, Market Timing, and Volatility Dynamics

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