Diversification with internationalassets and cryptocurrencies usingBlack-Litterman

Authors

DOI:

https://doi.org/10.1108/REGE-05-2022-0080

Keywords:

Portfolios, Black-Litterman, Cryptoassets

Abstract

Objective: The aim of the study was to analyze the performance of Black-Litterman portfolios using a views estimation procedure that simulates investor forecasts based on technical analysis.

Methodology: Ibovespa, S&P500, Bitcoin, and IDR (interbank deposit rate) indexes were respectively considered proxies for the national, international, cryptocurrency, and fixed income stock markets. Forecasts were made out of the sample aiming at incorporation them in the Black-Litterman model, using several portfolio weighting methods from June 13,2013 to August 30, 2022.

Results: The Sharpe, Treynor, and Omega Ratios point out that the proposed model, considering only variable return assets, generates portfolios with performances superior to their traditionally calculated counterparts, with emphasis on the risk parity portfolio. Nonetheless, the inclusion of the IDR leads to performance losses, especially in scenarios with lower risk tolerance. And finally, given the impact of turnover, the naive portfolio was also detected as a viable alternative.

Practical results: The results obtained can contribute to improve investors practices, specifically by validating both the performance improvement -- when including foreign assets and cryptocurrencies --, and the application of the Black-Litterman model for asset pricing.

Originality: The main contributions of the study are: performance analysis incorporating cryptocurrencies and international assets in an uncertain recent period; the use of a methodology to compute the views simulating the behavior of managers using technical analysis; and comparing the performance of portfolio management strategies based on the Black-Litterman model, taking into account different levels of risk and uncertainty.

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References

Allaj, E. (2013). The Black–Litterman model: A consistent estimation of the parameter tau. Financial Markets and Portfolio Management, 27(2), 217–251.

Allaj, E. (2020). The Black–Litterman model and views from a reverse optimization procedure: An out-of-sample performance evaluation. Computational Management Science, 17(3), 465–492.

Batista, D.T., & Alves, C.F. (2021). Análise do impacto do Bitcoin na eficiência de uma carteira diversificada para investidores brasileiros. Revista Brasileira de Gestão de Negócios, 23(2), 353-369.

Bessler, W., Opfer, H., & Wolff, D. (2017). Multi-asset portfolio optimization and out-of-sample performance: An evaluation of Black–Litterman, mean-variance, and naïve diversification approaches. The European Journal of Finance, 23(1), 1–30.

Bhutto, S.A., Ahmed, R.R., Streimikiene, D., Shaikh, S., & Streimikis, J. (2020). Portfolio Investment diversification at Global stock market: A Cointegration Analysis of Emerging BRICS(P) Group. Acta Montanistica Slovaca, 25(1), 57–69.

Black, F. (1989). Universal Hedging: Optimizing Currency Risk and Reward in International Equity Portfolios. Financial Analysts Journal, 45(4), 16–22.

Black, F., & Litterman, R. (1992). Global Portfolio Optimization. Financial Analysts Journal, 48(5), 28–43.

Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L.I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192–198.

Canner, N., Mankiw, N.G., & Weil, D.N. (1997). An Asset Allocation Puzzle. American Economic Review, 87(1), 181–191.

Cavalcante-Filho, E., De-Losso, R., & Santos, J.C.S. (2021). Which Factors Matter to Investors? Evidence from Brazilian Mutual Funds. Revista Brasileira de Gestão de Negócios, 23, 63–80.

DeMiguel, V., & Nogales, F.J. (2009). Portfolio Selection with Robust Estimation. Operations Research, 57(3), 560–577.

Duqi, A., Franci, L., & Torluccio, G. (2014). The Black–Litterman model: The definition of views based on volatility forecasts. Applied Financial Economics, 24(19), 1285–1296.

Elsayed, A.H., Gozgor, G., & Yarovaya, L. (2022). Volatility and return connectedness of cryptocurrency, gold, and uncertainty: Evidence from the cryptocurrency uncertainty indices. Finance Research Letters, 102732.

Engle, R. (2012). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business & Economic Statistics, 20(3), 339–350.

Fernandes, B., Street, A., Fernandes, C., & Valladão, D. (2018). On an adaptive Black–Litterman investment strategy using conditional fundamentalist information: A Brazilian case study. Finance Research Letters, 27, 201–207.

Harris, R. D. F., Stoja, E., & Tan, L. (2017). The dynamic Black–Litterman approach to asset allocation. European Journal of Operational Research, 259(3), 1085–1096.

Harvey, C.R., Hoyle, E., Korgaonkar, R., Rattray, S., Sargaison, M., & Van Hemert, O. (2018). The impact of volatility targeting. The Journal of Portfolio Management, 45(1), 14-33.

Hu, A.S., Parlour, C.A., & Rajan, U. (2019). Cryptocurrencies: Stylized facts on a new investible instrument. Financial Management, 48(4), 1049–1068.

Idzorek, T. (2007). A step-by-step guide to the Black-Litterman model: Incorporating user-specified confidence levels. Em: Forecasting Expected Returns in the Financial Markets (Vol. 1, p. 17–38). Academic Press.

Iquiapaza, R.A., Vaz, G.F.C., & Borges, S.L. (2016). Portfolio evaluation of volatility timing and reward to risk timing investment strategies: The Brazilian case. Revista de Finanças Aplicadas, 7(2), 1-19.

Jensen, M.C. (1968). The Performance of Mutual Funds in the Period 1945-1964. The Journal of Finance, 23(2), 389–416.

Kara, M., Ulucan, A., & Atici, K.B. (2019). A hybrid approach for generating investor views in Black–Litterman model. Expert Systems with Applications, 128, 256–270.

Keating, C., & Shadwick, W.F. (2002). A Universal Performance Measure. Journal of Performance Measurement, 6(3), 59–84.

Kim, T. (2017). On the transaction cost of Bitcoin. Finance Research Letters, 23(1), 300–305.

Kolm, P.N., Ritter, G., & Simonian, J. (2021). Black-Litterman and Beyond: The Bayesian Paradigm in Investment Management. The Journal of Portfolio Management, 47(5), 91–113.

Ledoit, O., & Wolf, M. (2008). Robust performance hypothesis testing with the Sharpe ratio. Journal of Empirical Finance, 15(5), 850-859.

Lewis, K.K. (1999). Trying to Explain Home Bias in Equities and Consumption. Journal of Economic Literature, 37(2), 571–608.

Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91.

Modigliani, F., & Modigliani, L. (1997). Risk-adjusted performance. Journal of portfolio management, 23(2), 45–54.

NEFIN (2017). Metodologia. FEA-USP. Disponível em: https://nefin.com.br/resources/NEFIN_methodology.pdf

Neto, O.D., & Colombo, J.A. (2021). The impact of cryptocurrencies on the performance of multi-asset portfolios: Evidence from Brazil. Revista Brasileira de Financas, 19(4), 86–130.

O'Toole, R. (2013). The Black–Litterman model: A risk budgeting perspective. Journal of Asset Management, 14(1), 2-13.

Pflug, G.C., Pichler, A., & Wozabal, D. (2012). The 1/N investment strategy is optimal under high model ambiguity. Journal of Banking & Finance, 36(2), 410–417.

Platanakis, E., & Urquhart, A. (2020). Should investors include Bitcoin in their portfolios? A portfolio theory approach. The British Accounting Review, 52(4), 100837.

Portelinha, M., Campani, C.H., & Roquete, R. (2021). The impacts of cryptocurrencies in the performance of Brazilian stocks’ portfolios. Economics Bulletin, 41(3), 1919–1931.

Santos, J.O. dos, & Coelho, P.A. (2010). Análise da relação risco e retorno em carteiras compostas por índices de bolsa de valores de países desenvolvidos e de países emergentes integrantes do bloco econômico BRIC. Revista Contabilidade & Finanças, 21, 23–37.

Sharpe, W.F. (1964). Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. The Journal of Finance, 19(3), 425–442.

Treynor, J.L. (1996). How to rate management investment funds. Harvard Business Review, 43(1), 63–75.

Trimborn, S., & Härdle, W.K. (2018). CRIX an Index for cryptocurrencies. Journal of Empirical Finance, 49, 107–122.

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Published

2024-07-11

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How to Cite

Diversification with internationalassets and cryptocurrencies usingBlack-Litterman. (2024). REGE Revista De Gestão, 31(2). https://doi.org/10.1108/REGE-05-2022-0080