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Portfolio Optimisation Under Flexible Dynamic Dependence Modelling

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  • Mauro Bernardi
  • Leopoldo Catania

Abstract

Signals coming from multivariate higher order conditional moments as well as the information contained in exogenous covariates, can be effectively exploited by rational investors to allocate their wealth among different risky investment opportunities. This paper proposes a new flexible dynamic copula model being able to explain and forecast the time-varying shape of large dimensional asset returns distributions. Moreover, we let the univariate marginal distributions to be driven by an updating mechanism based on the scaled score of the conditional distribution. This framework allows us to introduce time-variation in up to the fourth moment of the conditional distribution. The time-varying dependence pattern is subsequently modelled as function of a latent Markov Switching process, allowing also for the inclusion of exogenous covariates in the dynamic updating equation. We empirically assess that the proposed model substantially improves the optimal portfolio allocation of rational investors maximising their expected utility.

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  • Mauro Bernardi & Leopoldo Catania, 2016. "Portfolio Optimisation Under Flexible Dynamic Dependence Modelling," Papers 1601.05199, arXiv.org.
  • Handle: RePEc:arx:papers:1601.05199
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    References listed on IDEAS

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    Cited by:

    1. Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Forecasting Cryptocurrencies Financial Time Series," Working Papers No 5/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    2. Leopoldo Catania & Stefano Grassi, 2017. "Modelling Crypto-Currencies Financial Time-Series," CEIS Research Paper 417, Tor Vergata University, CEIS, revised 11 Dec 2017.

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