On the use of conditional expectation in portfolio selection problems

Ortobelli, Sergio; Kouaissah, Noureddine; Tichý, Tomáš

doi:10.1007/s10479-018-2890-3

Cited by 11 publications

(12 citation statements)

References 42 publications

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“…In practical applications, we estimate and assess the returns of a given portfolio based on multifactor models (Ortobelli, Kouaissah, & Tichy, 2017;Pelger, 2019;Ross, 1978). Multifactor models have been widely used both theoretically and empirically in economics and finance (Fan, Fan, & Lv, 2008;Ortobelli et al, 2019;Ross, 1978). Derived by Ross (1976) proposing the arbitrage pricing theory (APT) and by Chamberlain and Rothschild (1983) in a large economy; the multifactor models state that the expected returns are typically approximated by multiple linear regression models.…”

Section: Technical Trading Rules and Systemic Risk Alarmsmentioning

confidence: 99%

“…In this respect, some of the most widely used economic theories both theoretically and empirically are based on factor models (e.g., the ATP of Ross, 1976). The multifactor models have been widely employed and examined in finance and portfolio theory, including by Ross (1976), Fama and French (1993), Bai (2003), Ait-Sahalia and Xiu (2017), Bollerslev, Meddahi, and Nyawa (2019), Ortobelli et al (2019), and the references therein. When the factors are latent, principal component analysis (PCA) obviously becomes the key tool to the decision maker (Ait-Sahalia & Xiu, 2017;Jolliffe, 2002).…”

Section: Multifactor Models and Optimal Portfolio Selection In A Reward-risk Frameworkmentioning

confidence: 99%

“…Many classical financial models (e.g., the APT of Ross, 1976, and the capital asset pricing model (CAPM)) have been justified by assuming elliptical distributions of financial returns (Owen & Rabinovitch, 1983). These models solve the large-scale portfolio problem by using a linear factorial model that depends on certain uncorrelated factors (Ortobelli et al, 2019;Ross, 1978). Significant efforts have been devoted to determining the number of factors by using statistical methodologies or relying on random matrix theory (e.g., Kapetanios, 2010;Onatski, 2010;Trapani, 2018).…”

Section: Multifactor Models and Optimal Portfolio Selection In A Reward-risk Frameworkmentioning

confidence: 99%

“…The literature of multifactor models is well established and commonly applied in finance and that several related problems have been resolved (e.g., Ait-Sahalia & Xiu, 2017;Ortobelli et al, 2019;Pelger, 2019). Importantly, the suggested approach is also applicable not only to the multifactor model (Equation 11) but also to the other models that help to approximate the expected returns (e.g., simulation methods or the threefactor model of Fama & French, 1993, and its extensions).…”

Section: Multifactor Models and Optimal Portfolio Selection In A Reward-risk Frameworkmentioning

confidence: 99%

“…In practical applications, we integrate the alarm rules into portfolio selection strategies that predict returns using multifactor models (e.g., the k-fund separation model of Ross, 1978). Technical indicators and multifactor models provide complementary information to decision makers, enabling better portfolio choice management; alarm rules based on technical indicators more accurately detect the most typical market declines, whereas multifactor models are used to estimate the expected returns for optimal asset allocation (Ortobelli, Kouaissah, & Tichy, 2019). Empirically, we assess the usefulness of technical trading rules for forecasting systemic risk by comparing the wealth obtained from different portfolio strategies.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting systemic risk in portfolio selection: The role of technical trading rules

Kouaissah

Hocine

2020

Journal of Forecasting

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This paper proposes and implements methods for determining whether incorporating technical trading rules accurately forecasts systemic risk and improves the performance of out-of-sample portfolios. The proposed methodology considers various trading rules for forecasting and addressing potential systemic risk in portfolio selection problems. The method incorporates major trading rules as early warning systems or alarm rules to detect market failure within diverse reward-risk measures. Methodologically, the alarm rules are integrated into portfolio selection strategies that predict returns using multifactor models. Therefore, the portfolio strategies combine the predictive ability of both technical trading rules and multifactor models. Empirical analyses validate the suggested approaches and evaluate the impacts of different technical trading rules on portfolio selection problems. This paper compares the ex ante sample paths of several portfolio strategies aiming to maximize portfolio wealth using either reward-risk or drawdown-based performance measures. The results show that the proposed methodologies outperform the classic approach in terms of out-of-sample performance.

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Section: Technical Trading Rules and Systemic Risk Alarmsmentioning

confidence: 99%

Section: Multifactor Models and Optimal Portfolio Selection In A Reward-risk Frameworkmentioning

confidence: 99%

Section: Multifactor Models and Optimal Portfolio Selection In A Reward-risk Frameworkmentioning

confidence: 99%

Section: Multifactor Models and Optimal Portfolio Selection In A Reward-risk Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecasting systemic risk in portfolio selection: The role of technical trading rules

Kouaissah

Hocine

2020

Journal of Forecasting

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Portfolio Selection Using Multivariate Semiparametric Estimators and a Copula PCA-Based Approach

2021

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Dynamic Return Scenario Generation Approach for Large-Scale Portfolio Optimisation Framework

Neděla,

Ortobelli Lozza,

Tichý

2024

Comput Econ

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In this paper, we propose a complex return scenario generation process that can be incorporated into portfolio selection problems. In particular, we assume that returns follow the ARMA–GARCH model with stable-distributed and skewed t-copula dependent residuals. Since the portfolio selection problem is large-scale, we apply the multifactor model with a parametric regression and a nonparametric regression approaches to reduce the complexity of the problem. To do this, the recently proposed trend-dependent correlation matrix is used to obtain the main factors of the asset dependency structure by applying principal component analysis (PCA). However, when a few main factors are assumed, the obtained residuals of the returns still explain a non-negligible part of the portfolio variability. Therefore, we propose the application of a novel approach involving a second PCA to the Pearson correlation to obtain additional factors of residual components leading to the refinement of the final prediction. Future return scenarios are predicted using Monte Carlo simulations. Finally, the impact of the proposed approaches on the portfolio selection problem is evaluated in an empirical analysis of the application of a classical mean–variance model to a dynamic dataset of stock returns from the US market. The results show that the proposed scenario generation approach with nonparametric regression outperforms the traditional approach for out-of-sample portfolios.

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On the use of conditional expectation in portfolio selection problems

Cited by 11 publications

References 42 publications

Forecasting systemic risk in portfolio selection: The role of technical trading rules

Forecasting systemic risk in portfolio selection: The role of technical trading rules

Portfolio Selection Using Multivariate Semiparametric Estimators and a Copula PCA-Based Approach

Dynamic Return Scenario Generation Approach for Large-Scale Portfolio Optimisation Framework

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