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Financial Asset Returns, Market Timing, and Volatility Dynamics

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  • Peter Christoffersen
  • Francis X. Diebold

Abstract

We consider three sets of phenomena that feature prominently and separately in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs with implications for market timing, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (1) Volatility dependence produces sign dependence, so long as expected returns are nonzero. Hence one should expect sign dependence, given the overwhelming evidence of volatility dependence. (2) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence. In particular, sign dependence does not imply market inefficiency. (3) Sign dependence is not likely to be found via analysis of sign autocorrelations, because the nature of sign dependence is highly nonlinear. (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. Nous considérons trois ensembles de phénomènes qui sont souvent - et séparément - discutés dans la littérature d'économie financière, à savoir la dépendance de la moyenne conditionnelle (ou l'absence de dépendance) dans les rendements d'actifs, la dépendance (et donc prévisibilité) des signes de rendements d'actifs ainsi que leurs implications dans le timing du marché, et la dépendance (et donc prévisibilité) dans les volatilités des rendements d'actifs. Nous montrons que ces phénomènes sont étroitement interreliés et nous explorons leurs relations en détail. Entre autres, nous montrons que : 1) la dépendance de la volatilité produit une dépendance du signe tant que les rendements attendus sont non nuls. On devrait par conséquent s attendre à une dépendance du signe, étant donné la présence notoire de dépendance de volatilité; 2) le résultat classique qui ne trouve que peu ou pas de dépendance de la moyenne conditionnelle est parfaitement compatible avec un degré significatif de dépendance de signe et de dépendance de volatilité. En particulier, la dépendance de signe n'implique pas une inefficacité du marché; 3) Il est peu probable qu'une analyse des autocorrélations de signes révèle une dépendance de signe, parce que la nature de la dépendance du signe est fortement non linéaire; 4) il est également peu probable que l'on retrouve une dépendance de signe dans des rendements à très haute fréquence (par exemple quotidiens) ou à très basse fréquence (par exemple annuels). Il est plus probable qu'on la trouve avec des horizons de rendements intermédiaires.

Suggested Citation

  • Peter Christoffersen & Francis X. Diebold, 2002. "Financial Asset Returns, Market Timing, and Volatility Dynamics," CIRANO Working Papers 2002s-02, CIRANO.
  • Handle: RePEc:cir:cirwor:2002s-02
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    2. Jaehun Chung & Yongmiao Hong, 2007. "Model-free evaluation of directional predictability in foreign exchange markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 855-889.
    3. Ayedi Ahmed & Marjène Gana & Stéphane Goutte & Khaled Guesmi, 2023. "Managing Portfolio Risk During the BREXIT Crisis: A Cross-Quantilogram Analysis of Stock Markets and Commodities Across European Countries, the US, and BRICS," Working Papers halshs-04068651, HAL.
    4. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    5. Alex Maynard, 2006. "The forward premium anomaly: statistical artefact or economic puzzle? New evidence from robust tests," Canadian Journal of Economics, Canadian Economics Association, vol. 39(4), pages 1244-1281, November.
    6. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    7. Aviral Kumar Tiwari & Muhammad Shahbaz & Rabeh Khalfaoui & Rizwan Ahmed & Shawkat Hammoudeh, 2024. "Directional predictability from energy markets to exchange rates and stock markets in the emerging market countries (E7 + 1): New evidence from cross‐quantilogram approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 719-789, January.
    8. Linton, Oliver & Whang, Yoon-Jae, 2003. "A quantilogram approach to evaluating directional predictability," LSE Research Online Documents on Economics 2112, London School of Economics and Political Science, LSE Library.
    9. Linton, O. & Whang, Yoon-Jae, 2007. "The quantilogram: With an application to evaluating directional predictability," Journal of Econometrics, Elsevier, vol. 141(1), pages 250-282, November.
    10. repec:wyi:journl:002068 is not listed on IDEAS

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    More about this item

    Keywords

    Sign prediction; direction of change; volatility timing; investment horizon; prédiction des signes; direction de changement; timing de la volatilité; horizon d'investissement;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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