On portfolio optimization

2016 ◽  
Vol 17 (3) ◽  
pp. 295-309 ◽  
Author(s):  
Theo Berger ◽  
Christian Fieberg
Keyword(s):  
Minimum Variance ◽  
Factor Models ◽  
Scale Factor ◽  
Covariance Matrices ◽  
Portfolio Performance ◽  
Content Type ◽  
Portfolio Decisions ◽  
Multi Scale ◽  
Out Of Sample ◽  
Factor Returns

Purpose The purpose of this paper is to show how investors can incorporate the multi-scale nature of asset and factor returns into their portfolio decisions and to evaluate the out-of-sample performance of such strategies. Design/methodology/approach The authors decompose daily return series of common risk factors and of all stocks listed in the Dow Jones Industrial Index (DJI) from 2000 to 2015 into different time scales to separate short-term noise from long-run trends. Then, the authors apply various (multi-scale) factor models to determine variance-covariance matrices which are used for minimum variance portfolio selection. Finally, the portfolios are evaluated by their out-of-sample performance. Findings The authors find that portfolios which are constructed on variance-covariance matrices stemming from multi-scale factor models outperform portfolio allocations which do not take the multi-scale nature of asset and factor returns into account. Practical implications The results of this paper provide evidence that accounting for the multi-scale nature of return distributions in portfolio decisions might be a promising approach from a portfolio performance perspective. Originality/value The authors demonstrate how investors can incorporate the multi-scale nature of returns into their portfolio decisions by applying wavelet filter techniques.

A comparison of minimum variance and maximum Sharpe ratio portfolios for mainstream investors

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anja Vinzelberg ◽  
Benjamin Rainer Auer
Keyword(s):  
Minimum Variance ◽  
Sharpe Ratio ◽  
Optimization Techniques ◽  
Estimation Methods ◽  
Content Type ◽  
Single Index ◽  
Index Model ◽  
Single Index Model ◽  
Out Of Sample ◽  
Asset Classes

PurposeMotivated by the recent theoretical rehabilitation of mean-variance analysis, the authors revisit the question of whether minimum variance (MinVar) or maximum Sharpe ratio (MaxSR) investment weights are preferable in practical portfolio formation.Design/methodology/approachThe authors answer this question with a focus on mainstream investors which can be modeled by a preference for simple portfolio optimization techniques, a tendency to cling to past asset characteristics and a strong interest in index products. Specifically, in a rolling-window approach, the study compares the out-of-sample performance of MinVar and MaxSR portfolios in two asset universes covering multiple asset classes (via investable indices and their subindices) and for two popular input estimation methods (full covariance and single-index model).FindingsThe authors find that, regardless of the setting, there is no statistically significant difference between MinVar and MaxSR portfolio performance. Thus, the choice of approach does not matter for mainstream investors. In addition, the analysis reveals that, contrary to previous research, using a single-index model does not necessarily improve out-of-sample Sharpe ratios.Originality/valueThe study is the first to provide an in-depth comparison of MinVar and MaxSR returns which considers (1) multiple asset classes, (2) a single-index model and (3) state-of-the-art bootstrap performance tests.

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 Z-score vs minimum variance preselection methods for constructing small portfolios

2020 ◽  
Vol 17 (1) ◽  
pp. 64-76
Author(s):  
Francesco Cesarone ◽  
Fabiomassimo Mango ◽  
Gabriele Sabato
Keyword(s):  
Risk Reduction ◽  
Real World ◽  
Minimum Variance ◽  
Portfolio Performance ◽  
Z Score ◽  
Real World Data ◽  
World Data ◽  
Preliminary Results ◽  
Out Of Sample ◽  
Variance Approach

Several contributions in the literature argue that a significant in-sample risk reduction can be obtained by investing in a relatively small number of assets in an investment universe. Furthermore, selecting small portfolios seems to yield good out-of-sample performances in practice. This analysis provides further evidence that an appropriate preselection of the assets in a market can lead to an improvement in portfolio performance. For preselection, this paper investigates the effectiveness of a minimum variance approach and that of an innovative index (the new Altman Z-score) based on the creditworthiness of the companies. Different classes of portfolio models are examined on real-world data by applying both the minimum variance and the Z-score preselection methods. Preliminary results indicate that the new Altman Z-score preselection provides encouraging out-of-sample performances with respect to those obtained with the minimum variance approach.

scholarly journals Dynamic risk-based optimization on cryptocurrencies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bayu Adi Nugroho
Keyword(s):  
Transaction Cost ◽  
Dynamic Optimization ◽  
Design Methodology ◽  
Minimum Variance ◽  
Multivariate Garch ◽  
Optimization Strategy ◽  
Content Type ◽  
Out Of Sample ◽  
Rolling Windows ◽  
Practical Implications

PurposeIt is crucial to find a better portfolio optimization strategy, considering the cryptocurrencies' asymmetric volatilities. Hence, this research aimed to present dynamic optimization on minimum variance (MVP), equal risk contribution (ERC) and most diversified portfolio (MDP).Design/methodology/approachThis study applied dynamic covariances from multivariate GARCH(1,1) with Student’s-t-distribution. This research also constructed static optimization from the conventional MVP, ERC and MDP as comparison. Moreover, the optimization involved transaction cost and out-of-sample analysis from the rolling windows method. The sample consisted of ten significant cryptocurrencies.FindingsDynamic optimization enhanced risk-adjusted return. Moreover, dynamic MDP and ERC could win the naïve strategy (1/N) under various estimation windows, and forecast lengths when the transaction cost ranging from 10 bps to 50 bps. The researcher also used another researcher's sample as a robustness test. Findings showed that dynamic optimization (MDP and ERC) outperformed the benchmark.Practical implicationsSophisticated investors may use the dynamic ERC and MDP to optimize cryptocurrencies portfolio.Originality/valueTo the best of the author’s knowledge, this is the first paper that studies the dynamic optimization on MVP, ERC and MDP using DCC and ADCC-GARCH with multivariate-t-distribution and rolling windows method.

Portfolio allocation across variance risk premia

2019 ◽  
Vol 20 (5) ◽  
pp. 556-593
Author(s):  
Julien Chevallier ◽  
Dinh-Tri Vo
Keyword(s):  
Risk Premium ◽  
Regime Switching ◽  
Realized Volatility ◽  
Risk Premia ◽  
Covariance Matrices ◽  
Management Style ◽  
Portfolio Performance ◽  
Content Type ◽  
Variance Risk Premium ◽  
Variance Risk

Purpose In asset management, what if clients want to purchase protection from risk factors, under the form of variance risk premia. This paper aims to address this topic by developing a portfolio optimization framework based on the criterion of the minimum variance risk premium (VRP) for any investor selecting stocks with an expected target return while minimizing the risk aversion associated to the portfolio according to “good” and “bad” times. Design/methodology/approach To accomplish this portfolio selection problem, the authors compute variance risk-premium as the difference from high-frequencies' realized volatility and options' implied volatility stemming from 19 stock markets, estimate a 2-state Markov-switching model on the variance risk-premia and optimize variance risk-premia portfolios across non-overlapping regions. The period goes from March 16, 2011, to March 28, 2018. Findings The authors find that optimized portfolios based on variance-covariance matrices stemming from VRP do not consistently outperform the benchmark based on daily returns. Several robustness checks are investigated by minimizing historical, realized or implicit variances, with/without regime switching. In a boundary case, accounting for the realized variance risk factor in portfolio decisions can be seen as a promising alternative from a portfolio performance perspective. Practical implications As a new management “style”, the realized volatility approach can, therefore, bring incremental value to construct the conditional covariance matrix estimates. Originality/value The authors assess the portfolio performance determined by the variance-covariance matrices that are derived by four models: “naive” (Markowitz returns benchmark), non-switching VRP, maximum likelihood regime-switching VRP and Bayesian regime switching VRP. The authors examine the best return-risk combination through the calculation of the Sharpe ratio. They also assess another different portfolio strategy: the risk parity approach.

scholarly journals Regularized Maximum Diversification Investment Strategy

Econometrics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
N’Golo Koné
Keyword(s):  
Covariance Matrix ◽  
Estimation Error ◽  
Investment Strategy ◽  
Minimum Variance ◽  
Sharpe Ratio ◽  
Tuning Parameter ◽  
Portfolio Performance ◽  
Asset Return ◽  
Out Of Sample ◽  
Global Minimum Variance Portfolio

The maximum diversification has been shown in the literature to depend on the vector of asset volatilities and the inverse of the covariance matrix of the asset return covariance matrix. In practice, these two quantities need to be replaced by their sample statistics. The estimation error associated with the use of these sample statistics may be amplified due to (near) singularity of the covariance matrix, in financial markets with many assets. This, in turn, may lead to the selection of portfolios that are far from the optimal regarding standard portfolio performance measures of the financial market. To address this problem, we investigate three regularization techniques, including the ridge, the spectral cut-off, and the Landweber–Fridman approaches in order to stabilize the inverse of the covariance matrix. These regularization schemes involve a tuning parameter that needs to be chosen. In light of this fact, we propose a data-driven method for selecting the tuning parameter. We show that the selected portfolio by regularization is asymptotically efficient with respect to the diversification ratio. In empirical and Monte Carlo experiments, the resulting regularized rules are compared to several strategies, such as the most diversified portfolio, the target portfolio, the global minimum variance portfolio, and the naive 1/N strategy in terms of in-sample and out-of-sample Sharpe ratio performance, and it is shown that our method yields significant Sharpe ratio improvements.

Large-cap vs small-cap portfolio performance: new empirical evidence from ETFs

2019 ◽  
Vol 18 (1) ◽  
pp. 71-94
Author(s):  
Gerasimos Rompotis
Keyword(s):  
Design Methodology ◽  
Portfolio Performance ◽  
Exchange Traded Funds ◽  
Investment Strategies ◽  
Time Data ◽  
Content Type ◽  
Risk Characteristics ◽  
The Usa ◽  
First Time ◽  
Practical Implications

PurposeA well-documented pattern in the literature concerns the outperformance of small-cap stocks relative to their larger-cap counterparts. This paper aims to address the “small-cap versus large-cap” issue using for the first time data from the exchange traded funds (ETFs) industry.Design/methodology/approachSeveral raw return and risk-adjusted return metrics are estimated over the period 2012-2016.FindingsResults are partially supportive of the “size effect”. In particular, small-cap ETFs outperform large-cap ETFs in overall raw return terms even though they fail the risk test. However, outperformance is not consistent on an annual basis. When risk-adjusted returns are taken into consideration, small-cap ETFs are inferior to their large-cap counterparts.Research limitations/implicationsThis research only covers the ETF market in the USA. However, given the tremendous growth of ETF markets worldwide, a similar examination of the “small vs large capitalization” issue could be conducted with data from other developed ETF markets in Europe and Asia. In such a case, useful comparisons could be made, so that we could conclude whether the findings of the current study are unique and US-specific or whether they could be generalized across the several international ETF markets.Practical implicationsA possible generalization of the findings would entail that profitable investment strategies could be based on the different performance and risk characteristics of small- and large-cap ETFs.Originality/valueThis is the first study to examine the performance of ETFs investing in large-cap stock indicesvis-à-visthe performance of ETFs tracking indices comprised of small-cap stocks.

Customizing persuasive messages; the value of operative measures

2018 ◽  
Vol 35 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Maurits Kaptein
Keyword(s):  
Interactive Media ◽  
Database Systems ◽  
Customer Behavior ◽  
Customer Relationship ◽  
Persuasive Messages ◽  
Psychological Traits ◽  
Content Type ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Operative Measures

Purpose This paper aims to examine whether estimates of psychological traits obtained using meta-judgmental measures (as commonly present in customer relationship management database systems) or operative measures are most useful in predicting customer behavior. Design/methodology/approach Using an online experiment (N = 283), the study collects meta-judgmental and operative measures of customers. Subsequently, it compares the out-of-sample prediction error of responses to persuasive messages. Findings The study shows that operative measures – derived directly from measures of customer behavior – are more informative than meta-judgmental measures. Practical implications Using interactive media, it is possible to actively elicit operative measures. This study shows that practitioners seeking to customize their marketing communication should focus on obtaining such psychographic observations. Originality/value While currently both meta-judgmental measures and operative measures are used for customization in interactive marketing, this study directly compares their utility for the prediction of future responses to persuasive messages.

scholarly journals Dynamic factor models: Does the specification matter?

SERIEs ◽  
2021 ◽  
Author(s):  
Karen Miranda ◽  
Pilar Poncela ◽  
Esther Ruiz
Keyword(s):  
Factor Models ◽  
Model Specification ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Number Of Factors ◽  
Time Varying Parameters ◽  
Out Of Sample ◽  
Mixed Frequency Data ◽  
Natural Way ◽  
Factor Estimation

AbstractDynamic factor models (DFMs), which assume the existence of a small number of unobserved underlying factors common to a large number of variables, are very popular among empirical macroeconomists. Factors can be extracted using either nonparametric principal components or parametric Kalman filter and smoothing procedures, with the former being computationally simpler and robust against misspecification and the latter coping in a natural way with missing and mixed-frequency data, time-varying parameters, nonlinearities and non-stationarity, among many other stylized facts often observed in real systems of economic variables. This paper analyses the empirical consequences on factor estimation, in-sample predictions and out-of-sample forecasting of using alternative estimators of the DFM under various sources of potential misspecification. In particular, we consider factor extraction when assuming different number of factors and different factor dynamics. The factors are extracted from a popular data base of US macroeconomic variables, widely analyzed in the literature without consensus about the most appropriate model specification. We show that this lack of consensus is only marginally crucial when it comes to factor extraction, but it matters when the objective is out-of-sample forecasting.

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