Robust estimation of efficient mean–variance frontiers

Grossi, Luigi; Laurini, Fabrizio

doi:10.1007/s11634-010-0082-3

Cited by 10 publications

(14 citation statements)

References 16 publications

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“…In each experiment, we follow Grossi and Laurini (2011) and randomly replace 30% of the values of the dependent and independent variables with values drawn from a distribution with higher variance. This procedure results in fat-tailed distributions for both the dependent and independent variables, which are consistent with returns on stocks or portfolios.…”

Section: Application To Simulated Datamentioning

confidence: 99%

Identifying outliers in asset pricing data with a new weighted forward search estimator

Aronne

Grossi

Bressan

2020

Rev. contab. finanç.

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The purpose of this work is to present the Weighted Forward Search (FSW) method for the detection of outliers in asset pricing data. This new estimator, which is based on an algorithm that downweights the most anomalous observations of the dataset, is tested using both simulated and empirical asset pricing data. The impact of outliers on the estimation of asset pricing models is assessed under different scenarios, and the results are evaluated with associated statistical tests based on this new approach. Our proposal generates an alternative procedure for robust estimation of portfolio betas, allowing for the comparison between concurrent asset pricing models. The algorithm, which is both efficient and robust to outliers, is used to provide robust estimates of the models’ parameters in a comparison with traditional econometric estimation methods usually used in the literature. In particular, the precision of the alphas is highly increased when the Forward Search (FS) method is used. We use Monte Carlo simulations, and also the well-known dataset of equity factor returns provided by Prof. Kenneth French, consisting of the 25 Fama-French portfolios on the United States of America equity market using single and three-factor models, on monthly and annual basis. Our results indicate that the marginal rejection of the Fama-French three-factor model is influenced by the presence of outliers in the portfolios, when using monthly returns. In annual data, the use of robust methods increases the rejection level of null alphas in the Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model, with more efficient estimates in the absence of outliers and consistent alphas when outliers are present.

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Section: Application To Simulated Datamentioning

confidence: 99%

Identifying outliers in asset pricing data with a new weighted forward search estimator

Aronne

Grossi

Bressan

2020

Rev. contab. finanç.

Self Cite

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“…Grossi and Laurini introduced a new method to downweight influential extreme daily returns using an outlyingness measure based on the forward search trajectories of the corresponding residuals as suggested by another work of the aforementioned authors in the framework of robust GARCH tests.…”

Section: Literature Review For Extreme Observations In Portfolio Optimentioning

confidence: 99%

“…However, the use of the variance as a risk measure has been debated and strongly questioned (see, eg, the work of Michaud). To estimate the variance all data are used, but in terms of risk measure, some of the data provide negligible information . The Markowitz approach has also been criticized, because within the set of optimal portfolios it leads to a number of combinations of assets, which could be discarded as irrelevant for a financial investor …”

Section: Introductionmentioning

confidence: 99%

Robust asset allocation with conditional value at risk using the forward search

Grossi

Laurini

2019

Appl Stoch Models Bus & Ind

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The well‐known Markowitz approach to portfolio allocation, based on expected returns and their covariance, seems to provide questionable results in financial management. One motivation for the pitfall is that financial returns have heavier than Gaussian tails, so the covariance of returns, used in the Markowitz model as a measure of portfolio risk, is likely to provide a loose quantification of the effective risk. Additionally, the Markowitz approach is very sensitive to small changes in either the expected returns or their correlation, often leading to irrelevant portfolio allocations. More recent allocation techniques are based on alternative risk measures, such as value at risk (VaR) and conditional VaR (CVaR), which are believed to be more accurate measures of risk for fat‐tailed distributions. Nevertheless, both VaR and CVaR estimates can be influenced by the presence of extreme returns. In this paper, we discuss sensitivity to the presence of extreme returns and outliers when optimizing the allocation, under the constraint of keeping CVaR to a minimum. A robust and efficient approach, based on the forward search, is suggested. A Monte Carlo simulation study shows the advantages of the proposed approach, which outperforms both robust and nonrobust alternatives under a variety of specifications. The performance of the method is also thoroughly evaluated with an application to a set of US stocks.

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“…The target of the portfolio manager is to keep the portfolio that yields lowest expected costs for electricity consumption while respecting his/her risks orientation or appetite. Finally, standard methods for optimal allocation of shares in a financial portfolio are determined by second-order conditions, which are very sensitive to outliers according to Grossi and Laurini (2011). The problem in Markowitz's mean-variance model is to minimise risk, which is defined as the variance of a portfolio for a given return.…”

Section: Introductionmentioning

confidence: 99%

Portfolio optimisation of power futures market: evidence from France and Germany

Fianu

2018

IJPP

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Understanding the nature of power futures is particularly crucial, for non-storable commodities such as wholesale electricity since it has been deregulated. This paper examines different types of optimisation techniques and provides a temporal analysis of energy future prices. In particular, it highlights how one of the well-known elements of modern finance theory could improve the accuracy of evaluating the risk exposure inherent in power futures market via a modified version of the mean-variance portfolio (MVP) theory. The optimisation techniques employed account for the initial capital requirement of the energy futures and estimate the optimal weights needed to mitigate the downside risk inherent in the energy futures market. One major finding of this paper shows that, a portfolio of energy prices with different maturities could provide market players with a less risky investment in the energy market. In addition, the feasibility of the methodologies utilised have been presented.

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Robust estimation of efficient mean–variance frontiers

Abstract: Efficient frontiers, Forward search, Multivariate outlier detection, Portfolio allocation, 62F35, 62P20,

Cited by 10 publications

References 16 publications

Identifying outliers in asset pricing data with a new weighted forward search estimator

Identifying outliers in asset pricing data with a new weighted forward search estimator

Robust asset allocation with conditional value at risk using the forward search

Portfolio optimisation of power futures market: evidence from France and Germany

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