The Black-Litterman Model and Views from a Reverse Optimization Procedure: An Out-of-Sample Performance Evaluation

2017 ◽  
Author(s):  
Erindi Allaj
Keyword(s):  
Performance Evaluation ◽  
Optimization Procedure ◽  
Out Of Sample ◽  
Reverse Optimization

International diversification benefits: an investigation from the perspective of Chinese investors

2013 ◽  
Vol 3 (3) ◽  
pp. 225-249 ◽  
Author(s):  
Chonghui Jiang ◽  
Yongkai Ma ◽  
Yunbi An
Keyword(s):  
Optimization Procedure ◽  
Optimal Strategies ◽  
International Diversification ◽  
Expected Returns ◽  
Domestic Investment ◽  
Foreign Markets ◽  
Content Type ◽  
Econometric Methods ◽  
Out Of Sample ◽  
Set Up

PurposeThe main purpose of this paper is to investigate whether Chinese investors can benefit from international diversification and where these benefits are to be found.Design/methodology/approachThis paper applies an expanding optimization procedure, which is different from the econometric methods or Monte Carlo simulations adopted in many empirical investigations in the literature. The authors' analysis is based on various realized portfolios that are set up at different dates in the sample period.FindingsBased on a stream of realized portfolios, the authors show that Chinese investors can gain substantially in terms of risk reduction as they venture into foreign markets, regardless of the region into which they choose to diversify and whether in‐sample or out‐of‐sample performance is evaluated. However, the optimal strategies under consideration cannot achieve higher out‐of‐sample expected returns and risk‐adjusted returns than does the domestic investment.Originality/valueIn contrast with those in the literature, the authors' analysis is based on the out‐of‐sample performance of a series of realized optimal portfolios. Their method can address time‐varying correlations that are ignored in most previous research. In addition, this method not only allows them to analyze sizes of diversification benefits but also enables them to examine the major characteristics of international portfolios to gauge the effectiveness of different diversification strategies.

scholarly journals Performance evaluation for distributionally robust optimization with binary entries

2020 ◽  
Vol 11 (1) ◽  
pp. 1-9
Author(s):  
Shunichi Ohmori ◽  
Kazuho Yoshimoto
Keyword(s):  
Performance Evaluation ◽  
Robust Optimization ◽  
Optimization Technique ◽  
Data Driven ◽  
Optimal Decision ◽  
Distributionally Robust Optimization ◽  
Worst Case ◽  
Leibler Divergence ◽  
Out Of Sample ◽  
Distributionally Robust

We consider the data-driven stochastic programming problem with binary entries where the probability of existence of each entry is not known, instead realization of data is provided. We applied the distributionally robust optimization technique to minimize the worst-case expected cost taken over the ambiguity set based on the Kullback-Leibler divergence. We investigate the out-of-sample performance of the resulting optimal decision and analyze its dependence on the sparsity of the problem.

scholarly journals Spanning Tests for Assets with Option-Like Payoffs: The Case of Hedge Funds

2020 ◽  
Vol 66 (12) ◽  
pp. 5969-5989 ◽  
Author(s):  
Paul Karehnke ◽  
Frans de Roon
Keyword(s):  
Performance Evaluation ◽  
Mutual Funds ◽  
Hedge Funds ◽  
Factor Models ◽  
Significant Fraction ◽  
Risk Averse ◽  
Economic Significance ◽  
Out Of Sample ◽  
Mean Variance ◽  
Standard Tests

We draw on the skewness literature to propose regression-based performance evaluation tests designed for investments with option-like returns. These tests deliver conclusions valid for all risk-averse mean-variance-skewness investors and can better account for nonlinearities in returns than option-based factor models. Applied to mutual and hedge funds, our tests usually suggest selecting different funds than standard tests and find that a significant fraction (11%) of hedge funds adds value to investors, whereas this is an insignificant 4% for mutual funds. We also analyze the economic significance of these option-like returns and their out-of-sample persistence. This paper was accepted by Tyler Shumway, finance.

Fine Particle Mass Monitoring with Low-Cost Sensors: Corrections and Long-Term Performance Evaluation

2019 ◽  
Author(s):  
Carl Malings ◽  
Rebecca Tanzer ◽  
Aliaksei Hauryliuk ◽  
Provat K. Saha ◽  
Allen L. Robinson ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Fine Particle ◽  
Low Cost ◽  
Particle Mass ◽  
Long Term Performance ◽  
Term Performance
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