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Risks
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Computational Methods in Quantitative Risk Management
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Computational Methods in Quantitative Risk Management

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

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 16809

Special Issue Editors

Prof. Dr. Marco Bee

E-Mail Website
Guest Editor
Department of Economics and Management, University of Trento, 38122 Trento, Italy
Interests: applied econometrics; computational statistics; loss models; Monte Carlo methods; quantitative risk management; statistical distributions
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Trapin

E-Mail Website
Guest Editor
Department of Statistics, University of Bologna, 40126 Bologna, Italy
Interests: financial econometrics; climate econometrics; time series analysis; extreme value analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Quantitative risk management is an active area of research from both the theoretical and the practical points of view. Financial datasets feature peculiar and ever-changing stylized facts, arising for example from the development of new financial products, or by the increased availability of high-frequency data. Therefore, considerable research efforts have focused on non-standard distributions that often require computer-intensive methods, both for estimation and for prediction.

These issues are mostly tackled using a multidisciplinary approach, so that the techniques used are borrowed from a multitude of fields. In particular, in recent years, statistics and financial econometrics have been backed up by machine learning and artificial intelligence.

Accordingly, research faces new challenges related to the interplay of approaches typically used by different communities. It is the purpose of this Special Issue to explore recent developments of such methods in the field of quantitative risk management. We thus solicit high-quality papers about the following topics:

  • Non-standard loss distributions and their applications
  • Estimation, prediction and backtesting of risk management models
  • Financial econometrics
  • Empirical finance
  • Computational methods for derivatives pricing
  • Monte Carlo simulation in risk management and financial engineering
  • High-frequency econometrics
  • Volatility specification and estimation
  • Extreme Value Theory

Dr. Marco Bee
Dr. Luca Trapin
Guest Editors

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 submissions that pass pre-check are 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 monthly 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 1800 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

  • Loss distribution
  • Backtesting of risk management models
  • Copulas
  • Derivatives
  • Monte Carlo simulation
  • Variance reduction
  • Volatility
  • Extreme Value Theory

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Published Papers (5 papers)

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Research

16 pages, 5183 KiB  
Article
Forward-Looking Volatility Estimation for Risk-Managed Investment Strategies during the COVID-19 Crisis
by Luca Di Persio, Matteo Garbelli and Kai Wallbaum
Risks 2021, 9(2), 33; https://doi.org/10.3390/risks9020033 - 1 Feb 2021
Cited by 8 | Viewed by 5072
Abstract
Under the impact of both increasing credit pressure and low economic returns characterizing developed countries, investment levels have decreased over recent years. Moreover, the recent turbulence caused by the COVID-19 crisis has accelerated the latter process. Within this scenario, we consider the so-called [...] Read more.
Under the impact of both increasing credit pressure and low economic returns characterizing developed countries, investment levels have decreased over recent years. Moreover, the recent turbulence caused by the COVID-19 crisis has accelerated the latter process. Within this scenario, we consider the so-called Volatility Target (VolTarget) strategy. In particular, we focus our attention on estimating volatility levels of a risky asset to perform a VolTarget simulation over two different time horizons. We first consider a 20 year period, from January 2000 to January 2020, then we analyse the last 12 months to emphasize the effects related to the COVID-19 virus’s diffusion. We propose a hybrid algorithm based on the composition of a GARCH model with a Neural Network (NN) approach. Let us underline that, as an alternative to standard allocation methods based on realized and backward oriented volatilities, we exploited an innovative forward-looking estimation process exploiting a Machine Learning (ML) solution. Our solution provides a more accurate volatility estimation, allowing us to derive an effective investor risk-return profile during market crisis periods. Moreover, we show that, via a forward-looking VolTarget strategy while using an ML-based prediction as the input, the average outcome for an investment in a drawdown plan is more sustainable while representing an efficient risk-control solution for long time period investments. Full article
(This article belongs to the Special Issue Computational Methods in Quantitative Risk Management)
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14 pages, 474 KiB  
Article
Combining a Matheuristic with Simulation for Risk Management of Stochastic Assets and Liabilities
by Christopher Bayliss, Marti Serra, Armando Nieto and Angel A. Juan
Risks 2020, 8(4), 131; https://doi.org/10.3390/risks8040131 - 4 Dec 2020
Cited by 3 | Viewed by 3104
Abstract
Specially in the case of scenarios under uncertainty, the efficient management of risk when matching assets and liabilities is a relevant issue for most insurance companies. This paper considers such a scenario, where different assets can be aggregated to better match a liability [...] Read more.
Specially in the case of scenarios under uncertainty, the efficient management of risk when matching assets and liabilities is a relevant issue for most insurance companies. This paper considers such a scenario, where different assets can be aggregated to better match a liability (or the other way around), and the goal is to find the asset-liability assignments that maximises the overall benefit over a time horizon. To solve this stochastic optimisation problem, a simulation-optimisation methodology is proposed. We use integer programming to generate efficient asset-to-liability assignments, and Monte-Carlo simulation is employed to estimate the risk of failing to pay due liabilities. The simulation results allow us to set a safety margin parameter for the integer program, which encourage the generation of solutions satisfying a minimum reliability threshold. A series of computational experiments contribute to illustrate the proposed methodology and its utility in practical risk management. Full article
(This article belongs to the Special Issue Computational Methods in Quantitative Risk Management)
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18 pages, 2416 KiB  
Article
A Tale of Two Layers: The Mutual Relationship between Bitcoin and Lightning Network
by Stefano Martinazzi, Daniele Regoli and Andrea Flori
Risks 2020, 8(4), 129; https://doi.org/10.3390/risks8040129 - 1 Dec 2020
Cited by 3 | Viewed by 3610
Abstract
A major concern of the adoption and scalability of Blockchain technologies refers to their efficient use for payments. In this work, we analyze how Lightning Network (LN), which represents a relevant infrastructural novelty, is influenced by the market dynamics of its referring cryptocurrency, [...] Read more.
A major concern of the adoption and scalability of Blockchain technologies refers to their efficient use for payments. In this work, we analyze how Lightning Network (LN), which represents a relevant infrastructural novelty, is influenced by the market dynamics of its referring cryptocurrency, namely Bitcoin. In so doing, we focus on how the LN is efficient in performing transactions and we relate this feature to the market conditions of Bitcoin. By applying the Toda–Yamamoto variant of Granger-causality, we note that market conditions of Bitcoin do not significantly influence the topological configuration of the LN. Hence, although the LN represents a second layer on the Bitcoin blockchain, our findings suggest that its efficient functioning does not appear to be related to the simple market performance of its underlying cryptocurrency and, in particular, of its volatile market fluctuations. This result may therefore contribute to shed light on the practical usage of the LN as a blockchain technology to favor transactions. Full article
(This article belongs to the Special Issue Computational Methods in Quantitative Risk Management)
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21 pages, 466 KiB  
Article
Modeling Multivariate Financial Series and Computing Risk Measures via Gram–Charlier-Like Expansions
by Maria Grazia Zoia, Gianmarco Vacca and Laura Barbieri
Risks 2020, 8(4), 123; https://doi.org/10.3390/risks8040123 - 16 Nov 2020
Cited by 1 | Viewed by 2158
Abstract
This paper develops an approach based on Gram–Charlier-like expansions for modeling financial series to take in due account features such as leptokurtosis. A Gram–Charlier-like expansion adjusts the moments of interest of a given distribution via its own orthogonal polynomials. This approach, formerly adopted [...] Read more.
This paper develops an approach based on Gram–Charlier-like expansions for modeling financial series to take in due account features such as leptokurtosis. A Gram–Charlier-like expansion adjusts the moments of interest of a given distribution via its own orthogonal polynomials. This approach, formerly adopted for univariate series, is here extended to a multivariate context by means of spherical densities. Previous works proposed the Gram–Charlier of the multivariate Gaussian, obtained by using Hermite polynomials. This work shows how polynomial expansions of an entire class of spherical laws can be worked out with the aim of obtaining a wide set of leptokurtic multivariate distributions. A Gram–Charlier-like expansion is a distribution characterized by an additional parameter with respect to the parent spherical law. This parameter, which measures the increase in kurtosis due to the polynomial expansion, can be estimated so as to make the resulting distribution capable of describing the empirical kurtosis found in the data. An application of the Gram–Charlier-like expansions to a set of financial assets proves their effectiveness in modeling multivariate financial series and assessing risk measures, such as the value at risk and the expected shortfall. Full article
(This article belongs to the Special Issue Computational Methods in Quantitative Risk Management)
17 pages, 1227 KiB  
Article
Nonparametric Malliavin–Monte Carlo Computation of Hedging Greeks
by Maria Elvira Mancino and Simona Sanfelici
Risks 2020, 8(4), 120; https://doi.org/10.3390/risks8040120 - 13 Nov 2020
Cited by 2 | Viewed by 2167
Abstract
We propose a way to compute the hedging Delta using the Malliavin weight method. Our approach, which we name the λ-method, generally outperforms the standard Monte Carlo finite difference method, especially for discontinuous payoffs. Furthermore, our approach is nonparametric, as we only [...] Read more.
We propose a way to compute the hedging Delta using the Malliavin weight method. Our approach, which we name the λ-method, generally outperforms the standard Monte Carlo finite difference method, especially for discontinuous payoffs. Furthermore, our approach is nonparametric, as we only assume a general local volatility model and we substitute the volatility and the other processes involved in the Greek formula with quantities that can be nonparametrically estimated from a given time series of observed prices. Full article
(This article belongs to the Special Issue Computational Methods in Quantitative Risk Management)
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