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Quantitative Finance
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Quantitative Finance

A special issue of International Journal of Financial Studies (ISSN 2227-7072).

Deadline for manuscript submissions: closed (30 July 2021) | Viewed by 12295

Special Issue Editors

Dr. Vasilios Sogiakas

E-Mail Website
Guest Editor
Professor in Finance, Head of Accounting, Economics, and Finance, School of Business and Economics, The American College of Greece, 6 Gravias Street, GR-153 42 Aghia Paraskevi, Athens, Greece
Prof. Stefanos Papadamou

E-Mail Website
Co-Guest Editor
Department in Economics of Money and Banking, Department of Economics, University of Thessaly, 38333 Volos, Greece

Special Issue Information

Dear Colleagues,

This Special Issue of the International Journal of Financial Studies is devoted to Quantitative Finance reflecting the imperative necessity to incorporate advanced quantitative and computational techniques in Finance.

Our Special Issue welcomes papers dealing with original and innovative contributions in the following areas:

  • Asset pricing
  • EMH and adaptive market hypothesis
  • Financial markets
  • Financial econometrics
  • Risk management
  • Financial regulation
  • Artificial intelligence machine learning in financial trading
  • Volatility modeling and risk management
  • Nonlinear and stochastic optimization in finance
  • Behavior finance
  • Corporate finance
  • Derivatives pricing and hedging
  • Portfolio management
  • Financial markets’ regulation
  • Spillover effects
  • Price discovery and informational efficiency
  • Asset pricing and macroeconomic fundamentals
  • Financial market structure and microstructure
  • Mutual funds and hedge funds

Dr. Vasilios Sogiakas
Prof. Stefanos Papadamou
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. International Journal of Financial Studies 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 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

  • Financial markets
  • Portfolio choice
  • Asset pricing
  • Information and market efficiency
  • Contingent pricing
  • Government policy and regulation

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

23 pages, 689 KiB  
Article
Bootstrapping Time-Varying Uncertainty Intervals for Extreme Daily Return Periods
by Katleho Makatjane and Tshepiso Tsoku
Int. J. Financial Stud. 2022, 10(1), 10; https://doi.org/10.3390/ijfs10010010 - 27 Jan 2022
Cited by 4 | Viewed by 2874
Abstract
This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which are based on a weighted threshold and quantile of a [...] Read more.
This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which are based on a weighted threshold and quantile of a continuously ranked probability score, are developed. Developed backtesting procedures revealed that an estimated Seasonal autoregressive integrated moving average-generalized autoregressive score-generalized extreme value distribution (SARIMA–GAS–GEVD) with a skewed student-t distribution had the best prediction performance in forecasting and bootstrapping VaR and ES. Extension of this non-stationary distribution in literature is quite complicated since it requires specifications not only on how the usual Bayesian parameters change over time but also those with bulk distribution components. This implies that the combination of a stochastic econometric model with extreme value theory (EVT) procedures provides a robust basis necessary for the statistical backtesting and bootstrapping density predictions for VaR and ES. Full article
(This article belongs to the Special Issue Quantitative Finance)
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12 pages, 2401 KiB  
Article
Common Factors in the Term Structure of Credit Spreads and Predicting the Macroeconomy in Japan
by Takeshi Kobayashi
Int. J. Financial Stud. 2021, 9(2), 23; https://doi.org/10.3390/ijfs9020023 - 21 Apr 2021
Cited by 1 | Viewed by 2672
Abstract
This study extracts the common factors from firm-based credit spreads of major Japanese corporate bonds and examines the predictive content of the credit spread on the real economy. Instead of employing single-maturity corporate bond spreads, we focus on the entire term structure of [...] Read more.
This study extracts the common factors from firm-based credit spreads of major Japanese corporate bonds and examines the predictive content of the credit spread on the real economy. Instead of employing single-maturity corporate bond spreads, we focus on the entire term structure of the credit spread to predict the business cycle. We extend the dynamic Nelson-Siegel model to allow for both common and firm-specific factors. The results show that the estimated common factors are important drivers of individual credit spreads and have substantial predictive power for future Japanese economic activity. This study contributes to the literature by examining the relationship between firm-based credit spread curves and economic fluctuation and forecasting the business cycle. Full article
(This article belongs to the Special Issue Quantitative Finance)
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21 pages, 759 KiB  
Article
A Comprehensive Approach for Calculating Banking Sector Risks
by Carmelo Salleo, Alberto Grassi and Constantinos Kyriakopoulos
Int. J. Financial Stud. 2020, 8(4), 69; https://doi.org/10.3390/ijfs8040069 - 10 Nov 2020
Cited by 4 | Viewed by 3301
Abstract
We propose a comprehensive approach for the analysis of real economy and government sector risk transmission to the banking system and apply it in ten Euro-Area countries from 2005 to 2017. A flexible methodology is developed to model banks’ assets according to the [...] Read more.
We propose a comprehensive approach for the analysis of real economy and government sector risk transmission to the banking system and apply it in ten Euro-Area countries from 2005 to 2017. A flexible methodology is developed to model banks’ assets according to the risk-adjusted balance sheet of the counterparts. The use of distance to distress as a popular risk metric shows that Contingent Claims Analysis underestimates banks risk in stable periods and overstates it during crisis. Furthermore, the approach succeeds in detecting spillovers from households, non-financial corporations and sovereign sectors: for the countries examined the main source of instability comes from the Non-Financial Corporation sector and its increased assets volatility. Full article
(This article belongs to the Special Issue Quantitative Finance)
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18 pages, 280 KiB  
Article
Population, Income, and Farmland Pricing in an Open Economy
by Huijian Dong and Xiaomin Guo
Int. J. Financial Stud. 2020, 8(4), 67; https://doi.org/10.3390/ijfs8040067 - 28 Oct 2020
Viewed by 2494
Abstract
Farmland valuation models usually incorporate local purchasing power as one of the pricing factors. A plausible rationale is that a larger population and higher income per capita imply increasing demand for agricultural products and farmland. In this paper, we study the relationship between [...] Read more.
Farmland valuation models usually incorporate local purchasing power as one of the pricing factors. A plausible rationale is that a larger population and higher income per capita imply increasing demand for agricultural products and farmland. In this paper, we study the relationship between the agricultural land prices, the regional population, and income per capita in an open economy setting in nominal and real variable terms using data from 1929 to 2018 at the state level. We show that in most areas of the United States, agricultural land prices are less affected by the state population or personal income. The valuation of agricultural land should not factor in the local purchasing power factors, with a few exceptions. Full article
(This article belongs to the Special Issue Quantitative Finance)
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