Next Issue
Previous Issue

Table of Contents

J. Risk Financial Manag., Volume 11, Issue 2 (June 2018)

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
View options order results:
result details:
Displaying articles 1-15
Export citation of selected articles as:
Open AccessArticle Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models
J. Risk Financial Manag. 2018, 11(2), 30; https://doi.org/10.3390/jrfm11020030
Received: 21 April 2018 / Revised: 1 June 2018 / Accepted: 7 June 2018 / Published: 9 June 2018
PDF Full-text (4818 KB) | HTML Full-text | XML Full-text
Abstract
Precise modeling and forecasting of the volatility of energy futures is vital to structuring trading strategies in spot markets for risk managers. Capturing conditional distribution, fat tails and price spikes properly is crucial to the correct measurement of risk. This paper is an
[...] Read more.
Precise modeling and forecasting of the volatility of energy futures is vital to structuring trading strategies in spot markets for risk managers. Capturing conditional distribution, fat tails and price spikes properly is crucial to the correct measurement of risk. This paper is an attempt to model volatility of energy futures under different distributions. In empirical analysis, we estimate the volatility of Natural Gas Futures, Brent Oil Futures and Heating Oil Futures through GARCH and APARCH models under gev, gat and alpha-stable distributions. We also applied various VaR analyses, Gaussian, Historical and Modified (Cornish-Fisher) VaR, for each variable. Results suggest that the APARCH model largely outperforms the GARCH model, and gat distribution performs better in modeling fat tails in returns. Our results also indicate that the correct volatility level, in gat distribution, is higher than those suggested under normal distribution with rates of 56%, 45% and 67% for Natural Gas Futures, Brent Oil Futures and Heating Oil Futures, respectively. Implemented VaR analyses also support this conclusion. Additionally, VaR test results demonstrate that energy futures display riskier behavior than S&P 500 returns. Our findings suggest that for optimum risk management and trading strategies, risk managers should consider alternative distributions in their models. According to our results, prices in energy markets are wilder than the perception of normal distribution. In this regard, regulators and policy makers should enhance transparency and competitiveness in the energy markets to protect consumers. Full article
(This article belongs to the Special Issue Commodity Market Finance and Microstructure)
Figures

Figure 1a

Open AccessArticle Leverage and Volatility Feedback Effects and Conditional Dependence Index: A Nonparametric Study
J. Risk Financial Manag. 2018, 11(2), 29; https://doi.org/10.3390/jrfm11020029
Received: 5 April 2018 / Revised: 30 May 2018 / Accepted: 4 June 2018 / Published: 8 June 2018
PDF Full-text (2459 KB) | HTML Full-text | XML Full-text
Abstract
This paper studies the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method. Specifically, we propose a conditional dependence index to investigate how the dependence between the two series varies across different
[...] Read more.
This paper studies the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method. Specifically, we propose a conditional dependence index to investigate how the dependence between the two series varies across different segments of the market return distribution. We find that: (a) the two series exhibit strong, negative, extreme tail dependence; (b) the negative dependence is stronger in extreme bearish markets than in extreme bullish markets; (c) the dependence gradually weakens as the market return moves toward the center of its distribution, or in quiet markets. The unique dependence structure supports the VIX as a barometer of markets’ mood in general. Moreover, applying the proposed method to the S&P 500 returns and the implied variance (VIX2), we find that the nonparametric leverage effect is much stronger than the nonparametric volatility feedback effect, although, in general, both effects are weaker than the dependence relation between the market returns and the log-increments of the VIX. Full article
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
Figures

Figure 1

Open AccessArticle Customer Preferences and Implicit Tradeoffs in Accident Scenarios for Self-Driving Vehicle Algorithms
J. Risk Financial Manag. 2018, 11(2), 28; https://doi.org/10.3390/jrfm11020028
Received: 18 April 2018 / Revised: 30 May 2018 / Accepted: 30 May 2018 / Published: 4 June 2018
PDF Full-text (894 KB) | HTML Full-text | XML Full-text
Abstract
The development of self-driving vehicles is proceeding rapidly and with significant investment of resources. However, a full-scale deployment is not imminent. Among the challenges self-driving vehicles are facing, they will have to navigate complex ethical challenges. The algorithms governing their behavior will have
[...] Read more.
The development of self-driving vehicles is proceeding rapidly and with significant investment of resources. However, a full-scale deployment is not imminent. Among the challenges self-driving vehicles are facing, they will have to navigate complex ethical challenges. The algorithms governing their behavior will have to decide how to steer them in situations where accidents cannot be avoided. In some of these situations they will have to decide which of several potential parties to injure in the process. We investigate the preferences of Swiss customers for this decision by forcing a selection between simplified scenarios where a given number of car passengers or a given number of pedestrians will be killed in the accident. Both passengers and pedestrians can be adults or children. The passengers are explicitly identified as the respondent themselves and their family. While children are implicitly valued higher than adults, Swiss customers value passengers and pedestrians implicitly roughly equally, and assign increasingly higher marginal values to additional people, both passengers and pedestrians. These results seem to partially contradict similar studies conducted in other countries and recent statements by automotive companies, potentially indicating the need to adapt both corporate communications and steering algorithms in different geographies. Full article
Figures

Figure 1

Open AccessArticle Credit Rating and Pricing: Poles Apart
J. Risk Financial Manag. 2018, 11(2), 27; https://doi.org/10.3390/jrfm11020027
Received: 3 March 2018 / Revised: 3 May 2018 / Accepted: 12 May 2018 / Published: 23 May 2018
PDF Full-text (460 KB) | HTML Full-text | XML Full-text
Abstract
Corporate credit ratings remove the information asymmetry between lenders and borrowers to find an equilibrium price. Structured finance ratings, however, are informationally insufficient because the systematic risk of equally rated assets can vary substantially. As I demonstrate in a Monte Carlo analysis, highly-rated
[...] Read more.
Corporate credit ratings remove the information asymmetry between lenders and borrowers to find an equilibrium price. Structured finance ratings, however, are informationally insufficient because the systematic risk of equally rated assets can vary substantially. As I demonstrate in a Monte Carlo analysis, highly-rated structured finance bonds can exhibit far higher non-linear systematic risks than lowly-rated corporate bonds. I value credit instruments under a four-moment CAPM, between and within some markets there is no one-to-one relation between expected loss (rating) and credit spread (pricing). The linear CAPM beta is insufficient, buyers and sellers need also the same information on non-linear risk to have an equilibrium. Full article
(This article belongs to the Special Issue Risk and Financial Instability)
Figures

Figure 1

Open AccessArticle The Wolf and the Caribou: Coexistence of Decentralized Economies and Competitive Markets
J. Risk Financial Manag. 2018, 11(2), 26; https://doi.org/10.3390/jrfm11020026
Received: 31 March 2018 / Revised: 11 May 2018 / Accepted: 16 May 2018 / Published: 23 May 2018
PDF Full-text (638 KB) | HTML Full-text | XML Full-text
Abstract
Starting with BitTorrent and then Bitcoin, decentralized technologies have been on the rise over the last 15+ years, gaining significant momentum in the last 2+ years with the advent of platform ecosystems such as the Blockchain platform Ethereum. New projects have evolved from
[...] Read more.
Starting with BitTorrent and then Bitcoin, decentralized technologies have been on the rise over the last 15+ years, gaining significant momentum in the last 2+ years with the advent of platform ecosystems such as the Blockchain platform Ethereum. New projects have evolved from decentralized games to marketplaces to open funding models to decentralized autonomous organizations. The hype around cryptocurrency and the valuation of innovative projects drove the market cap of cryptocurrencies to over a trillion dollars at one point in 2017. These high valued technologies are now enabling something new: globally scaled and decentralized business models. Despite their valuation and the hype, these new business ecosystems are frail. This is not only because the underlying technology is rapidly evolving, but also because competitive markets see a profit opportunity in exponential cryptocurrency returns. This extracts value from these ecosystems, which could lead to their collapse, if unchecked. In this paper, we explore novel ways for decentralized economies to protect themselves from, and coexist with, competitive markets at a global scale utilizing decentralized technologies such as Blockchain. Full article
(This article belongs to the Special Issue Digital and Internet Finance)
Figures

Figure 1

Open AccessFeature PaperArticle Mean-Variance Portfolio Selection in a Jump-Diffusion Financial Market with Common Shock Dependence
J. Risk Financial Manag. 2018, 11(2), 25; https://doi.org/10.3390/jrfm11020025
Received: 3 April 2018 / Revised: 9 May 2018 / Accepted: 14 May 2018 / Published: 16 May 2018
PDF Full-text (280 KB) | HTML Full-text | XML Full-text
Abstract
This paper considers the optimal investment problem in a financial market with one risk-free asset and one jump-diffusion risky asset. It is assumed that the insurance risk process is driven by a compound Poisson process and the two jump number processes are correlated
[...] Read more.
This paper considers the optimal investment problem in a financial market with one risk-free asset and one jump-diffusion risky asset. It is assumed that the insurance risk process is driven by a compound Poisson process and the two jump number processes are correlated by a common shock. A general mean-variance optimization problem is investigated, that is, besides the objective of terminal condition, the quadratic optimization functional includes also a running penalizing cost, which represents the deviations of the insurer’s wealth from a desired profit-solvency goal. By solving the Hamilton-Jacobi-Bellman (HJB) equation, we derive the closed-form expressions for the value function, as well as the optimal strategy. Moreover, under suitable assumption on model parameters, our problem reduces to the classical mean-variance portfolio selection problem and the efficient frontier is obtained. Full article
Figures

Figure 1

Open AccessArticle Credit Ratings and Liquidity Risk for the Optimization of Debt Maturity Structure
J. Risk Financial Manag. 2018, 11(2), 24; https://doi.org/10.3390/jrfm11020024
Received: 19 March 2018 / Revised: 6 May 2018 / Accepted: 8 May 2018 / Published: 11 May 2018
PDF Full-text (603 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The purpose of this study is to examine the relationship between credit rating scales and debt maturity choices. A liquidity hypothesis is used to formulate the testable proposition and conceptual framework. Generalized linear model (GLM) and pooled ordinary least square (OLS) are utilized
[...] Read more.
The purpose of this study is to examine the relationship between credit rating scales and debt maturity choices. A liquidity hypothesis is used to formulate the testable proposition and conceptual framework. Generalized linear model (GLM) and pooled ordinary least square (OLS) are utilized by SAS programming to test the proposed hypothesis. Other different estimation techniques are also used for robust evidence. Results suggest that companies with high and low ratings have a shorter debt maturity. Companies with medium ratings have longer debt maturity structure. Liquidity shows a negative association with longer debt maturity structure. It is evident that at high rating scale with high liquidity, and at lower rating scales with lower liquidity firms have a shorter debt maturity. Mid rated firms with a low probability of refinancing risk show longer debt maturity structure. Considering refinancing risk by Asian companies make the nonlinear relationship between credit ratings and debt maturity choices. Results suggest the importance of credit ratings for the optimization of debt maturity structure of Asian firms, which was totally overlooked by the past studies. The findings of this study are consistent with the liquidity hypothesis. The findings also motivating financial managers and investors to consider credit ratings as a measure of financial constraints. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling)
Figures

Figure 1

Open AccessArticle Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis
J. Risk Financial Manag. 2018, 11(2), 23; https://doi.org/10.3390/jrfm11020023
Received: 10 April 2018 / Revised: 5 May 2018 / Accepted: 8 May 2018 / Published: 10 May 2018
Cited by 1 | PDF Full-text (875 KB) | HTML Full-text | XML Full-text
Abstract
We use the GARCH-MIDAS model to extract the long- and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We
[...] Read more.
We use the GARCH-MIDAS model to extract the long- and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We find that S&P 500 realized volatility has a negative and highly significant effect on long-term Bitcoin volatility. The finding is atypical for volatility co-movements across financial markets. Moreover, we find that the S&P 500 volatility risk premium has a significantly positive effect on long-term Bitcoin volatility. Finally, we find a strong positive association between the Baltic dry index and long-term Bitcoin volatility. This result shows that Bitcoin volatility is closely linked to global economic activity. Overall, our findings can be used to construct improved forecasts of long-term Bitcoin volatility. Full article
(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)
Figures

Figure 1

Open AccessArticle Investigation of the Financial Stability of S&P 500 Using Realized Volatility and Stock Returns Distribution
J. Risk Financial Manag. 2018, 11(2), 22; https://doi.org/10.3390/jrfm11020022
Received: 2 April 2018 / Revised: 20 April 2018 / Accepted: 26 April 2018 / Published: 28 April 2018
PDF Full-text (1571 KB) | HTML Full-text | XML Full-text
Abstract
In this work, the financial data of 377 stocks of Standard & Poor’s 500 Index (S&P 500) from the years 1998–2012 with a 250-day time window were investigated by measuring realized stock returns and realized volatility. We examined the normal distribution and frequency
[...] Read more.
In this work, the financial data of 377 stocks of Standard & Poor’s 500 Index (S&P 500) from the years 1998–2012 with a 250-day time window were investigated by measuring realized stock returns and realized volatility. We examined the normal distribution and frequency distribution for both daily stock returns and volatility. We also determined the beta-coefficient and correlation among the stocks for 15 years and found that, during the crisis period, the beta-coefficient between the market index and stock’s prices and correlation among stock’s prices increased remarkably and decreased during the non-crisis period. We compared the stock volatility and stock returns for specific time periods i.e., non-crisis, before crisis and during crisis year in detail and found that the distribution behaviors of stock return prices has a better long-term effect that allows predictions of near-future market behavior than realized volatility of stock returns. Our detailed statistical analysis provides a valuable guideline for both researchers and market participants because it provides a significantly clearer comparison of the strengths and weaknesses of the two methods. Full article
(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)
Figures

Graphical abstract

Open AccessArticle Testing for Causality-In-Mean and Variance between the UK Housing and Stock Markets
J. Risk Financial Manag. 2018, 11(2), 21; https://doi.org/10.3390/jrfm11020021
Received: 24 March 2018 / Revised: 6 April 2018 / Accepted: 6 April 2018 / Published: 26 April 2018
PDF Full-text (4013 KB) | HTML Full-text | XML Full-text
Abstract
This paper employs the two-step procedure to analyze the causality-in-mean and causality-in-variance between the housing and stock markets of the UK. The empirical findings make two key contributions. First, although previous studies have indicated a one-way causal relation from the housing market to
[...] Read more.
This paper employs the two-step procedure to analyze the causality-in-mean and causality-in-variance between the housing and stock markets of the UK. The empirical findings make two key contributions. First, although previous studies have indicated a one-way causal relation from the housing market to the stock market in the UK, this paper discovered a two-way causal relation between them. Second, a causality-in-variance as well as a causality-in-mean was detected from the housing market to the stock market. Full article
(This article belongs to the Special Issue Empirical Finance)
Figures

Graphical abstract

Open AccessEditorial Editorial Note: Review Papers for Journal of Risk and Financial Management (JRFM)
J. Risk Financial Manag. 2018, 11(2), 20; https://doi.org/10.3390/jrfm11020020
Received: 24 April 2018 / Revised: 24 April 2018 / Accepted: 24 April 2018 / Published: 25 April 2018
PDF Full-text (138 KB) | HTML Full-text | XML Full-text
Abstract
The Journal of Risk and Financial Management (JRFM) was inaugurated in 2008 and has continued publishing successfully with Volume 11 in 2018. Since the journal was established, JRFM has published in excess of 110 topical and interesting theoretical and empirical papers in financial
[...] Read more.
The Journal of Risk and Financial Management (JRFM) was inaugurated in 2008 and has continued publishing successfully with Volume 11 in 2018. Since the journal was established, JRFM has published in excess of 110 topical and interesting theoretical and empirical papers in financial economics, financial econometrics, banking, finance, mathematical finance, statistical finance, accounting, decision sciences, information management, tourism economics and finance, international rankings of journals in financial economics, and bibliometric rankings of journals in cognate disciplines. Papers published in the journal range from novel technical and theoretical papers to innovative empirical contributions. The journal wishes to encourage critical review papers on topical subjects in any of the topics mentioned above in financial economics and in cognate disciplines. Full article
(This article belongs to the Special Issue Review Papers for Journal of Risk and Financial Management (JRFM))
Open AccessArticle Exchange Rate Effects on International Commercial Trade Competitiveness
J. Risk Financial Manag. 2018, 11(2), 19; https://doi.org/10.3390/jrfm11020019
Received: 27 January 2018 / Revised: 26 March 2018 / Accepted: 30 March 2018 / Published: 8 April 2018
PDF Full-text (2928 KB) | HTML Full-text | XML Full-text
Abstract
This study is meant to be an evaluation sustained by theoretical and empirical considerations of the exchange rate impact on international commercial trade competitiveness. In this respect, the study aims to find how the exchange rate influences Romanian competitiveness through assessing the effects
[...] Read more.
This study is meant to be an evaluation sustained by theoretical and empirical considerations of the exchange rate impact on international commercial trade competitiveness. In this respect, the study aims to find how the exchange rate influences Romanian competitiveness through assessing the effects generated on exports and imports. The main purpose of the study is to assess the complex action of the exchange rate on international commercial trade competitiveness in contemporaneity and the connections between these variables. The empirical part contains a regression analysis where exports and imports are dependent variables influenced by a series of determinants. Full article
(This article belongs to the Special Issue Analysis of Global Financial Markets)
Figures

Figure 1

Open AccessArticle Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs. GARCH
J. Risk Financial Manag. 2018, 11(2), 18; https://doi.org/10.3390/jrfm11020018
Received: 16 March 2018 / Revised: 3 April 2018 / Accepted: 3 April 2018 / Published: 5 April 2018
PDF Full-text (446 KB) | HTML Full-text | XML Full-text
Abstract
This study compares the performance of several methods to calculate the Value-at-Risk of the six main ASEAN stock markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample performance is analyzed by various backtesting procedures. We find that simpler
[...] Read more.
This study compares the performance of several methods to calculate the Value-at-Risk of the six main ASEAN stock markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample performance is analyzed by various backtesting procedures. We find that simpler models fail to produce sufficient Value-at-Risk forecasts, which appears to stem from several econometric properties of the return distributions. With stochastic volatility models, we obtain better Value-at-Risk forecasts compared to GARCH. The quality varies over forecasting horizons and across markets. This indicates that, despite a regional proximity and homogeneity of the markets, index volatilities are driven by different factors. Full article
(This article belongs to the Special Issue Trends in Emerging Markets Finance, Institutions and Money)
Figures

Figure 1

Open AccessArticle Equity Options During the Shorting Ban of 2008
J. Risk Financial Manag. 2018, 11(2), 17; https://doi.org/10.3390/jrfm11020017
Received: 11 February 2018 / Revised: 16 March 2018 / Accepted: 27 March 2018 / Published: 31 March 2018
PDF Full-text (988 KB) | HTML Full-text | XML Full-text
Abstract
The Securities and Exchange Commission’s 2008 emergency order introduced a shorting ban of some 800 financials traded in the US. This paper provides an empirical analysis of the options market around the ban period. Using transaction level data from OPRA (The Options Price
[...] Read more.
The Securities and Exchange Commission’s 2008 emergency order introduced a shorting ban of some 800 financials traded in the US. This paper provides an empirical analysis of the options market around the ban period. Using transaction level data from OPRA (The Options Price Reporting Authority), we study the options volume, spreads, pricing measures and option trade volume informativeness during the ban. We also consider the put–call parity relationship. While mostly statistically significant, economic magnitudes of our results suggest that the impact of the ban on the equity options market was likely not as dramatic as initially thought. Full article
(This article belongs to the Special Issue Empirical Asset Pricing)
Figures

Figure 1a

Open AccessArticle Contagion Effect of Natural Disaster and Financial Crisis Events on International Stock Markets
J. Risk Financial Manag. 2018, 11(2), 16; https://doi.org/10.3390/jrfm11020016
Received: 19 February 2018 / Revised: 22 March 2018 / Accepted: 26 March 2018 / Published: 30 March 2018
PDF Full-text (351 KB) | HTML Full-text | XML Full-text
Abstract
In the contemporary world bustling with global trade, a natural disaster or financial crisis in one country (or region) can cause substantial economic losses and turbulence in the local financial markets, which may then affect the economic activities and financial assets of other
[...] Read more.
In the contemporary world bustling with global trade, a natural disaster or financial crisis in one country (or region) can cause substantial economic losses and turbulence in the local financial markets, which may then affect the economic activities and financial assets of other countries (or regions). This study focuses on the major natural disasters that occurred worldwide during the last decade, especially those in the Asia–Pacific region, and the economic effects of global financial crises. The heteroscedasticity bias correlation coefficient method and exponential general autoregressive conditional heteroscedasticity model are employed to compare the contagion effect in the stock markets of the initiating country on other countries, determining whether economically devastating factors have contagion or spillover effects on other countries. The empirical results indicate that among all the natural disasters considered, the 2008 Sichuan Earthquake in China caused the most substantial contagion effect in the stock markets of neighboring Asian countries. Regarding financial crises, the financial tsunami triggered by the secondary mortgage fallout in the United States generated the strongest contagion effect on the stock markets of developing and emerging economies. When building a diversified global investment portfolio, investors should be aware of the risks of major natural disasters and financial incidents. Full article
(This article belongs to the Special Issue Advances on Volatility Modeling and Forecasting)
Back to Top