Special Issue "Measuring and Modelling Financial Risk and Derivatives"

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

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

Special Issue Editors

Prof. Dr. Chia-Lin Chang
Website
Guest Editor
Prof. Dr. Michael McAleer
Website
Guest Editor
Department of Finance, College of Management, Asia University, Taichung 41354, Taiwan Discipline of Business Analytics, University of Sydney Business School, Sydney 2006, Australia Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3062 Rotterdam, The Netherlands Department of Economic Analysis and ICAE, Complutense University of Madrid, 28040 Madrid, Spain Department of Mathematics and Statistics, University of Canterbury, Christchurch 8041, New Zealand Institute of Advanced Sciences, Yokohama National University, Yokohama 240-8501, Japan
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
Special Issues and Collections in MDPI journals
Dr. Wing-Keung Wong
Website
Guest Editor
Department of Finance, College of Management, Asia University, Wufeng, Taichung 407705, Taiwan
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; environmental research and public health
Special Issues and Collections in MDPI journals

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 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

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

Published Papers (12 papers)

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Research

Open AccessArticle
How Risky Are the Options? A Comparison with the Underlying Stock Using MaxVaR as a Risk Measure
Risks 2020, 8(3), 76; https://doi.org/10.3390/risks8030076 - 10 Jul 2020
Abstract
This paper investigates the risk exposure for options and proposes MaxVaR as an alternative risk measure which captures the risk better than Value-at-Risk especially. While VaR is a measure of end-of-horizon risk, MaxVaR captures the interim risk exposure of a position or a [...] Read more.
This paper investigates the risk exposure for options and proposes MaxVaR as an alternative risk measure which captures the risk better than Value-at-Risk especially. While VaR is a measure of end-of-horizon risk, MaxVaR captures the interim risk exposure of a position or a portfolio. MaxVaR is a more stringent risk measure as it assesses the risk during the risk horizon. For a 30-day maturity option, we find that MaxVaR can be 40% higher than VaR at a 5% significance level. It highlights the importance of MaxVaR as a risk measure and shows that the risk is vastly underestimated when VaR is used as the measure for risk. The sensitivity of MaxVaR with respect to option characteristics like moneyness, time to maturity and risk horizons at different significance levels are observed. Further, interestingly enough we find that the MaxVar to VaR ratio is higher for stocks than the options and we can surmise that stock returns are more volatile than options. For robustness, the study is carried out under different distributional assumptions on residuals and for different stock index options. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
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Open AccessArticle
Information Sharing, Bank Penetration and Tax Evasion in Emerging Markets
Risks 2020, 8(2), 38; https://doi.org/10.3390/risks8020038 - 20 Apr 2020
Abstract
Tax evasion, which is typically considered an illegal activity, is a critical problem and is considered a barrier to economic growth. A review of the literature shows that tax and social security contributions, regulations, public sector services, the quality of institutions and tax [...] Read more.
Tax evasion, which is typically considered an illegal activity, is a critical problem and is considered a barrier to economic growth. A review of the literature shows that tax and social security contributions, regulations, public sector services, the quality of institutions and tax compliance, play important roles in determining the degree to which firms attempt to evade taxes. Measuring tax evasion is problematic due to data requirements and inadequacies. Few tax evasion indices have been estimated but it appears that they cannot be used for international comparisons across countries. This important issue has largely been ignored in the literature, in particular for emerging markets. Consequently, this paper is conducted to develop a new tax evasion index (TEI) using the most substantial and recent data from the standardized World Bank Enterprises Survey 2006–2017. In addition, using the newly developed TEI, the paper examines the importance and contribution of information sharing and bank penetration to the degree of tax evasion in emerging markets. The paper uses a sample of 112 emerging markets from 2006–2017 and the Tobit model in estimation. The empirical findings from the paper indicate that the average TEI during the 2006–2017 period for emerging markets is 0.62, with a range of (0.25, 0.75). In addition, we find that information sharing and bank penetration negatively affect the degree of tax evasion, as proxied by the TEI, in emerging markets. The empirical results also confirm the view that large firms are considered to have adopted good tax compliance practices, while firms located in remote areas are more likely to evade taxes. Policy implications have emerged on the basis of the empirical findings from the paper. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
Open AccessArticle
Systematic Risk at the Industry Level: A Case Study of Australia
Risks 2020, 8(2), 36; https://doi.org/10.3390/risks8020036 - 13 Apr 2020
Abstract
The cornerstone of the capital asset pricing model (CAPM) lies with its beta. The question of whether or not beta is dead has attracted great attention from academics and practitioners in the last 50 years or so, and the debate is still ongoing. [...] Read more.
The cornerstone of the capital asset pricing model (CAPM) lies with its beta. The question of whether or not beta is dead has attracted great attention from academics and practitioners in the last 50 years or so, and the debate is still ongoing. Many empirical studies have been conducted to test the validity of beta within the framework of CAPM. However, it is a claim of this paper that beta at the industry level has been largely ignored in the current literature. This study is conducted to examine if beta, proxied for a systematic risk, should be considered valid in the application of the CAPM at the industry level for Australia using daily data on 2200 stocks listed on the Australian Securities Exchange from January 2007 to 31 December 2016. Various portfolio formations are utilized in this paper. General economic conditions such as interest rate, inflation, and GDP are examples of systematic risk. Findings from this study indicate that the selection of portfolio construction, estimation technique, and news about economic conditions significantly affects the view whether or not beta should be considered as a valid measure of systematic risk. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
Open AccessArticle
Do We Need Stochastic Volatility and Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns on FTSE
Risks 2020, 8(1), 12; https://doi.org/10.3390/risks8010012 - 01 Feb 2020
Abstract
The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV) of FTSE sampled at 5 min intervals [...] Read more.
The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV) of FTSE sampled at 5 min intervals taken from the Oxford Man Realised Library. Both models demonstrated comparable performance and were correlated to a similar extent with RV estimates when measured by ordinary least squares (OLS). However, a crude variant of Corsi’s (2009) Heterogeneous Autoregressive (HAR) model, applied to squared demeaned daily returns on FTSE, appeared to predict the daily RV of FTSE better than either of the two models. Quantile regressions suggest that all three methods capture tail behaviour similarly and adequately. This leads to the question of whether we need either of the two standard volatility models if the simple expedient of using lagged squared demeaned daily returns provides a better RV predictor, at least in the context of the sample. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
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Open AccessArticle
Market Risk Analysis of Energy in Vietnam
Risks 2019, 7(4), 112; https://doi.org/10.3390/risks7040112 - 04 Nov 2019
Cited by 2
Abstract
The purpose of this paper is to evaluate and estimate market risk for the ten major industries in Vietnam. The focus of the empirical analysis is on the energy sector, which has been designated as one of the four key industries, together with [...] Read more.
The purpose of this paper is to evaluate and estimate market risk for the ten major industries in Vietnam. The focus of the empirical analysis is on the energy sector, which has been designated as one of the four key industries, together with services, food, and telecommunications, targeted for economic development by the Vietnam Government through to 2020. The oil and gas industry is a separate energy-related major industry, and it is evaluated separately from energy. The data set is from 2009 to 2017, which is decomposed into two distinct sub-periods after the Global Financial Crisis (GFC), namely the immediate post-GFC (2009–2011) period and the normal (2012–2017) period, in order to identify the behavior of market risk for Vietnam’s major industries. For the stock market in Vietnam, the website used in this paper provided complete and detailed data for each stock, as classified by industry. Two widely used approaches to measure and analyze risk are used in the empirical analysis, namely Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). The empirical findings indicate that Energy and Pharmaceuticals are the least risky industries, whereas oil and gas and securities have the greatest risk. In general, there is strong empirical evidence that the four key industries display relatively low risk. For public policy, the Vietnam Government’s proactive emphasis on the targeted industries, including energy, to achieve sustainable economic growth and national economic development, seems to be working effectively. This paper presents striking empirical evidence that Vietnam’s industries have substantially improved their economic performance over the full sample, moving from relatively higher levels of market risk in the immediate post-GFC period to a lower risk environment in a normal period several years after the end of the calamitous GFC. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
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Open AccessArticle
Option Implied Stock Buy-Side and Sell-Side Market Depths
Risks 2019, 7(4), 108; https://doi.org/10.3390/risks7040108 - 28 Oct 2019
Abstract
This paper investigates option valuation when the underlying market suffers from illiquidity of price impact. Using option data, I infer trading activities and price impacts on the buy side and the sell side in the stock market from option prices across maturities. The [...] Read more.
This paper investigates option valuation when the underlying market suffers from illiquidity of price impact. Using option data, I infer trading activities and price impacts on the buy side and the sell side in the stock market from option prices across maturities. The finding displays that the stock market is active when the stock prices plummet, but becomes silent after the market crashes. In addition, the difference of option implied price impacts between the buy side and the sell side, which indicates asymmetric liquidity, increases with the time to maturity, especially on the day of the market crisis. Moreover, investors have different perspectives on the future liquidity after liquidity shocks when they are in a bull market or in a bear market according to the option implied price impact (or market depth) curves. I also calibrate three market indices simultaneously and reach the same conclusion that the three markets become erratic on the event date and calm down in the aftermath. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
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Open AccessArticle
Volatility Timing in CPF Investment Funds in Singapore: Do They Outperform Non-CPF Funds?
Risks 2019, 7(4), 106; https://doi.org/10.3390/risks7040106 - 20 Oct 2019
Cited by 1
Abstract
The purpose of this study is to examine the volatility-timing performance of Singapore-based funds under the Central Provident Fund (CPF) Investment Scheme and non-CPF linked funds by taking into account the currency risk effect on internationally managed funds. In particular, we empirically assess [...] Read more.
The purpose of this study is to examine the volatility-timing performance of Singapore-based funds under the Central Provident Fund (CPF) Investment Scheme and non-CPF linked funds by taking into account the currency risk effect on internationally managed funds. In particular, we empirically assess whether the funds under the CPF Investment Scheme outperform non-CPF funds by examining the volatility-timing performance associated with these funds. The volatility-timing ability of CPF funds will provide the CPF board with a new method for risk classification. We employ the GARCH models and modified factor models to capture the response of funds to market abnormal conditional volatility including the weekday effect. The SMB and HML factors for non-US based funds are constructed from stock market data to exclude the contribution of the size effect and the BE/ME effect. The results show that volatility timing is one of the factors contributing to the excess return of funds. However, funds’ volatility-timing seems to be country-specific. Most of the Japanese equity funds and global equity funds under the CPF Investment Scheme are found to have the ability of volatility timing. This finding contrasts with the existing studies on Asian, ex-Japan funds and Greater China funds. Moreover, there is no evidence that funds under the CPF Investment Scheme show a better group performance of volatility timing. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
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Open AccessArticle
A Study on Global Investors’ Criteria for Investment in the Local Currency Bond Markets Using AHP Methods: The Case of the Republic of Korea
Risks 2019, 7(4), 101; https://doi.org/10.3390/risks7040101 - 01 Oct 2019
Cited by 2
Abstract
Global investors’ investment in local currency bonds, especially Korea Treasury Bonds, has increased significantly since the mid-2000s, and their influence on bonds and financial markets has grown consistently. In this paper, we investigate global investor’s priority of decision factors in investing in Korea [...] Read more.
Global investors’ investment in local currency bonds, especially Korea Treasury Bonds, has increased significantly since the mid-2000s, and their influence on bonds and financial markets has grown consistently. In this paper, we investigate global investor’s priority of decision factors in investing in Korea Treasury Bonds by distributing a pairwise comparative survey to experts and analyzing the results using the analytical hierarchy process technique. For analysis, we created model frames with experts in the field of investment based on literature analysis, selected survey participants by considering their institution of their employment, work experience and region, and obtained responses. We find that investors with short-term investment propensities are more sensitive to international and domestic factors and less to risk factors, and more heavily influenced by U.S. dollar funding conditions. On the other hand, investors with long-term investment tendencies are found to be more sensitive to international and risk factors as opposed to domestic factors, and influenced by: global policy rate decisions and fiscal soundness, sovereign credit rating, possible global economic recession, and geographical risks. Our findings not only contribute to enhancing investors’ understanding of the Korean bond market by discussing consensus among investors, but also provide policy implications for Korean government policymakers who need stable and sustained funding. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
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Open AccessArticle
Determining Distribution for the Product of Random Variables by Using Copulas
Risks 2019, 7(1), 23; https://doi.org/10.3390/risks7010023 - 25 Feb 2019
Cited by 6
Abstract
Determining distributions of the functions of random variables is one of the most important problems in statistics and applied mathematics because distributions of functions have wide range of applications in numerous areas in economics, finance, risk management, science, and others. However, most studies [...] Read more.
Determining distributions of the functions of random variables is one of the most important problems in statistics and applied mathematics because distributions of functions have wide range of applications in numerous areas in economics, finance, risk management, science, and others. However, most studies only focus on the distribution of independent variables or focus on some common distributions such as multivariate normal joint distributions for the functions of dependent random variables. To bridge the gap in the literature, in this paper, we first derive the general formulas to determine both density and distribution of the product for two or more random variables via copulas to capture the dependence structures among the variables. We then propose an approach combining Monte Carlo algorithm, graphical approach, and numerical analysis to efficiently estimate both density and distribution. We illustrate our approach by examining the shapes and behaviors of both density and distribution of the product for two log-normal random variables on several different copulas, including Gaussian, Student-t, Clayton, Gumbel, Frank, and Joe Copulas, and estimate some common measures including Kendall’s coefficient, mean, median, standard deviation, skewness, and kurtosis for the distributions. We found that different types of copulas affect the behavior of distributions differently. In addition, we also discuss the behaviors via all copulas above with the same Kendall’s coefficient. Our results are the foundation of any further study that relies on the density and cumulative probability functions of product for two or more random variables. Thus, the theory developed in this paper is useful for academics, practitioners, and policy makers. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
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Open AccessArticle
The Value-At-Risk Estimate of Stock and Currency-Stock Portfolios’ Returns
Risks 2018, 6(4), 133; https://doi.org/10.3390/risks6040133 - 16 Nov 2018
Cited by 1
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
Risks 2018, 6(4), 105; https://doi.org/10.3390/risks6040105 - 22 Sep 2018
Cited by 2
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
Risks 2018, 6(3), 94; https://doi.org/10.3390/risks6030094 - 10 Sep 2018
Cited by 3
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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