Special Issue "Quantitative Risk"

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Risk".

Deadline for manuscript submissions: closed (31 May 2020).

Special Issue Editor

Dr. Leung Lung Chan
Website
Guest Editor
School of Mathematics and Statistics, University of New South Wales Sydney NSW 2052, Australia
Interests: asset pricing models; regime-switching model; volatility derivatives; stochastic volatility models; consumption and investment
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is concerned with the broad topic of Quantitative Risk and includes any theoretical or empirical work related to this area.

Quantitative risk includes all areas of risk management with the application of quantitative methods to real world problems. Any research associated with any contribution in: Credit risk modeling; volatility risk modeling, including pricing volatility derivatives; model risk; operational risk; interest rate risk; liquidity risk; mortality risk; measures of risk exposure, such as the value at risk and coherent risk measure; hedging strategies; correlation risk; corporate risk; etc. is welcome.

We invite investigators to contribute original research articles that advance the use of mathematics, probability, and statistics in all areas of quantitative risk. All submissions must contain original unpublished work not being considered for publication elsewhere.

Dr. Leung Lung Chan
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. Journal of Risk and Financial Management 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 1000 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

  • Risk management
  • Credit risk
  • Interest rate risk
  • Volatility risk
  • Mortality risk
  • Liquidity risk
  • Measures of risk
  • Operation risk

Published Papers (6 papers)

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Research

Open AccessArticle
Information Frictions and Stock Returns
J. Risk Financial Manag. 2020, 13(7), 140; https://doi.org/10.3390/jrfm13070140 - 01 Jul 2020
Abstract
The purpose of this paper is to assess the impact of ambiguity on financial analyst forecast incentives and the associated abnormal stock returns. I present a model incorporating ambiguity aversion into a two-period Lucas tree model. The resulting model confirms the role of [...] Read more.
The purpose of this paper is to assess the impact of ambiguity on financial analyst forecast incentives and the associated abnormal stock returns. I present a model incorporating ambiguity aversion into a two-period Lucas tree model. The resulting model confirms the role of ambiguity in the determination of asset returns. In particular, the model with ambiguity aversion generates a lower price and a higher required rate of returns compared to the classical model without ambiguity concern. I construct a measure of ambiguity and provide empirical evidence showing that the incentive of analysts to misrepresent information is a function of ambiguity. Analysts are more likely to bias their forecasts when it is more difficult for investors to detect their misrepresentation. Under ambiguity, analysts’ optimistic forecasts for good/bad news tend to deteriorate. Moreover, stock returns are positively related with ambiguity. Under ambiguity neither good nor bad news is credible. Investors systematically underreact to good news forecast and overreact to bad news forecast when ambiguity exists. Full article
(This article belongs to the Special Issue Quantitative Risk)
Open AccessArticle
Modeling Portfolio Credit Risk Taking into Account the Default Correlations Using a Copula Approach: Implementation to an Italian Loan Portfolio
J. Risk Financial Manag. 2020, 13(6), 129; https://doi.org/10.3390/jrfm13060129 - 17 Jun 2020
Abstract
This work aims to illustrate an advanced quantitative methodology for measuring the credit risk of a loan portfolio allowing for diversification effects. Also, this methodology can allocate the credit capital coherently to each counterparty in the portfolio. The analytical approach used for estimating [...] Read more.
This work aims to illustrate an advanced quantitative methodology for measuring the credit risk of a loan portfolio allowing for diversification effects. Also, this methodology can allocate the credit capital coherently to each counterparty in the portfolio. The analytical approach used for estimating the portfolio credit risk is a binomial type based on a Monte Carlo Simulation. This method takes into account the default correlations among the credit counterparties in the portfolio by following a copula approach and utilizing the asset return correlations of the obligors, as estimated by rigorous statistical methods. Moreover, this model considers the recovery rates as stochastic and dependent on each other and on the time until defaults. The methodology utilized for coherently allocating credit capital in the portfolio estimates the marginal contributions of each obligor to the overall risk of the loan portfolio in terms of Expected Shortfall (ES), a risk measure more coherent and conservative than the traditional measure of Value-at-Risk (VaR). Finally, this advanced analytical structure is implemented to a hypothetical, but typical, loan portfolio of an Italian commercial bank operating across the overall national country. The national loan portfolio is composed of 17 sub-portfolios, or geographic clusters of credit exposures to 10,500 non-financial firms (or corporates) belonging to each geo-cluster or sub-portfolio. The outcomes, in terms of correlations, portfolio risk measures and capital allocations obtained from this advanced analytical framework, are compared with the results found by implementing the Internal Rating Based (IRB) approach of Basel II and III. Our chief conclusion is that the IRB model is unable to capture the real credit risk of loan portfolios because it does not take into account the actual dependence structure among the default events, and between the recovery rates and the default events. We underline that the adoption of this regulatory model can produce a dangerous underestimation of the portfolio credit risk, especially when the economic uncertainty and the volatility of the financial markets increase. Full article
(This article belongs to the Special Issue Quantitative Risk)
Open AccessArticle
The Influence of Domestic and Foreign Shocks on Portfolio Diversification Gains and the Associated Risks
J. Risk Financial Manag. 2019, 12(4), 160; https://doi.org/10.3390/jrfm12040160 - 10 Oct 2019
Cited by 1
Abstract
This paper evaluates the influence of foreign or domestic stock market return and return of volatility shocks on dynamic conditional correlations (DCCs) between international stock markets and correlation volatility, respectively. The correlations between markets have implications for the gains from portfolio diversification, while [...] Read more.
This paper evaluates the influence of foreign or domestic stock market return and return of volatility shocks on dynamic conditional correlations (DCCs) between international stock markets and correlation volatility, respectively. The correlations between markets have implications for the gains from portfolio diversification, while correlation volatilities can be seen as risks to portfolio diversification. Meanwhile, domestic shocks are sourced from the return and return volatility from 24 developed, emerging, and frontier stock markets. The US stock market is the source of foreign shocks. The domestic and foreign shocks are derived using market-based returns and under bearish market conditions. We estimate multivariate exponential generalized autoregressive conditional heteroskedasticity (E-GARCH) models using daily and monthly MSCI based stock price data of selected developed, emerging, and frontier markets over 1993:1–2014:1. Our key results are as follows. Domestic market shocks were significant drivers of gains from portfolio diversification most of the time, although the US market effects were relatively stronger. On the other hand, the US, in terms of the number of significant cases as well as the size effects of shocks, dominated as a determinant of correlation volatility (or risks to portfolio diversification). Further, under bear market conditions, adjustments in correlations and correlation volatilities are found to be mostly US-induced. Bearish shocks, rather than market return based shocks, show strong evidence of the leverage effect. Signs of persistence of shocks are mainly noticed under bearish conditions. Full article
(This article belongs to the Special Issue Quantitative Risk)
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Open AccessArticle
Empirical Credit Risk Ratings of Individual Corporate Bonds and Derivation of Term Structures of Default Probabilities
J. Risk Financial Manag. 2019, 12(3), 124; https://doi.org/10.3390/jrfm12030124 - 23 Jul 2019
Abstract
Undoubtedly, it is important to have an empirically effective credit risk rating method for decision-making in the financial industry, business, and even government. In our approach, for each corporate bond (CB) and its issuer, we first propose a credit risk rating (Crisk-rating) system [...] Read more.
Undoubtedly, it is important to have an empirically effective credit risk rating method for decision-making in the financial industry, business, and even government. In our approach, for each corporate bond (CB) and its issuer, we first propose a credit risk rating (Crisk-rating) system with rating intervals for the standardized credit risk price spread (S-CRiPS) measure presented by Kariya et al. (2015), where credit information is based on the CRiPS measure, which is the difference between the CB price and its government bond (GB)-equivalent CB price. Second, for each Crisk-homogeneous class obtained through the Crisk-rating system, a term structure of default probability (TSDP) is derived via the CB-pricing model proposed in Kariya (2013), which transforms the Crisk level of each class into a default probability, showing the default likelihood over a future time horizon, in which 1545 Japanese CB prices, as of August 2010, are analyzed. To carry it out, the cross-sectional model of pricing government bonds with high empirical performance is required to get high-precision CRiPS and S-CRiPS measures. The effectiveness of our GB model and the S-CRiPS measure have been demonstrated with Japanese and United States GB prices in our papers and with an evaluation of the credit risk of the GBs of five countries in the EU and CBs issued by US energy firms in Kariya et al. (2016a, b). Our Crisk-rating system with rating intervals is tested with the distribution of the ratings of the 1545 CBs, a specific agency’s credit rating, and the ratings of groups obtained via a three-stage cluster analysis. Full article
(This article belongs to the Special Issue Quantitative Risk)
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Open AccessArticle
Default Risk and Cross Section of Returns
J. Risk Financial Manag. 2019, 12(2), 95; https://doi.org/10.3390/jrfm12020095 - 06 Jun 2019
Cited by 1
Abstract
Prior research uses the basic one-period European call-option pricing model to compute default measures for individual firms and concludes that both the size and book-to-market effects are related to default risk. For example, small firms earn higher return than big firms only if [...] Read more.
Prior research uses the basic one-period European call-option pricing model to compute default measures for individual firms and concludes that both the size and book-to-market effects are related to default risk. For example, small firms earn higher return than big firms only if they have higher default risk and value stocks earn higher returns than growth stocks if their default risk is high. In this paper we use a more advanced compound option pricing model for the computation of default risk and provide a more exhaustive test of stock returns using univariate and double-sorted portfolios. The results show that long/short hedge portfolios based on Geske measures of default risk produce significantly larger return differentials than Merton’s measure of default risk. The paper provides new evidence that mediates between the rational and behavioral explanations of value premium. Full article
(This article belongs to the Special Issue Quantitative Risk)
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Open AccessArticle
Tax Competitiveness of the New EU Member States
J. Risk Financial Manag. 2019, 12(1), 34; https://doi.org/10.3390/jrfm12010034 - 14 Feb 2019
Cited by 1
Abstract
This paper investigates tax competitiveness among the EU member countries. The tax competition of countries causes both positive and negative effects on macroeconomic processes such as the effectiveness of government spending, the rationality of supply of externalities, and the length and amplitudes of [...] Read more.
This paper investigates tax competitiveness among the EU member countries. The tax competition of countries causes both positive and negative effects on macroeconomic processes such as the effectiveness of government spending, the rationality of supply of externalities, and the length and amplitudes of business cycles. A considerable reduction of corporate tax in the EU is related to increased tax competition after new members entered the EU. Multiple criteria methods were chosen for the quantitative evaluation of EU countries from different regions of the EU. Criteria of evaluation were chosen and structured into a hierarchy. The convergence process of the new members of the EU is reinforced with the increasing tax competitiveness of such countries. Results of the multiple criteria evaluation revealed both the factors that increased the tax competitiveness of new members of the EU, and outlined the factors that hampered such competition. Full article
(This article belongs to the Special Issue Quantitative Risk)
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