Special Issue "Measuring and Modelling Financial Risk and Derivatives"

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

Deadline for manuscript submissions: 31 July 2019

Special Issue Editors

Guest Editor
Prof. Chia-Lin Chang

Department of Applied Economics, College of Agriculture and Natural Resources, National Chung Hsing University, Taiwan
Website | E-Mail
Interests: applied econometrics, financial econometrics, financial economics, finance, energy economics and finance, time series analysis, forecasting, empirical industrial organisation, risk management
Guest Editor
Prof. Dr. Michael McAleer

University Chair Professor, Department of Finance, College of Management, Asia University, Wufeng 41354, Taiwan
Website | E-Mail
Phone: +04-2332-3456 (ext. 1837)
Interests: theoretical and applied econometrics; financial econometrics; financial economics; finance, theoretical and applied statistics; time series analysis; forecasting; risk management; energy economics and finance; applied mathematics
Guest Editor
Prof. Dr. Wing-Keung Wong

Department of Finance, College of Management, Asia University, Wufeng, Taichung, Taiwan
Website | E-Mail
Interests: financial economics; econometrics; mathematical finance; mathematical economics; equity analysis; investment theory; risk management; behavioral finance; behavioral economics; operational research; stochastic dominance theory; time series analysis; Bayesian theory and decision theory

Special Issue Information

Dear Colleagues,

Risk is prevalent in any aspect of human existence.

Understanding, measuring, modelling and forecasting financial risk is paramount in every aspect of modern living, including banking, stocks, bonds, currencies, and related financial derivatives.

Measuring and modelling financial risk is demanding, especially in light of the number of financial stocks and related commodities that can be and are produced quickly in real time.

The number and complexity of financial derivatives that arise from basic spot or cash indexes contribute to the difficulties associated with understanding, measuring, analyzing, modelling, evaluating and forecasting financial risk.

Using different risk measures could compare the performances of different variables through the analysis of empirical real-world data.

A Special Issue of Risks on “Measuring and Modelling Financial Risk and Derivatives” will be devoted to advancements in the analytical, econometric, mathematical and statistical development of risk measures, with special reference to derivatives such as futures, options, VIX, ETFs, and related financial products.

It is envisaged that the financial commodities and their associated derivatives will accommodate financial products, energy products, green energy and the associated agricultural products to produce bio-ethanol and bio-diesel, renewable and sustainable energy products, and the associated carbon emissions and carbon spot and futures prices.

The Special Issue will encompass innovative theoretical developments, challenging and exciting practical applications, and interesting case studies in the analysis of financial risk and derivatives in finance and cognate disciplines.

The Guest Editors invite innovative contributions of original research articles in the theory, practice and applications of financial risk measures, models, portfolio analysis, and financial derivatives across a wide range of disciplines.

All submissions must contain original unpublished work not being considered for publication elsewhere.

Prof. Wing-Keung  Wong
Prof. Chia-Lin Chang
Prof. Michael McAleer
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 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. Risks 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 350 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

  • modelling and forecasting financial risk
  • banking
  • stocks
  • bonds
  • currencies
  • financial derivatives
  • futures
  • options
  • VIX
  • ETFs

Published Papers (3 papers)

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Research

Open AccessArticle The Value-At-Risk Estimate of Stock and Currency-Stock Portfolios’ Returns
Received: 14 August 2018 / Revised: 10 November 2018 / Accepted: 12 November 2018 / Published: 16 November 2018
Cited by 1 | PDF Full-text (564 KB) | HTML Full-text | XML Full-text
Abstract
This study utilizes the seven bivariate generalized autoregressive conditional heteroskedasticity (GARCH) models to forecast the out-of-sample value-at-risk (VaR) of 21 stock portfolios and seven currency-stock portfolios with three weight combinations, and then employs three accuracy tests and one efficiency test to evaluate the [...] Read more.
This study utilizes the seven bivariate generalized autoregressive conditional heteroskedasticity (GARCH) models to forecast the out-of-sample value-at-risk (VaR) of 21 stock portfolios and seven currency-stock portfolios with three weight combinations, and then employs three accuracy tests and one efficiency test to evaluate the VaR forecast performance for the above models. The seven models are constructed by four types of bivariate variance-covariance specifications and two approaches of parameters estimates. The four types of bivariate variance-covariance specifications are the constant conditional correlation (CCC), asymmetric and symmetric dynamic conditional correlation (ADCC and DCC), and the BEKK, whereas the two types of approach include the standard and non-standard approaches. Empirical results show that, regarding the accuracy tests, the VaR forecast performance of stock portfolios varies with the variance-covariance specifications and the approaches of parameters estimate, whereas it does not vary with the weight combinations of portfolios. Conversely, the VaR forecast performance of currency-stock portfolios is almost the same for all models and still does not vary with the weight combinations of portfolios. Regarding the efficiency test via market risk capital, the NS-BEKK model is the most suitable model to be used in the stock and currency-stock portfolios for bank risk managers irrespective of the weight combination of portfolios. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
Open AccessArticle Long Run Returns Predictability and Volatility with Moving Averages
Received: 13 September 2018 / Revised: 20 September 2018 / Accepted: 21 September 2018 / Published: 22 September 2018
Cited by 1 | PDF Full-text (3254 KB) | HTML Full-text | XML Full-text
Abstract
This paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affects financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and [...] Read more.
This paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affects financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The important issue regarding the predictability of returns is assessed. It is found that performance improves, on average, when the rolling window is expanded and the data frequency is low. However, when the size of the rolling window reaches three years, the frequency loses its significance and all frequencies considered produce similar financial performance. Therefore, the results support stock returns predictability in the long run. The procedure takes account of the issues of variable persistence as we use only returns in the analysis. Therefore, we use the performance of MA rules as an instrument for testing returns predictability in financial stock markets. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
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Open AccessArticle Country Risk Ratings and Stock Market Returns in Brazil, Russia, India, and China (BRICS) Countries: A Nonlinear Dynamic Approach
Received: 5 August 2018 / Revised: 5 September 2018 / Accepted: 7 September 2018 / Published: 10 September 2018
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Abstract
This study examines the linkages between Brazil, Russia, India, and China (BRICS) stock market returns, country risk ratings, and international factors via Non-linear Auto Regressive Distributed Lags models (NARDL) that allow for testing the asymmetric effects of changes in country risk ratings on [...] Read more.
This study examines the linkages between Brazil, Russia, India, and China (BRICS) stock market returns, country risk ratings, and international factors via Non-linear Auto Regressive Distributed Lags models (NARDL) that allow for testing the asymmetric effects of changes in country risk ratings on stock market returns. We show that BRICS countries exhibit quite a degree of heterogeneity in the interaction of their stock market returns with country-specific political, financial, and economic risk ratings. Positive and negative rating changes in some BRICS countries are found to have significant implications for both local stock market returns, as well as commodity price dynamics. While the commodity market acts as a catalyst for these emerging stock markets in the long-run, we also observe that negative changes in the country risk ratings generally command a higher impact on stock returns, implying the greater impact of bad news on market dynamics. Our findings suggest that not all BRICS nations are the same in terms of how they react to ratings changes and how they interact with global market variables. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
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