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4 pages, 183 KiB

Open AccessEditorial

Computational Issues in Insurance and Finance

by Cira Perna and Marilena Sibillo

Computation 2023, 11(4), 80; https://0-doi-org.brum.beds.ac.uk/10.3390/computation11040080 - 14 Apr 2023

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Abstract

Comparison and cultural exchange always enrich and produce innovative and interesting results [...] Full article

(This article belongs to the Special Issue Computational Issues in Insurance and Finance)

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13 pages, 416 KiB

Open AccessArticle

Modelling Qualitative Data from Repeated Surveys

by Marcella Corduas and Domenico Piccolo

Computation 2023, 11(3), 64; https://0-doi-org.brum.beds.ac.uk/10.3390/computation11030064 - 20 Mar 2023

Cited by 1 | Viewed by 1056

Abstract

This article presents an innovative dynamic model that describes the probability distributions of ordered categorical variables observed over time. For this purpose, we extend the definition of the mixture distribution obtained from the combination of a uniform and a shifted binomial distribution (CUB [...] Read more.

This article presents an innovative dynamic model that describes the probability distributions of ordered categorical variables observed over time. For this purpose, we extend the definition of the mixture distribution obtained from the combination of a uniform and a shifted binomial distribution (CUB model), introducing time-varying parameters. The model parameters identify the main components ruling the respondent evaluation process: the degree of attraction towards the object under assessment, the uncertainty related to the answer, and the weight of the refuge category that is selected when a respondent is unwilling to elaborate a thoughtful judgement. The method provides a tool to quantify the data from qualitative surveys. For illustrative purposes, the dynamic CUB model is applied to the consumers’ perceptions and expectations of inflation in Italy to investigate: (a) the effect of the COVID pandemic on inflation beliefs; (b) the impact of income level on respondents’ expectations. Full article

(This article belongs to the Special Issue Computational Issues in Insurance and Finance)

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17 pages, 1940 KiB

Open AccessArticle

Measuring the Recovery Performance of a Portfolio of NPLs

by Alessandra Carleo, Roberto Rocci and Maria Sole Staffa

Computation 2023, 11(2), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/computation11020029 - 07 Feb 2023

Cited by 3 | Viewed by 1733

Abstract

The objective of the present paper is to propose a new method to measure the recovery performance of a portfolio of non-performing loans (NPLs) in terms of recovery rate and time to liquidate. The fundamental idea is to draw a curve representing the [...] Read more.

The objective of the present paper is to propose a new method to measure the recovery performance of a portfolio of non-performing loans (NPLs) in terms of recovery rate and time to liquidate. The fundamental idea is to draw a curve representing the recovery rates over time, here assumed discretized, for example, in years. In this way, the user can get simultaneously information about recovery rate and time to liquidate of the portfolio. In particular, it is discussed how to estimate such a curve in the presence of right-censored data, e.g., when the NPLs composing the portfolio have been observed in different time periods, with a method based on an algorithm that is usually used in the construction of survival curves. The curves obtained are smoothed with nonparametric statistical learning techniques. The effectiveness of the proposal is shown by applying the method to simulated and real financial data. The latter are about some portfolios of Italian unsecured NPLs taken over by a specialized operator. Full article

(This article belongs to the Special Issue Computational Issues in Insurance and Finance)

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13 pages, 322 KiB

Open AccessArticle

Nonparametric Estimation of Range Value at Risk

by Suparna Biswas and Rituparna Sen

Computation 2023, 11(2), 28; https://0-doi-org.brum.beds.ac.uk/10.3390/computation11020028 - 06 Feb 2023

Cited by 3 | Viewed by 1662

Abstract

Range value at risk (RVaR) is a quantile-based risk measure with two parameters. As special examples, the value at risk (VaR) and the expected shortfall (ES), two well-known but competing regulatory risk measures, are both members of the RVaR family. The estimation of [...] Read more.

Range value at risk (RVaR) is a quantile-based risk measure with two parameters. As special examples, the value at risk (VaR) and the expected shortfall (ES), two well-known but competing regulatory risk measures, are both members of the RVaR family. The estimation of RVaR is a critical issue in the financial sector. Several nonparametric RVaR estimators are described here. We examine these estimators’ accuracy in various scenarios using Monte Carlo simulations. Our simulations shed light on how changing p and q with respect to n affects the effectiveness of RVaR estimators that are nonparametric, with n representing the total number of samples. Finally, we perform a backtesting exercise of RVaR based on Acerbi and Szekely’s test. Full article

(This article belongs to the Special Issue Computational Issues in Insurance and Finance)

11 pages, 335 KiB

Open AccessArticle

Some Remarks on Malicious and Negligent Data Breach Distribution Estimates

by Maria Francesca Carfora and Albina Orlando

Computation 2022, 10(12), 208; https://0-doi-org.brum.beds.ac.uk/10.3390/computation10120208 - 30 Nov 2022

Cited by 1 | Viewed by 1718

Abstract

Digitization offers great opportunities as well as new challenges. Indeed, these opportunities entail increased cyber risks, both from deliberate cyberattacks and from incidents caused by inadvertent human error. Cyber risk must be mastered, and to this aim, its quantification is an urgent challenge. [...] Read more.

Digitization offers great opportunities as well as new challenges. Indeed, these opportunities entail increased cyber risks, both from deliberate cyberattacks and from incidents caused by inadvertent human error. Cyber risk must be mastered, and to this aim, its quantification is an urgent challenge. There is a lot of interest in this topic from the insurance community in order to price adequate coverage to their customers. A key first step is to investigate the frequency and severity of cyber incidents. On the grounds that data breaches seem to be the main cause of cyber incidents, the aim of this paper is to give further insights about the frequency and severity statistical distributions of malicious and negligent data breaches. For this purpose, we refer to a publicly available dataset: the Chronology of Data Breaches provided by the Privacy Rights Clearinghouse. Full article

(This article belongs to the Special Issue Computational Issues in Insurance and Finance)

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20 pages, 534 KiB

Open AccessArticle

Deep Neural Network Algorithms for Parabolic PIDEs and Applications in Insurance and Finance

by Rüdiger Frey and Verena Köck

Computation 2022, 10(11), 201; https://0-doi-org.brum.beds.ac.uk/10.3390/computation10110201 - 10 Nov 2022

Cited by 4 | Viewed by 1795

Abstract

In this paper we study deep neural network algorithms for solving linear and semilinear parabolic partial integro-differential equations with boundary conditions in high dimension. Our method can be considered as an extension of the deep splitting method for PDEs to equations with non-local [...] Read more.

In this paper we study deep neural network algorithms for solving linear and semilinear parabolic partial integro-differential equations with boundary conditions in high dimension. Our method can be considered as an extension of the deep splitting method for PDEs to equations with non-local terms. To show the viability of our approach, we discuss several case studies from insurance and finance. Full article

(This article belongs to the Special Issue Computational Issues in Insurance and Finance)

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11 pages, 2359 KiB

Open AccessArticle

Factors Affecting Demand and Supply in the Housing Market: A Study on Three Major Cities in Turkey

by Sheikh Abdul Kader, Nurul Mohammad Zayed, Md. Faisal-E-Alam, Muhammad Salah Uddin, Vitalii Nitsenko and Yuliia Klius

Computation 2022, 10(11), 196; https://0-doi-org.brum.beds.ac.uk/10.3390/computation10110196 - 02 Nov 2022

Cited by 7 | Viewed by 5655

Abstract

This paper aims to identify the economic factors that significantly affect the demand for and supply of housing in three major cities in Turkey, such as Istanbul, Ankara, and Izmir. This study uses monthly data ranges from January 2010 to December 2020 because [...] Read more.

This paper aims to identify the economic factors that significantly affect the demand for and supply of housing in three major cities in Turkey, such as Istanbul, Ankara, and Izmir. This study uses monthly data ranges from January 2010 to December 2020 because of the limited housing price data from each city. For smooth measurement, the logarithm of all data except measurements of nominal interest rate, real interest rate and inflation is used. This research uses the Co-integration Analysis and Vector Error Correction Model (VECM) to investigate the macroeconomic variables’ effects on the demand and supply. Mortgage credit volume, as a dependent variable, is influenced by real per capita GDP, real house prices, projected inflation, and nominal interest rates. On the contrary, the building site is used as a dependent variable on the supply side that is determined by the real housing price, the real interest rate, and the real cost of construction. In the VECM model, the mortgage credit volume and constriction cost were dominated by error correction variables, showing the adjustment of disequilibrium towards an equilibrium point. In the case of Ankara, supply-side variables have a long-term relationship. Both housing demand and supply-related factors have a long-term impact on the housing market in Istanbul and Izmir. Given a significant p-value, the coefficient of C1 derived from system equations is negative. Full article

(This article belongs to the Special Issue Computational Issues in Insurance and Finance)

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14 pages, 336 KiB

Open AccessArticle

On Barrier Binary Options in the Telegraph-like Financial Market Model

by Nikita Ratanov

Computation 2022, 10(9), 163; https://0-doi-org.brum.beds.ac.uk/10.3390/computation10090163 - 17 Sep 2022

Cited by 2 | Viewed by 1233

Abstract

The article continues the study of the market model based on jump-telegraph processes. It is assumed that the price of a risky asset follows the stochastic exponential of a piecewise linear process, equipped with jumps that occur at the moments of a pattern [...] Read more.

The article continues the study of the market model based on jump-telegraph processes. It is assumed that the price of a risky asset follows the stochastic exponential of a piecewise linear process, equipped with jumps that occur at the moments of a pattern change. In this case, the standard option pricing formula was derived previously, while exotic options for this model have not yet been explored. Within this framework, we are developing procedures for pricing binary barrier options. This article concerns the “cash-(at hit)-or-nothing” binary barrier option. The main tools of this analysis are methods developed for first-pass probabilities. Some known results related to the ruin probabilities follow directly from these settings. Full article

(This article belongs to the Special Issue Computational Issues in Insurance and Finance)

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41 pages, 664 KiB

Open AccessArticle

Credit Spreads, Leverage and Volatility: A Cointegration Approach

by Federico Maglione

Computation 2022, 10(9), 155; https://0-doi-org.brum.beds.ac.uk/10.3390/computation10090155 - 05 Sep 2022

Cited by 2 | Viewed by 1668

Abstract

This work documents the existence of a cointegration relationship between credit spreads, leverage and equity volatility for a large set of US companies. It is shown that accounting for the long-run equilibrium dynamic between these variables is essential to correctly explain credit spread [...] Read more.

This work documents the existence of a cointegration relationship between credit spreads, leverage and equity volatility for a large set of US companies. It is shown that accounting for the long-run equilibrium dynamic between these variables is essential to correctly explain credit spread changes. Using a novel structural model in which equity is modeled as a compound option on the firm’s assets, a new methodology for estimating the unobservable market value of the firm’s assets and volatility is developed. The proposed model allows to significantly reduce the pricing errors in predicting credit spreads when compared with several structural models. In terms of correlation analysis, it is shown that not accounting for the long-run equilibrium equation embedded in an error correction mechanism (ECM) results into a misspecification problem when regressing a set of explanatory variables onto the spread changes. Once credit spreads, leverage and volatility are correctly modeled, thus allowing for a long-run equilibrium, the fit of the regressions sensibly increases if compared to the results of previous research. It is further shown that most of the cross-sectional variation of the spreads appears to be more driven by firm-specific characteristics rather than systematic factors. Full article

(This article belongs to the Special Issue Computational Issues in Insurance and Finance)

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