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Predicting International Equity Returns: Evidence from Time-Varying Parameter Vector Autoregressive Models

Author

Listed:
  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Florian Huber

    (Department of Economics, WU Vienna University of Economics and Business)

  • Philipp Piribauer

    (Austrian Institute of Economic Research (WIFO))

Abstract

In this paper, we forecast monthly stock returns of eight advanced economies using a time varying parameter vector autoregressive model (TVP-VAR). Compared to standard TVP-VARs, our proposed model automatically detects whether time-variation in the parameters is needed through the introduction of a latent threshold process that is driven by the absolute size of parameter changes. The advantage of this framework is that it can dynamically detect whether a given regression coefficient is constant or time-varying during distinct time periods. We moreover compare the performance of this model with a wide range of nested alternative time-varying and constant parameter VAR models. Our results indicate that the threshold TVP-VAR outperforms its competitors in terms of point and density forecasts. A portfolio allocation exercise confirms the superiority of our proposed framework. In addition, a copula-based analysis also indicates that it pays off to adopt a multivariate modeling framework, especially during periods of stress, like the recent financial crisis.

Suggested Citation

  • Rangan Gupta & Florian Huber & Philipp Piribauer, 2018. "Predicting International Equity Returns: Evidence from Time-Varying Parameter Vector Autoregressive Models," Working Papers 201826, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201826
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    Cited by:

    1. is not listed on IDEAS
    2. Malte Knuppel & Fabian Kruger & Marc-Oliver Pohle, 2022. "Score-based calibration testing for multivariate forecast distributions," Papers 2211.16362, arXiv.org, revised Dec 2023.
    3. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2022. "Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data," Energies, MDPI, vol. 15(22), pages 1-26, November.
    4. Oguzhan Cepni & Rangan Gupta & Qiang Ji, 2023. "Sentiment Regimes and Reaction of Stock Markets to Conventional and Unconventional Monetary Policies: Evidence from OECD Countries," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(3), pages 365-381, July.
    5. Afees A. Salisu & Rangan Gupta, 2023. "Oil Price Returns Skewness and Forecastability of International Stock Returns Over One Century of Data," Working Papers 202339, University of Pretoria, Department of Economics.
    6. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Forecasting Stock Market Returns in Advanced Economies over a Century," Mathematics, MDPI, vol. 11(9), pages 1-21, April.
    7. Tsai, I-Chun & Chen, Han-Bo & Lin, Che-Chun, 2024. "The ability of energy commodities to hedge the dynamic risk of epidemic black swans," Resources Policy, Elsevier, vol. 89(C).
    8. Afees A. Salisu & Rangan Gupta, 2021. "Commodity Prices and Forecastability of South African Stock Returns Over a Century: Sentiments versus Fundamentals," Working Papers 202144, University of Pretoria, Department of Economics.
    9. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2023. "Tail risks and forecastability of stock returns of advanced economies: evidence from centuries of data," The European Journal of Finance, Taylor & Francis Journals, vol. 29(4), pages 466-481, March.
    10. Afees A. Salisu & Rangan Gupta, 2025. "Commodity Risk and Forecastability of International Stock Returns: The Role of Oil Returns Skewness," Risks, MDPI, vol. 13(3), pages 1-20, March.

    More about this item

    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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