Special Issue "Computational Methods for Risk Management in Economics and Finance"

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (30 September 2018)

Special Issue Editor

Guest Editor
Dr. Marina Resta

University of Genova, Italy
Website 1 | Website 2 | E-Mail
Interests: Machine Learning; Portfolio Optimization; Bayesian Networks; Networks Analysis

Special Issue Information

Dear Colleagues,

Nowadays computational methods have gained considerable attention in economics and finance as an alternative to conventional analytical and numerical paradigms. This special issue is devoted to bringing together contributions from both the theoretical and the application side, with a focus on the use of computational intelligence in finance and economics. We therefore welcome and encourage the submission of high quality papers related (but not limited) to:

  • Asset Pricing

  • Business Analytics

  • Big Data Analytics

  • Financial Data Mining

  • Economic and Financial Decision Making under Uncertainty

  • Portfolio Management and Optimization

  • Risk Management

  • Credit Risk Modelling

  • Commodity Markets

  • Term Structure Models

  • Trading Systems

  • Hedging Strategies

  • Risk Arbitrage

  • Exotic Options

  • Deep Learning and Artificial Neural Networks

  • Fuzzy Sets, Rough Sets, & Granular Computing

  • Hybrid Systems

  • Support Vector Machines

  • Non-linear Dynamics

Dr. Marina Resta
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Computational methods

  • Financial engineering

  • Data analytics

Published Papers (6 papers)

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Research

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Open AccessArticle Target Matrix Estimators in Risk-Based Portfolios
Received: 14 October 2018 / Revised: 29 October 2018 / Accepted: 2 November 2018 / Published: 5 November 2018
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Abstract
Portfolio weights solely based on risk avoid estimation errors from the sample mean, but they are still affected from the misspecification in the sample covariance matrix. To solve this problem, we shrink the covariance matrix towards the Identity, the Variance Identity, the Single-index [...] Read more.
Portfolio weights solely based on risk avoid estimation errors from the sample mean, but they are still affected from the misspecification in the sample covariance matrix. To solve this problem, we shrink the covariance matrix towards the Identity, the Variance Identity, the Single-index model, the Common Covariance, the Constant Correlation, and the Exponential Weighted Moving Average target matrices. Using an extensive Monte Carlo simulation, we offer a comparative study of these target estimators, testing their ability in reproducing the true portfolio weights. We control for the dataset dimensionality and the shrinkage intensity in the Minimum Variance (MV), Inverse Volatility (IV), Equal-Risk-Contribution (ERC), and Maximum Diversification (MD) portfolios. We find out that the Identity and Variance Identity have very good statistical properties, also being well conditioned in high-dimensional datasets. In addition, these two models are the best target towards which to shrink: they minimise the misspecification in risk-based portfolio weights, generating estimates very close to the population values. Overall, shrinking the sample covariance matrix helps to reduce weight misspecification, especially in the Minimum Variance and the Maximum Diversification portfolios. The Inverse Volatility and the Equal-Risk-Contribution portfolios are less sensitive to covariance misspecification and so benefit less from shrinkage. Full article
(This article belongs to the Special Issue Computational Methods for Risk Management in Economics and Finance)
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Open AccessArticle A General Framework for Portfolio Theory. Part II: Drawdown Risk Measures
Received: 29 June 2018 / Revised: 1 August 2018 / Accepted: 2 August 2018 / Published: 7 August 2018
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Abstract
The aim of this paper is to provide several examples of convex risk measures necessary for the application of the general framework for portfolio theory of Maier-Paape and Zhu (2018), presented in Part I of this series. As an alternative to classical portfolio [...] Read more.
The aim of this paper is to provide several examples of convex risk measures necessary for the application of the general framework for portfolio theory of Maier-Paape and Zhu (2018), presented in Part I of this series. As an alternative to classical portfolio risk measures such as the standard deviation, we, in particular, construct risk measures related to the “current” drawdown of the portfolio equity. In contrast to references Chekhlov, Uryasev, and Zabarankin (2003, 2005), Goldberg and Mahmoud (2017), and Zabarankin, Pavlikov, and Uryasev (2014), who used the absolute drawdown, our risk measure is based on the relative drawdown process. Combined with the results of Part I, Maier-Paape and Zhu (2018), this allows us to calculate efficient portfolios based on a drawdown risk measure constraint. Full article
(This article belongs to the Special Issue Computational Methods for Risk Management in Economics and Finance)
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Open AccessArticle A General Framework for Portfolio Theory—Part I: Theory and Various Models
Received: 30 March 2018 / Revised: 23 April 2018 / Accepted: 1 May 2018 / Published: 8 May 2018
Cited by 2 | PDF Full-text (500 KB) | HTML Full-text | XML Full-text
Abstract
Utility and risk are two often competing measurements on the investment success. We show that efficient trade-off between these two measurements for investment portfolios happens, in general, on a convex curve in the two-dimensional space of utility and risk. This is a rather [...] Read more.
Utility and risk are two often competing measurements on the investment success. We show that efficient trade-off between these two measurements for investment portfolios happens, in general, on a convex curve in the two-dimensional space of utility and risk. This is a rather general pattern. The modern portfolio theory of Markowitz (1959) and the capital market pricing model Sharpe (1964), are special cases of our general framework when the risk measure is taken to be the standard deviation and the utility function is the identity mapping. Using our general framework, we also recover and extend the results in Rockafellar et al. (2006), which were already an extension of the capital market pricing model to allow for the use of more general deviation measures. This generalized capital asset pricing model also applies to e.g., when an approximation of the maximum drawdown is considered as a risk measure. Furthermore, the consideration of a general utility function allows for going beyond the “additive” performance measure to a “multiplicative” one of cumulative returns by using the log utility. As a result, the growth optimal portfolio theory Lintner (1965) and the leverage space portfolio theory Vince (2009) can also be understood and enhanced under our general framework. Thus, this general framework allows a unification of several important existing portfolio theories and goes far beyond. For simplicity of presentation, we phrase all for a finite underlying probability space and a one period market model, but generalizations to more complex structures are straightforward. Full article
(This article belongs to the Special Issue Computational Methods for Risk Management in Economics and Finance)
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Open AccessArticle Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants
Received: 16 March 2018 / Revised: 25 April 2018 / Accepted: 1 May 2018 / Published: 7 May 2018
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Abstract
The direction of price movements are analysed under an ordered probit framework, recognising the importance of accounting for discreteness in price changes. By extending the work of Hausman et al. (1972) and Yang and Parwada (2012),This paper focuses on improving the forecast performance [...] Read more.
The direction of price movements are analysed under an ordered probit framework, recognising the importance of accounting for discreteness in price changes. By extending the work of Hausman et al. (1972) and Yang and Parwada (2012),This paper focuses on improving the forecast performance of the model while infusing a more practical perspective by enhancing flexibility. This is achieved by extending the existing framework to generate short term multi period ahead forecasts for better decision making, whilst considering the serial dependence structure. This approach enhances the flexibility and adaptability of the model to future price changes, particularly targeting risk minimisation. Empirical evidence is provided, based on seven stocks listed on the Australian Securities Exchange (ASX). The prediction success varies between 78 and 91 per cent for in-sample and out-of-sample forecasts for both the short term and long term. Full article
(This article belongs to the Special Issue Computational Methods for Risk Management in Economics and Finance)
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Open AccessArticle Credit Risk Analysis Using Machine and Deep Learning Models
Received: 9 February 2018 / Revised: 3 April 2018 / Accepted: 9 April 2018 / Published: 16 April 2018
Cited by 1 | PDF Full-text (620 KB) | HTML Full-text | XML Full-text
Abstract
Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, [...] Read more.
Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability. The top 10 important features from these models are selected and then used in the modeling process to test the stability of binary classifiers by comparing their performance on separate data. We observe that the tree-based models are more stable than the models based on multilayer artificial neural networks. This opens several questions relative to the intensive use of deep learning systems in enterprises. Full article
(This article belongs to the Special Issue Computational Methods for Risk Management in Economics and Finance)
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Review

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Open AccessReview Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations
Received: 28 February 2018 / Revised: 17 April 2018 / Accepted: 19 April 2018 / Published: 23 April 2018
Cited by 1 | PDF Full-text (2834 KB) | HTML Full-text | XML Full-text
Abstract
We review recent progress in modeling credit risk for correlated assets. We employ a new interpretation of the Wishart model for random correlation matrices to model non-stationary effects. We then use the Merton model in which default events and losses are derived from [...] Read more.
We review recent progress in modeling credit risk for correlated assets. We employ a new interpretation of the Wishart model for random correlation matrices to model non-stationary effects. We then use the Merton model in which default events and losses are derived from the asset values at maturity. To estimate the time development of the asset values, the stock prices are used, the correlations of which have a strong impact on the loss distribution, particularly on its tails. These correlations are non-stationary, which also influences the tails. We account for the asset fluctuations by averaging over an ensemble of random matrices that models the truly existing set of measured correlation matrices. As a most welcome side effect, this approach drastically reduces the parameter dependence of the loss distribution, allowing us to obtain very explicit results, which show quantitatively that the heavy tails prevail over diversification benefits even for small correlations. We calibrate our random matrix model with market data and show how it is capable of grasping different market situations. Furthermore, we present numerical simulations for concurrent portfolio risks, i.e., for the joint probability densities of losses for two portfolios. For the convenience of the reader, we give an introduction to the Wishart random matrix model. Full article
(This article belongs to the Special Issue Computational Methods for Risk Management in Economics and Finance)
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