Risk Parity, Maximum Diversification, and Minimum Variance: An Analytic Perspective

2012 ◽  
Author(s):  
Roger Clarke ◽  
Harindra de Silva ◽  
Steven Thorley
Keyword(s):  
Minimum Variance ◽  
Risk Parity

Risk Parity, Maximum Diversification,and Minimum Variance: An Analytic Perspective

2013 ◽  
Vol 39 (3) ◽  
pp. 39-53 ◽  
Author(s):  
Roger Clarke ◽  
Harindra de Silva ◽  
Steven Thorley
Keyword(s):  
Minimum Variance ◽  
Risk Parity

scholarly journals Factor investing and asset allocation strategies: a comparison of factor versus sector optimization

2021 ◽  
Author(s):  
Wolfgang Bessler ◽  
Georgi Taushanov ◽  
Dominik Wolff
Keyword(s):  
Asset Allocation ◽  
Factor Versus ◽  
Minimum Variance ◽  
Superior Performance ◽  
Relative Advantage ◽  
Allocation Model ◽  
Risk Parity ◽  
And Performance ◽  
Mean Variance ◽  
Allocation Strategies

AbstractGiven the tremendous growth of factor allocation strategies in active and passive fund management, we investigate whether factor or sector asset allocation strategies provide investors with a superior performance. Our focus is on comparing factor versus sector allocations as some recent empirical evidence indicates the dominance of sector over country portfolios. We analyze the performance and performance differences of sector and factor portfolios for various weighting and portfolio optimization approaches, including “equal-weighting” (1/N), “risk parity,” minimum-variance, mean-variance, Bayes–Stein and Black–Litterman. We employ a sample-based approach in which the sample moments are the input parameters for the allocation model. For the period from May 2007 to November 2020, our results clearly reveal that, over longer investment horizons, factor portfolios provide relative superior performances. For shorter periods, however, we observe time-varying and alternating performance dominances as the relative advantage of one over the other strategy depends on the economic cycle. One important insight is that during “normal” times factor portfolios clearly dominate sector portfolios, whereas during crisis periods sector portfolios are superior offering better diversification opportunities.

scholarly journals Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification

Risks ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 74 ◽  
Author(s):  
Prayut Jain ◽  
Shashi Jain
Keyword(s):  
Machine Learning ◽  
Predictive Ability ◽  
Forecast Model ◽  
Minimum Variance ◽  
Portfolio Performance ◽  
Risk Parity ◽  
Out Of Sample ◽  
Weighted Portfolio ◽  
The Impact ◽  
Allocation Methods

The Hierarchical risk parity (HRP) approach of portfolio allocation, introduced by Lopez de Prado (2016), applies graph theory and machine learning to build a diversified portfolio. Like the traditional risk-based allocation methods, HRP is also a function of the estimate of the covariance matrix, however, it does not require its invertibility. In this paper, we first study the impact of covariance misspecification on the performance of the different allocation methods. Next, we study under an appropriate covariance forecast model whether the machine learning based HRP outperforms the traditional risk-based portfolios. For our analysis, we use the test for superior predictive ability on out-of-sample portfolio performance, to determine whether the observed excess performance is significant or if it occurred by chance. We find that when the covariance estimates are crude, inverse volatility weighted portfolios are more robust, followed by the machine learning-based portfolios. Minimum variance and maximum diversification are most sensitive to covariance misspecification. HRP follows the middle ground; it is less sensitive to covariance misspecification when compared with minimum variance or maximum diversification portfolio, while it is not as robust as the inverse volatility weighed portfolio. We also study the impact of the different rebalancing horizon and how the portfolios compare against a market-capitalization weighted portfolio.

scholarly journals Can sector-specific REIT strategies outperform a diversified benchmark?

2017 ◽  
Vol 10 (3) ◽  
pp. 366-383 ◽  
Author(s):  
Alex Moss ◽  
Andrew Clare ◽  
Stephen Thomas ◽  
James Seaton
Keyword(s):  
Minimum Variance ◽  
Equal Weight ◽  
Content Type ◽  
Fund Managers ◽  
Property Type ◽  
Risk Parity ◽  
Efficient Portfolios ◽  
Market Capitalisation ◽  
Subsequent Addition ◽  
Practical Implications

Purpose The authors in this paper aim to investigate the performance of different portfolios of REITs which specialise by property type compared to the performance of a diversified free-float market capitalisation-weighted benchmark index to determine whether superior risk-adjusted returns can be achieved. Design/methodology/approach First, the authors examine the performance of portfolios constructed using the criteria of equal weight, minimum variance, maximum Sharpe and risk parity rather than free-float market capitalisation. Second, the authors apply an automated trading strategy of trend following to see if this filter will improve risk-adjusted returns. Findings The two-step process of forming combinations of REIT sectors with the subsequent addition of a trend following overlay can offer clear benefits relative to a passive benchmark investment. Research limitations/implications Although three of the four strategies were shown to outperform the benchmark index on a risk-adjusted basis, one issue was that the efficient portfolios tended to have large weightings to relatively few sectors. The authors also found that maximum drawdowns (losses) of the strategies tended to be rather high, as was the benchmark. Practical implications The methods outlined in this paper can be applied to construct superior risk-adjusted REIT portfolios globally. Originality/value Although studies have been undertaken separately on REIT specialisation and trend following in equity and commodity markets, this paper is the first to combine the two topics, and therefore has particular value for real estate fund managers globally.

Properties of the beta coefficient of the global minimum variance portfolio

2020 ◽  
Vol 8 (1) ◽  
pp. 11-21
Author(s):  
S. M. Yaroshko ◽  
◽  
M. V. Zabolotskyy ◽  
T. M. Zabolotskyy ◽  
◽  
...  
Keyword(s):  
Confidence Interval ◽  
Asymptotic Distribution ◽  
Global Minimum ◽  
Asset Returns ◽  
Minimum Variance ◽  
Benchmark Portfolio ◽  
Beta Coefficient ◽  
Minimum Variance Portfolio ◽  
Global Minimum Variance Portfolio ◽  
Daily Returns

The paper is devoted to the investigation of statistical properties of the sample estimator of the beta coefficient in the case when the weights of benchmark portfolio are constant and for the target portfolio, the global minimum variance portfolio is taken. We provide the asymptotic distribution of the sample estimator of the beta coefficient assuming that the asset returns are multivariate normally distributed. Based on the asymptotic distribution we construct the confidence interval for the beta coefficient. We use the daily returns on the assets included in the DAX index for the period from 01.01.2018 to 30.09.2019 to compare empirical and asymptotic means, variances and densities of the standardized estimator for the beta coefficient. We obtain that the bias of the sample estimator converges to zero very slowly for a large number of assets in the portfolio. We present the adjusted estimator of the beta coefficient for which convergence of the empirical variances to the asymptotic ones is not significantly slower than for a sample estimator but the bias of the adjusted estimator is significantly smaller.

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