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Risks
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Valuation Risk and Asset Pricing
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Valuation Risk and Asset Pricing

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2244

Special Issue Editors

Prof. Dr. Weidong Tian

E-Mail Website
Guest Editor
Department of Finance, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA
Interests: asset pricing; risk management; Knightian uncertainty; derivative and insurance market
Special Issues, Collections and Topics in MDPI journals
Dr. Steven P. Clark

E-Mail Website
Guest Editor
Department of Finance, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA
Interests: mathematical finance; derivatives; corporate finance; stochastic modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern asset pricing models have been applied in both theoretical and practical contexts for pricing standard asset classes, encompassing stocks, bonds, foreign exchanges, derivatives, structured products, and securitizations. Throughout this application, the assessment of valuation risks has remained a pivotal consideration. However, as markets evolve and new asset classes such as cryptocurrency emerge, alongside alternative asset classes like art and non-standard asset classes, including deposits, loans, and banking franchises, the task of developing pricing models and addressing valuation risks becomes notably more complex and fascinating for researchers and practitioners.

We cordially invite you to submit your paper for publication in the Risks Special Issue, entitled “Valuation Risk and Asset Pricing”.

We welcome any new empirical or theoretical papers which focus on the pricing of non-traditional asset classes, including, but not limited to, deposits, loans, securitizations, personal finance, cryptocurrency, alternative asset classes, and associated valuation risk management issues.

The Risks Special Issue will utilize a double-blind peer review process. The initial review will be completed within four weeks, and clear guidance will also be provided for the first-round revision.

Prof. Dr. Weidong Tian
Dr. Steven P. Clark
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

  • asset pricing
  • deposits
  • loans
  • securitizations
  • personal finance
  • cryptocurrency
  • alternative asset classes
  • valuation risk management issues

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Published Papers (3 papers)

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Research

19 pages, 784 KiB  
Article
Determinants of Firms’ Propensity to Use Intercorporate Loans: Empirical Evidence from India
by Biswajit Ghose, Prasenjit Roy, Yeshi Ngima, Kiran Gope, Pankaj Kumar Tyagi, Premendra Kumar Singh and Asokan Vasudevan
Risks 2025, 13(4), 71; https://doi.org/10.3390/risks13040071 - 2 Apr 2025
Viewed by 319
Abstract
Several studies have investigated the determinants of firms’ capital structure choices. Though an intercorporate loan is an essential source of corporate debt, there are no studies that examine the determinants of firms’ preference to use the intercorporate loan as a source of debt. [...] Read more.
Several studies have investigated the determinants of firms’ capital structure choices. Though an intercorporate loan is an essential source of corporate debt, there are no studies that examine the determinants of firms’ preference to use the intercorporate loan as a source of debt. This study examines the relevance of the conventional capital structure determinants in explaining firms’ tendency to use intercorporate loans. The study is based on a dataset of 53,112 firm-year observations comprising 3739 non-financial listed Indian firms for 21 years from 2002 to 2022. The random effect logistic regression model is used to investigate the objectives. The conventional capital structure determinants are relevant in explaining firms’ decisions to use intercorporate loans. Firm size, asset tangibility, and earnings volatility favorably influence the tendency to use intercorporate loans, whereas profitability, growth, uniqueness, dividend payment, ownership concentration, and foreign promoter holdings adversely affect the same. The results reveal that the influence of firm size, uniqueness, earnings volatility, and ownership concentration are not unidirectional for group-affiliated and standalone firms. The findings are mostly consistent with the arguments of conventional capital structure theories. The results of this study will be pragmatic for financial managers in their capital structure decisions. Full article
(This article belongs to the Special Issue Valuation Risk and Asset Pricing)
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26 pages, 747 KiB  
Article
An Integrated Risk Management Methodology for Deposits and Loans
by Gregory R. Hackworth, Weidong Tian and Michael R. Vandenberg
Risks 2025, 13(3), 52; https://doi.org/10.3390/risks13030052 - 13 Mar 2025
Viewed by 503
Abstract
This paper presents an integrated risk management methodology for measuring and managing the economics, risks, and financial resources/constraints related to deposits and loans in a commercial bank. Within a comprehensive and integrated framework, we develop valuation and risk models for all financial products [...] Read more.
This paper presents an integrated risk management methodology for measuring and managing the economics, risks, and financial resources/constraints related to deposits and loans in a commercial bank. Within a comprehensive and integrated framework, we develop valuation and risk models for all financial products on the bank’s balance sheet. Our proposed methodology aligns with regulatory requirements while offering a practical implementation. Unlike traditional industry practices, which often rely on fragmented and siloed risk management solutions, our approach integrates risk modeling across all aspects of the bank’s balance sheet. This new perspective provides a more accurate and consistent assessment of financial risks, improving the bank’s ability to navigate regulatory and economic challenges. Full article
(This article belongs to the Special Issue Valuation Risk and Asset Pricing)
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24 pages, 1644 KiB  
Article
On GARCH and Autoregressive Stochastic Volatility Approaches for Market Calibration and Option Pricing
by Tao Pang and Yang Zhao
Risks 2025, 13(2), 31; https://doi.org/10.3390/risks13020031 - 10 Feb 2025
Viewed by 855
Abstract
In this paper, we carry out a comprehensive comparison of Gaussian generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive stochastic volatility (ARSV) models for volatility forecasting using the S&P 500 Index. In particular, we investigate their performance using the physical measure (also known as [...] Read more.
In this paper, we carry out a comprehensive comparison of Gaussian generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive stochastic volatility (ARSV) models for volatility forecasting using the S&P 500 Index. In particular, we investigate their performance using the physical measure (also known as the real-world probability measure) for risk management purposes and risk-neutral measures for derivative pricing purposes. Under the physical measure, after fitting the historical return sequence, we calculate the likelihoods and test the normality for the error terms of these two models. In addition, two robust loss functions, the MSE and QLIKE, are adopted for a comparison of the one-step-ahead volatility forecasts. The empirical results show that the ARSV(1) model outperforms the GARCH(1, 1) model in terms of the in-sample and out-of-sample performance under the physical measure. Under the risk-neutral measure, we explore the in-sample and out-of-sample average option pricing errors of the two models. The results indicate that these two models are considerably close when pricing call options, while the ARSV(1) model is significantly superior to the GARCH(1, 1) model regarding fitting and predicting put option prices. Another finding is that the implied versions of the two models, which parameterize the initial volatility, are not robust for out-of-sample option price predictions. Full article
(This article belongs to the Special Issue Valuation Risk and Asset Pricing)
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