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Empirical Evidences on the Interconnectedness between Sampling and Asset Returns’ Distributions

Author

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  • Giuseppe Orlando

    (Department of Economics and Finance, Università degli Studi di Bari Aldo Moro, Via C. Rosalba 53, 70124 Bari, Italy)

  • Michele Bufalo

    (Department of Methods and Models for Economics, Università degli Studi di Roma “La Sapienza”, Territory and Finance, Via del Castro Laurenziano 9, 00185 Roma, Italy)

Abstract

The aim of this work was to test how returns are distributed across multiple asset classes, markets and sampling frequency. We examine returns of swaps, equity and bond indices as well as the rescaling by their volatilities over different horizons (since inception to Q2-2020). Contrarily to some literature, we find that the realized distributions of logarithmic returns, scaled or not by the standard deviations, are skewed and that they may be better fitted by t-skew distributions. Our finding holds true across asset classes, maturity and developed and developing markets. This may explain why models based on dynamic conditional score (DCS) have superior performance when the underlying distribution belongs to the t-skew family. Finally, we show how sampling and distribution of returns are strictly connected. This is of great importance as, for example, extrapolating yearly scenarios from daily performances may prove not to be correct.

Suggested Citation

  • Giuseppe Orlando & Michele Bufalo, 2021. "Empirical Evidences on the Interconnectedness between Sampling and Asset Returns’ Distributions," Risks, MDPI, vol. 9(5), pages 1-35, May.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:5:p:88-:d:550538
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    References listed on IDEAS

    as
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    Cited by:

    1. Bufalo, Michele & Orlando, Giuseppe, 2023. "A three-factor stochastic model for forecasting production of energy materials," Finance Research Letters, Elsevier, vol. 51(C).
    2. Francesco Cesarone & Raffaello Cesetti & Giuseppe Orlando & Manuel Luis Martino & Jacopo Maria Ricci, 2022. "Comparing SSD-Efficient Portfolios with a Skewed Reference Distribution," Mathematics, MDPI, vol. 11(1), pages 1-20, December.
    3. Orlando, Giuseppe, 2022. "Simulating heterogeneous corporate dynamics via the Rulkov map," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 32-42.
    4. Orlando, Giuseppe & Bufalo, Michele, 2022. "Modelling bursts and chaos regularization in credit risk with a deterministic nonlinear model," Finance Research Letters, Elsevier, vol. 47(PA).

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