Black-Litterman model with copula-based views in mean-CVaR portfolio optimization framework with weight constraints

Tamara Teplova (), Mikova Evgeniia (), Qaiser Munir () and Nataliya Pivnitskaya ()
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Tamara Teplova: National Research University Higher School of Economics
Mikova Evgeniia: National Research University Higher School of Economics
Qaiser Munir: Department of Economics, Institute of Business Administration
Nataliya Pivnitskaya: National Research University Higher School of Economics

Economic Change and Restructuring, 2023, vol. 56, issue 1, No 19, 515-535

Abstract: Abstract This study examines the portfolio optimization problem by exploiting daily data of 10 international Exchange Trade Funds (ETF) from 2012 to 2022. We extend the Black-Litterman (BL) approach using ARMA-GARCH-copula-based expected returns as a proxy for investor views and use the CVaR metric as a risk measure in the optimization procedure. The BL approach provides a Bayesian methodology for combining the equilibrium returns and the investor views to produce expected returns. We use Regular Vine (R-vine) copula since it provides a flexible multivariate dependency modeling. The suggested approach is compared against the copula-CVaR portfolio, which likewise a BL copula approach avoids excessive corner solutions that many optimization approaches would generate in case of extreme values of estimated parameters. We compare the performance of these two approaches using out-of-sample back-testing against two benchmarks: Mean–Variance optimizations (MV) and equal weights portfolio (EW). To further reduce the sensitivity of considered strategies to input parameters, we evaluate out-of-sample performance at three levels of maximum weight constraints: 30%, 40%, and 50%. Moreover, in this paper, we consider different levels of view confidence—τ in the Black-Litterman model as it significantly affects the obtained results and inferences. We calculate and report the portfolios’ tail risks, maximum drawdown, turnover, and the break-even point for all optimization approaches. Our empirical analysis indicates better performance for the CBL portfolio regarding lower tail risk and higher risk-adjusted returns, and the copula-CVaR portfolio is better regarding lower turnover and higher break-even point.

Keywords: Asset allocation; Portfolio optimization; Copula; Black-Litterman; CVaR; ARMA-GARCH (search for similar items in EconPapers)
JEL-codes: C58 C61 G11 G15 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10644-022-09435-y

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