Portfolio selection with independent component analysis

Hitaj, Asmerilda; Mercuri, Lorenzo; Rroji, Edit

doi:10.1016/j.frl.2015.09.005

Cited by 18 publications

(9 citation statements)

References 18 publications

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“…However, since its introduction, it has drawn a lot of criticism that its assumptions do not match the real financial markets. Over the years, a lot of alternative and more refined models have been proposed (see, e.g., [7,8,9,19,20] and references therein).…”

Section: Introductionmentioning

confidence: 99%

Mixed-stable approach to the management of the portfolio using high-frequency financial data

Belovas

Sakalauskas²,

Starikovičius³

2017

ITC

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This paper considers the problem of portfolio selection using high-frequency financial time series. Such time series often exhibit the stagnation effect when the assets' returns are not changing. This effect causes a lot of unusual difficulties in the analysis and modelling of such series. In classical statistics, when the distributional law has two first moments, i.e. mean and variance, the relationship between the two random variables is described by the covariance or correlation. However, if the financial data follow the stable law, and empirical studies often support this assumption, covariance and especially correlation often cannot be calculated. In this work, alternative relation measures are applied to deal with the portfolio selection problem using the mixed-stable modelling. The modelling is applied to the high-frequency financial time series obtained from the German DAX index intra-daily data. The performance of the mixed-stable model is compared with alternative approaches. The portfolio selection problem is formulated as the optimization problem, with covariances replaced by the generalized power-correlations. The results of the portfolio selection strategy without the relationship coefficients matrix are also presented.

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Section: Introductionmentioning

confidence: 99%

Mixed-stable approach to the management of the portfolio using high-frequency financial data

Belovas

Sakalauskas²,

Starikovičius³

2017

ITC

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“…Starting from Back and Weigend (1998), several authors have applied the ICA in forecasting financial time series for example in Lu et al (2009) or for portfolio selection (see Madan and Yen 2004;Madan 2006). In Hitaj et al (2015) some parametric and nonparametric distributions are considered for the independent components in a portfolio optimization problem for a Constant Absolute Risk Aversion (CARA) utility function. The ICA algorithm has been also employed for the construction of multivariate GARCH( p, q) processes, called ICA-GARCH( p, q) (see Broda et al 2013, for instance) and in portfolio optimization (see Chen et al 2007, for optimal portfolios where the risk measure in the objective function is the Value at Risk).…”

Section: Introductionmentioning

confidence: 99%

“…In Hitaj et al. ( 2015 ) some parametric and nonparametric distributions are considered for the independent components in a portfolio optimization problem for a Constant Absolute Risk Aversion (CARA) utility function.…”

Section: Introductionmentioning

confidence: 99%

Portfolio Selection with Irregular Time Grids: an example using an ICA-COGARCH(1, 1) approach

Bianchi

Mercuri

Rroji

2021

Financ Mark Portf Manag

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In this paper we consider a portfolio selection problem defined for irregularly spaced observations. We use the Independent Component Analysis for the identification of the dependence structure and continuous-time GARCH models for the marginals. We discuss both estimation and simulation of market prices in a context where the time grid of price quotations differs across assets. We present an empirical analysis of the proposed approach using two high-frequency datasets that provides better out-of-sample results than competing portfolio strategies except for the case of severe market conditions with frequent rebalancements.

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“…Moreover, both papers do not apply dimension reduction (K = N ). Hitaj et al (2015) rely on ICA to estimate the maximum-CRRA-utility portfolio. Again, their focus is on the density estimation of the ICs via several parametric distributions.…”

Section: Introductionmentioning

confidence: 99%

Robust Portfolio Selection Using Sparse Estimation of Comoment Tensors

Lassance

Vrins²

2019

SSRN Journal

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It is well known that estimation issues severely impact the performances of investment strategies. This becomes even more problematic when accounting for higher moments as the number of parameters to be estimated quickly explodes with the number of assets. In this paper, we address this issue by relying on specific factor models. Although useful to reduce the dimension of the problem, principal component analysis (PCA) is only a partial solution. In particular, it does not break the exponential law that links the number of parameters to the moment order. This issue is tackled by using a new robust portfolio-selection technique that relies on independent component analysis (ICA). By linearly projecting the asset returns onto a small set of maximally independent factors, we obtain a sparse approximation of the comoment tensors of asset returns. This drastically decreases the dimensionality of the problem and, as expected, leads to well-performing, robust and low-turnover investment strategies.

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Portfolio selection with independent component analysis

Cited by 18 publications

References 18 publications

Mixed-stable approach to the management of the portfolio using high-frequency financial data

Mixed-stable approach to the management of the portfolio using high-frequency financial data

Portfolio Selection with Irregular Time Grids: an example using an ICA-COGARCH(1, 1) approach

Robust Portfolio Selection Using Sparse Estimation of Comoment Tensors

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