scholarly journals Evaluation of multi-asset investment strategies with digital assets

2021 ◽  
Author(s):  
Alla Petukhina ◽  
Erin Sprünken
Keyword(s):  
Strong Dependence ◽  
Financial Econometrics ◽  
Geometric Optimization ◽  
Asset Markets ◽  
Investment Strategies ◽  
Benchmark Portfolio ◽  
Measurement Tools ◽  
Out Of Sample ◽  
Mean Variance ◽  
Digital Assets

AbstractThe drastic growth of the cryptocurrencies market capitalization boosts investigation of their diversification benefits in portfolio construction. In this paper with a set of classical and modern measurement tools, we assess the out-of-sample performance of eight portfolio allocation strategies relative to the naive 1/N rule applied to traditional and crypto-assets investment universe. Evaluated strategies include a range from classical Markowitz rule to the recently introduced LIBRO approach (Trimborn et al. in Journal of Financial Econometrics 1–27, 2019). Furthermore, we also compare three extensions for strategies with respect to input estimators applied. The results show that in the presence of alternative assets, such as cryptocurrencies, mean–variance strategies underperform the benchmark portfolio. In contrast, CVaR optimization tends to outperform the benchmark as well as geometric optimization, although we find a strong dependence of the former’s success on trading costs. Furthermore, we find evidence that liquidity-bounded strategies tend to perform very well. Thus, our findings underscore the non-normal distribution of returns and the necessity to control for liquidity constraints at alternative asset markets.

Do Commodities Add Economic Value in Asset Allocation? New Evidence from Time-Varying Moments

2018 ◽  
Vol 53 (1) ◽  
pp. 365-393 ◽  
Author(s):  
Xin Gao ◽  
Federico Nardari
Keyword(s):  
Asset Allocation ◽  
Economic Value ◽  
Short Selling ◽  
Investment Strategies ◽  
Time Varying ◽  
Moderate Risk ◽  
Asset Return ◽  
Out Of Sample ◽  
New Evidence ◽  
Mean Variance

We conduct a comprehensive out-of-sample assessment of the economic value adding of commodities in multiasset investment strategies that exploit the predictability of asset return moments. We find that predictability makes the inclusion of commodities profitable even when short selling and high leverage are not permitted. For instance, a mean-variance (non-mean-variance) investor with moderate risk aversion and leverage, rebalancing quarterly, would be willing to pay up to 108 (155) basis points per year after transaction cost for adding commodities to her stock, bond, and cash portfolio. Previous research had reached mixed or even opposite conclusions, especially in an out-of-sample context.

Improving Mean Variance Optimization through Sparse Hedging Restrictions

2015 ◽  
Vol 50 (6) ◽  
pp. 1415-1441 ◽  
Author(s):  
Shingo Goto ◽  
Yan Xu
Keyword(s):  
Covariance Matrix ◽  
Factor Model ◽  
Risk Minimization ◽  
Portfolio Risk ◽  
Finite Samples ◽  
Inverse Covariance Matrix ◽  
Out Of Sample ◽  
Nonnegativity Constraints ◽  
Mean Variance ◽  
Equal Weighting

AbstractIn portfolio risk minimization, the inverse covariance matrix prescribes the hedge trades in which a stock is hedged by all the other stocks in the portfolio. In practice with finite samples, however, multicollinearity makes the hedge trades too unstable and unreliable. By shrinking trade sizes and reducing the number of stocks in each hedge trade, we propose a “sparse” estimator of the inverse covariance matrix. Comparing favorably with other methods (equal weighting, shrunk covariance matrix, industry factor model, nonnegativity constraints), a portfolio formed on the proposed estimator achieves significant out-of-sample risk reduction and improves certainty equivalent returns after transaction costs.

Basic Geometric Dispersion Theory of Decision Making Under Risk: Asymmetric Risk Relativity, New Predictions of Empirical Behaviors, and Risk Triad

2021 ◽  
Author(s):  
Behnam Malakooti ◽  
Mohamed Komaki ◽  
Camelia Al-Najjar
Keyword(s):  
Risk Behaviors ◽  
Empirical Studies ◽  
Dispersion Theory ◽  
Dispersion Models ◽  
Decision Making Under Risk ◽  
First Order ◽  
Order Stochastic Dominance ◽  
Out Of Sample ◽  
Mean Variance ◽  
Special Case

Many studies have spotlighted significant applications of expected utility theory (EUT), cumulative prospect theory (CPT), and mean-variance in assessing risks. We illustrate that these models and their extensions are unable to predict risk behaviors accurately in out-of-sample empirical studies. EUT uses a nonlinear value (utility) function of consequences but is linear in probabilities, which has been criticized as its primary weakness. Although mean-variance is nonlinear in probabilities, it is symmetric, contradicts first-order stochastic dominance, and uses the same standard deviation for both risk aversion and risk proneness. In this paper, we explore a special case of geometric dispersion theory (GDT) that is simultaneously nonlinear in both consequences and probabilities. It complies with first-order stochastic dominance and is asymmetric to represent the mixed risk-averse and risk-prone behaviors of the decision makers. GDT is a triad model that uses expected value, risk-averse dispersion, and risk-prone dispersion. GDT uses only two parameters, z and zX; these constants remain the same regardless of the scale of risk problem. We compare GDT to several other risk dispersion models that are based on EUT and/or mean-variance, and identify verified risk paradoxes that contradict EUT, CPT, and mean-variance but are easily explainable by GDT. We demonstrate that GDT predicts out-of-sample empirical risk behaviors far more accurately than EUT, CPT, mean-variance, and other risk dispersion models. We also discuss the underlying assumptions, meanings, and perspectives of GDT and how it reflects risk relativity and risk triad. This paper covers basic GDT, which is a special case of general GDT of Malakooti [Malakooti (2020) Geometric dispersion theory of decision making under risk: Generalizing EUT, RDEU, & CPT with out-of-sample empirical studies. Working paper, Case Western Reserve University, Cleveland.].

On the Distribution of Terminal Wealth under Dynamic Mean-Variance Optimal Investment Strategies

2021 ◽  
Vol 12 (2) ◽  
pp. 566-603
Author(s):  
Pieter M. van Staden ◽  
Duy-Minh Dang ◽  
Peter A. Forsyth
Keyword(s):  
Optimal Investment ◽  
Investment Strategies ◽  
Terminal Wealth ◽  
Mean Variance

Mean-variance portfolio selection with an uncertain exit-time in a regime-switching market

2019 ◽  
Vol 53 (4) ◽  
pp. 1171-1186
Author(s):  
Reza Keykhaei
Keyword(s):  
Portfolio Selection ◽  
Regime Switching ◽  
Exit Time ◽  
Efficient Frontier ◽  
Optimal Investment ◽  
Homogeneous Markov Chain ◽  
Investment Strategies ◽  
Special Cases ◽  
Mean Variance Portfolio ◽  
Mean Variance

In this paper, we deal with multi-period mean-variance portfolio selection problems with an exogenous uncertain exit-time in a regime-switching market. The market is modelled by a non-homogeneous Markov chain in which the random returns of assets depend on the states of the market and investment time periods. Applying the Lagrange duality method, we derive explicit closed-form expressions for the optimal investment strategies and the efficient frontier. Also, we show that some known results in the literature can be obtained as special cases of our results. A numerical example is provided to illustrate the results.

scholarly journals Optimal Option Portfolio Strategies: Deepening the Puzzle of Index Option Mispricing

2017 ◽  
Vol 52 (1) ◽  
pp. 277-303 ◽  
Author(s):  
José Afonso Faias ◽  
Pedro Santa-Clara
Keyword(s):  
Asset Allocation ◽  
Risk Premia ◽  
Volatility Risk ◽  
Short Sample ◽  
Option Returns ◽  
Portfolio Strategies ◽  
Portfolio Strategy ◽  
Out Of Sample ◽  
Index Option ◽  
Mean Variance

Traditional methods of asset allocation (such as mean–variance optimization) are not adequate for option portfolios because the distribution of returns is non-normal and the short sample of option returns available makes it difficult to estimate their distribution. We propose a method to optimize a portfolio of European options, held to maturity, with a myopic objective function that overcomes these limitations. In an out-of-sample exercise incorporating realistic transaction costs, the portfolio strategy delivers a Sharpe ratio of 0.82 with positive skewness. This performance is mostly obtained by exploiting mispricing between options and not by loading on jump or volatility risk premia.

Humans, Econs and Portfolio Choice

2016 ◽  
Vol 07 (02) ◽  
pp. 1750001 ◽  
Author(s):  
Michael J. Best ◽  
Robert R. Grauer
Keyword(s):  
Prospect Theory ◽  
Portfolio Choice ◽  
Loss Aversion ◽  
Asset Allocation ◽  
Power Utility ◽  
Numerical Example ◽  
Portfolio Choices ◽  
Out Of Sample ◽  
Finance Theory ◽  
Mean Variance

We compare the portfolio choices of Humans — prospect theory investors — to the portfolio choices of Econs — power utility and mean-variance (MV) investors. In a numerical example, prospect theory portfolios are decidedly unreasonable. In an in-sample asset allocation setting, the prospect theory results are consistent with myopic loss aversion. However, the portfolios are extremely unstable. The power utility and MV results are consistent with traditional finance theory, where the portfolios are stable across decision horizons. In an out-of-sample asset allocation setting, the power utility and portfolios outperform the prospect theory portfolios. Nonetheless the prospect theory portfolios with loss aversion coefficients of 2.25 and 2 perform well.

scholarly journals Towards the Estimation of an Efficient Benchmark Portfolio: The Case of Croatian Emerging Market

2017 ◽  
Vol 20 (s1) ◽  
pp. 13-23 ◽  
Author(s):  
Denis Dolinar ◽  
Davor Zoričić ◽  
Antonija Kožul
Keyword(s):  
Emerging Market ◽  
Expected Returns ◽  
Trade Off ◽  
Risk Return ◽  
Market Index ◽  
Benchmark Portfolio ◽  
Out Of Sample ◽  
Shrinkage Method ◽  
Research Findings ◽  
To Come

Abstract The fact that cap-weighted indices provide an inefficient risk-return trade-off is well known today. Various research approaches evolved suggesting alternative to cap-weighting in an effort to come up with a more efficient market index benchmark. In this paper we aim to use such an approach and focus on the Croatian capital market. We apply statistical shrinkage method suggested by Ledoit and Wolf (2004) to estimate the covariance matrix and follow the work of Amenc et al. (2011) to obtain estimates of expected returns that rely on risk-return trade-off. Empirical findings for the proposed portfolio optimization include out-of-sample and robustness testing. This way we compare the performance of the capital-weighted benchmark to the alternative and ensure that consistency is achieved in different volatility environments. Research findings do not seem to support relevant research results for the developed markets but rather complement earlier research (Zoričić et al., 2014).

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