Valuation, Downside Risk Measures and Asymmetrical Information: A Portfolio Optimization Approach

2010 ◽  
Author(s):  
Yoram Kroll
Keyword(s):  
Portfolio Optimization ◽  
Risk Measures ◽  
Downside Risk ◽  
Optimization Approach ◽  
Asymmetrical Information

scholarly journals Mean-Variance Optimization Is a Good Choice, But for Other Reasons than You Might Think

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 29 ◽  
Author(s):  
Andrea Rigamonti
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Risk Measures ◽  
Traditional Approach ◽  
Real Data ◽  
Downside Risk ◽  
Good Choice ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Allocation Decisions

Mean-variance portfolio optimization is more popular than optimization procedures that employ downside risk measures such as the semivariance, despite the latter being more in line with the preferences of a rational investor. We describe strengths and weaknesses of semivariance and how to minimize it for asset allocation decisions. We then apply this approach to a variety of simulated and real data and show that the traditional approach based on the variance generally outperforms it. The results hold even if the CVaR is used, because all downside risk measures are difficult to estimate. The popularity of variance as a measure of risk appears therefore to be rationally justified.

Fuzzy portfolio optimization under downside risk measures

2007 ◽  
Vol 158 (7) ◽  
pp. 769-782 ◽  
Author(s):  
Enriqueta Vercher ◽  
José D. Bermúdez ◽  
José Vicente Segura
Keyword(s):  
Portfolio Optimization ◽  
Risk Measures ◽  
Downside Risk

Utilizing Downside Risk Measures

2014 ◽  
Vol 31 (3) ◽  
pp. 42-50 ◽  
Author(s):  
Michelle McCarthy
Keyword(s):  
Risk Measures ◽  
Downside Risk

scholarly journals Portfolio Optimization Constrained by Performance Attribution

2021 ◽  
Vol 14 (5) ◽  
pp. 201
Author(s):  
Yuan Hu ◽  
W. Brent Lindquist ◽  
Svetlozar T. Rachev
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Selection Effect ◽  
Conditional Value At Risk ◽  
Positive Role ◽  
Performance Attribution ◽  
Dow Jones Industrial Average

This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the selection effect (SE), as constraints on asset weights. The test portfolio consists of stocks from the Dow Jones Industrial Average index. Values for the performance attributes are established relative to two benchmarks, equi-weighted and price-weighted portfolios of the same stocks. Performance of the optimized portfolios is judged using comparisons of cumulative price and the risk-measures: maximum drawdown, Sharpe ratio, Sortino–Satchell ratio and Rachev ratio. The results suggest that achieving SE performance thresholds requires larger turnover values than that required for achieving comparable AA thresholds. The results also suggest a positive role in price and risk-measure performance for the imposition of constraints on AA and SE.

Portfolio Optimization Using Novel Intelligent Probabilistic Forecasts of Risk Measures

2021 ◽  
Author(s):  
You Liang ◽  
Aerambamoorthy Thavaneswaran ◽  
Alexander Paseka ◽  
Ruppa K. Thulasiram ◽  
Ethan Johnson-Skinner
Keyword(s):  
Portfolio Optimization ◽  
Risk Measures ◽  
Probabilistic Forecasts

scholarly journals CHARACTERISTICS OF OMEGA-OPTIMIZED PORTFOLIOS AT DIFFERENT LEVELS OF THRESHOLD RETURNS

2014 ◽  
Vol 12 (2) ◽  
pp. 245-265 ◽  
Author(s):  
Renaldas Vilkancas
Keyword(s):  
Portfolio Optimization ◽  
Risk Measures ◽  
High Threshold ◽  
Portfolio Performance ◽  
Threshold Values ◽  
Portfolio Returns ◽  
Upside Risk ◽  
And Performance ◽  
Risk Free Rate ◽  
Different Levels

There is little literature considering effects that the loss-gain threshold used for dividing good and bad outcomes by all downside (upside) risk measures has on portfolio optimization and performance. The purpose of this study is to assess the performance of portfolios optimized with respect to the Omega function developed by Keating and Shadwick at different levels of the threshold returns. The most common choices of the threshold values used in various Omega studies cover the risk-free rate and the average market return or simply a zero return, even though the inventors of this measure for risk warn that “using the values of the Omega function at particular points can be critically misleading” and that “only the entire Omega function contains information on distribution”. The obtained results demonstrate the importance of the selected values of the threshold return on portfolio performance – higher levels of the threshold lead to an increase in portfolio returns, albeit at the expense of a higher risk. In fact, within a certain threshold interval, Omega-optimized portfolios achieved the highest net return, compared with all other strategies for portfolio optimization using three different test datasets. However, beyond a certain limit, high threshold values will actually start hurting portfolio performance while meta-heuristic optimizers typically are able to produce a solution at any level of the threshold, and the obtained results would most likely be financially meaningless.

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