Open AccessArticle
Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function
J. Risk Financial Manag. 2016, 9(4), 13; doi:10.3390/jrfm9040013 -
Abstract
Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied
[...] Read more.
Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied in the fields of statistics and machine learning. In this paper a novel fuzzy support vector machine (SVM) credit scoring model is proposed for credit risk analysis, in which fuzzy membership is adopted to indicate different contribution of each input point to the learning of SVM classification hyperplane. Considering the methodological consistency, support vector data description (SVDD) is introduced to construct the fuzzy membership function and to reduce the effect of outliers and noises. The SVDD-based fuzzy SVM model is tested against the traditional fuzzy SVM on two real-world datasets and the research results confirm the effectiveness of the presented method. Full article
Figures

Figure 1

Open AccessArticle
The Design and Risk Management of Structured Finance Vehicles
J. Risk Financial Manag. 2016, 9(4), 12; doi:10.3390/jrfm9040012 -
Abstract
Special investment vehicles (SIVs), extremely popular financial structures for the creation of highly-rated tranched securities, experienced spectacular demise in the 2007-2008 financial crisis. These financial vehicles epitomize the shadow banking sector, characterized by high leverage, undiversified asset pools, and long-dated assets supported by
[...] Read more.
Special investment vehicles (SIVs), extremely popular financial structures for the creation of highly-rated tranched securities, experienced spectacular demise in the 2007-2008 financial crisis. These financial vehicles epitomize the shadow banking sector, characterized by high leverage, undiversified asset pools, and long-dated assets supported by short-term debt, thus bearing material rollover risk on their liabilities which led to defeasance. This paper models these vehicles, and shows that imposing leverage risk control triggers can be optimal for all capital providers, though they may not always be appropriate. The efficacy of these risk controls varies depending on anticipated asset volatility and fire-sale discounts on defeasance. Despite risk management controls, we show that a high failure rate is inherent in the design of these vehicles, and may be mitigated to some extent by including contingent capital provisions in the ex-ante covenants. Post the recent subprime financial crisis, we inform the creation of safer SIVs in structured finance, and propose avenues of mitigating risks faced by senior debt through deleveraging policies in the form of leverage risk controls and contingent capital. Full article
Open AccessArticle
Portfolios Dominating Indices: Optimization with Second-Order Stochastic Dominance Constraints vs. Minimum and Mean Variance Portfolios
J. Risk Financial Manag. 2016, 9(4), 11; doi:10.3390/jrfm9040011 -
Abstract
The paper compares portfolio optimization with the Second-Order Stochastic Dominance (SSD) constraints with mean-variance and minimum variance portfolio optimization. As a distribution-free decision rule, stochastic dominance takes into account the entire distribution of return rather than some specific characteristic, such as variance. The
[...] Read more.
The paper compares portfolio optimization with the Second-Order Stochastic Dominance (SSD) constraints with mean-variance and minimum variance portfolio optimization. As a distribution-free decision rule, stochastic dominance takes into account the entire distribution of return rather than some specific characteristic, such as variance. The paper is focused on practical applications of the portfolio optimization and uses the Portfolio Safeguard (PSG) package, which has precoded modules for optimization with SSD constraints, mean-variance and minimum variance portfolio optimization. We have done in-sample and out-of-sample simulations for portfolios of stocks from the Dow Jones, S&P 100 and DAX indices. The considered portfolios’ SSD dominate the Dow Jones, S&P 100 and DAX indices. Simulation demonstrated a superior performance of portfolios with SD constraints, versus mean-variance and minimum variance portfolios. Full article
Figures

Figure 1

Open AccessArticle
On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?
J. Risk Financial Manag. 2016, 9(3), 10; doi:10.3390/jrfm9030010 -
Abstract
This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight close-to-open price
[...] Read more.
This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight close-to-open price variation. The benchmark is the bivariate VaR modeling approach proposed by Ahoniemi et al. that constructs the forecast at the market close instead and, accordingly, it models separately the daytime and overnight return processes and their covariance. For a small cap portfolio, the bivariate VaR approach affords superior predictive ability than the ex post overnight VaR approach whereas for a large cap portfolio the results are reversed. The contrast indicates that price discovery at the market open is less efficient for small capitalization, thinly traded stocks. Full article
Figures

Open AccessArticle
The Nexus between Social Capital and Bank Risk Taking
J. Risk Financial Manag. 2016, 9(3), 9; doi:10.3390/jrfm9030009 -
Abstract
This study explores social capital and its relevance to bank risk taking across countries. Our empirical results show that the levels of bank risk taking are lower in countries with higher levels of social capital, and that the impact of social capital is
[...] Read more.
This study explores social capital and its relevance to bank risk taking across countries. Our empirical results show that the levels of bank risk taking are lower in countries with higher levels of social capital, and that the impact of social capital is mainly reflected by the reduced value of the standard deviation of return on assets. Moreover, the impact of social capital is found to be weaker when the legal system lacks strength. Furthermore, the study considers the impacts of social capital of the banks’ largest shareholders in these countries and finds that high levels of social capital present in these countries exert a negative effect on bank risk taking, but the effect is not strongly significant. Full article
Figures

Open AccessArticle
The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective
J. Risk Financial Manag. 2016, 9(3), 8; doi:10.3390/jrfm9030008 -
Abstract
Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk
[...] Read more.
Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk estimates, obtained from the Realized Range Volatility, corrected for microstructure noise and jumps, and quantile regression methods, we evaluate the forecasting implications of the equity risk determinants in different volatility states and, without distributional assumptions on the realized range innovations, we recover both the points and the conditional distribution forecasts. In addition, we analyse how the the relationships among the involved variables evolve over time, through a rolling window procedure. The results show evidence of the selected variables’ relevant impacts and, particularly during periods of market stress, highlight heterogeneous effects across quantiles. Full article
Figures

Figure 1

Open AccessArticle
Probability of Default and Default Correlations
J. Risk Financial Manag. 2016, 9(3), 7; doi:10.3390/jrfm9030007 -
Abstract
We consider a system where the asset values of firms are correlated with the default thresholds. We first evaluate the probability of default of a single firm under the correlated assets assumptions. This extends Merton’s probability of default of a single firm under
[...] Read more.
We consider a system where the asset values of firms are correlated with the default thresholds. We first evaluate the probability of default of a single firm under the correlated assets assumptions. This extends Merton’s probability of default of a single firm under the independent asset values assumption. At any time, the distance-to-default for a single firm is derived in the system, and this distance-to-default should provide a different measure for credit rating with the correlated asset values into consideration. Then we derive a closed formula for the joint default probability and a general closed formula for the default correlation via the correlated multivariate process of the first-passage-time default correlation model. Our structural model encodes the sensitivities of default correlations with respect to the underlying correlation among firms’ asset values. We propose the disparate credit risk management from our result in contrast to the commonly used risk measurement methods considering default correlations into consideration. Full article
Open AccessArticle
Down-Side Risk Metrics as Portfolio Diversification Strategies across the Global Financial Crisis
J. Risk Financial Manag. 2016, 9(2), 6; doi:10.3390/jrfm9020006 -
Abstract
This paper features an analysis of the effectiveness of a range of portfolio diversification strategies, with a focus on down-side risk metrics, as a portfolio diversification strategy in a European market context. We apply these measures to a set of daily arithmetically-compounded returns,
[...] Read more.
This paper features an analysis of the effectiveness of a range of portfolio diversification strategies, with a focus on down-side risk metrics, as a portfolio diversification strategy in a European market context. We apply these measures to a set of daily arithmetically-compounded returns, in U.S. dollar terms, on a set of ten market indices representing the major European markets for a nine-year period from the beginning of 2005 to the end of 2013. The sample period, which incorporates the periods of both the Global Financial Crisis (GFC) and the subsequent European Debt Crisis (EDC), is a challenging one for the application of portfolio investment strategies. The analysis is undertaken via the examination of multiple investment strategies and a variety of hold-out periods and backtests. We commence by using four two-year estimation periods and a subsequent one-year investment hold out period, to analyse a naive 1/N diversification strategy and to contrast its effectiveness with Markowitz mean variance analysis with positive weights. Markowitz optimisation is then compared to various down-side investment optimisation strategies. We begin by comparing Markowitz with CVaR, and then proceed to evaluate the relative effectiveness of Markowitz with various draw-down strategies, utilising a series of backtests. Our results suggest that none of the more sophisticated optimisation strategies appear to dominate naive diversification. Full article
Open AccessArticle
Humanizing Finance by Hedging Property Values
J. Risk Financial Manag. 2016, 9(2), 5; doi:10.3390/jrfm9020005 -
Abstract
The recent financial crisis triggered the greatest recession since the 1930s and had a devastating impact on households’ wealth and on their capacity to reduce their indebtedness. In the aftermath, it became clear that there is significant room for improvement in property risk
[...] Read more.
The recent financial crisis triggered the greatest recession since the 1930s and had a devastating impact on households’ wealth and on their capacity to reduce their indebtedness. In the aftermath, it became clear that there is significant room for improvement in property risk management. While there has been innovation in the management of corporate finance risk, real estate has lagged behind. Now is the time to expand the range of tools available for hedging households’ risks and, thus, to advance the democratization of finance. Property equity represents the major asset in households’ portfolios in developed and undeveloped countries. The present paper analyzes a set of potential innovations in real estate risk management, such as price level-adjusted mortgages, property derivatives, and home equity value insurance. Financial institutions, households, and governments should work together to improve the performance of the financial instruments available and, thus, to help mitigate the worst impacts of economic cycles. Full article
Open AccessArticle
Application of Vine Copulas to Credit Portfolio Risk Modeling
J. Risk Financial Manag. 2016, 9(2), 4; doi:10.3390/jrfm9020004 -
Abstract
In this paper, we demonstrate the superiority of vine copulas over conventional copulas when modeling the dependence structure of a credit portfolio. We show statistical and economic implications of replacing conventional copulas by vine copulas for a subportfolio of the Euro Stoxx 50
[...] Read more.
In this paper, we demonstrate the superiority of vine copulas over conventional copulas when modeling the dependence structure of a credit portfolio. We show statistical and economic implications of replacing conventional copulas by vine copulas for a subportfolio of the Euro Stoxx 50 and the S&P 500 companies, respectively. Our study includes D-vines and R-vines where the bivariate building blocks are chosen from the Gaussian, the t and the Clayton family. Our findings are (i) the conventional Gauss copula is deficient in modeling the dependence structure of a credit portfolio and economic capital is seriously underestimated; (ii) D-vine structures offer a better statistical fit to the data than classical copulas, but underestimate economic capital compared to R-vines; (iii) when mixing different copula families in an R-vine structure, the best statistical fit to the data can be achieved which corresponds to the most reliable estimate for economic capital. Full article
Open AccessArticle
Revisiting Structural Modeling of Credit Risk—Evidence from the Credit Default Swap (CDS) Market
J. Risk Financial Manag. 2016, 9(2), 3; doi:10.3390/jrfm9020003 -
Abstract
The ground-breaking Black-Scholes-Merton model has brought about a generation of derivative pricing models that have been successfully applied in the financial industry. It has been a long standing puzzle that the structural models of credit risk, as an application of the same modeling
[...] Read more.
The ground-breaking Black-Scholes-Merton model has brought about a generation of derivative pricing models that have been successfully applied in the financial industry. It has been a long standing puzzle that the structural models of credit risk, as an application of the same modeling paradigm, do not perform well empirically. We argue that the ability to accurately compute and dynamically update hedge ratios to facilitate a capital structure arbitrage is a distinctive strength of the Black-Scholes-Merton’s modeling paradigm which could be utilized in credit risk models as well. Our evidence is economically significant: We improve the implementation of a simple structural model so that it is more suitable for our application and then devise a simple capital structure arbitrage strategy based on the model. We show that the trading strategy persistently produced substantial risk-adjusted profit. Full article
Open AccessArticle
VaR and CVaR Implied in Option Prices
J. Risk Financial Manag. 2016, 9(1), 2; doi:10.3390/jrfm9010002 -
Abstract
VaR (Value at Risk) and CVaR (Conditional Value at Risk) are implied by option prices. Their relationships to option prices are derived initially under the pricing measure. It does not require assumptions about the distribution of portfolio returns. The effects of changes of
[...] Read more.
VaR (Value at Risk) and CVaR (Conditional Value at Risk) are implied by option prices. Their relationships to option prices are derived initially under the pricing measure. It does not require assumptions about the distribution of portfolio returns. The effects of changes of measure are modest at the short horizons typically used in applications. The computation of CVaR from option price is very convenient, because this measure is not elicitable, making direct comparisons of statistical inferences from market data problematic. Full article
Open AccessArticle
The Two Defaults Scenario for Stressing Credit Portfolio Loss Distributions
J. Risk Financial Manag. 2016, 9(1), 1; doi:10.3390/jrfm9010001 -
Abstract
The impact of a stress scenario of default events on the loss distribution of a credit portfolio can be assessed by determining the loss distribution conditional on these events. While it is conceptually easy to estimate loss distributions conditional on default events by
[...] Read more.
The impact of a stress scenario of default events on the loss distribution of a credit portfolio can be assessed by determining the loss distribution conditional on these events. While it is conceptually easy to estimate loss distributions conditional on default events by means of Monte Carlo simulation, it becomes impractical for two or more simultaneous defaults as then the conditioning event is extremely rare. We provide an analytical approach to the calculation of the conditional loss distribution for the CreditRisk + portfolio model with independent random loss given default distributions. The analytical solution for this case can be used to check the accuracy of an approximation to the conditional loss distribution whereby the unconditional model is run with stressed input probabilities of default (PDs). It turns out that this approximation is unbiased. Numerical examples, however, suggest that the approximation may be seriously inaccurate but that the inaccuracy leads to overestimation of tail losses and, hence, the approach errs on the conservative side. Full article
Open AccessCommentary
The Fundamental Equation in Tourism Finance
J. Risk Financial Manag. 2015, 8(4), 369-374; doi:10.3390/jrfm8040369 -
Abstract
The purpose of the paper is to present the fundamental equation in tourism finance that connects tourism research to empirical finance and financial econometrics. The energy industry, which includes, oil, gas and bio-energy fuels, together with the tourism industry, are two of the
[...] Read more.
The purpose of the paper is to present the fundamental equation in tourism finance that connects tourism research to empirical finance and financial econometrics. The energy industry, which includes, oil, gas and bio-energy fuels, together with the tourism industry, are two of the most important industries in the world today in terms of employment and generating income. The primary purpose in attracting domestic and international tourists to a country, region or city is to maximize tourism expenditure. The paper will concentrate on daily tourism expenditure, regardless of whether such data might be readily available. If such data are not available, a practical method is presented to calculate the appropriate data. Full article
Open AccessArticle
On a Discrete Interaction Risk Model with Delayed Claims
J. Risk Financial Manag. 2015, 8(4), 355-368; doi:10.3390/jrfm8040355 -
Abstract
We study a discrete-time interaction risk model with delayed claims within the framework of the compound binomial model. Using the technique of generating functions, we derive both a recursive formula and a defective renewal equation for the expected discounted penalty function. As applications,
[...] Read more.
We study a discrete-time interaction risk model with delayed claims within the framework of the compound binomial model. Using the technique of generating functions, we derive both a recursive formula and a defective renewal equation for the expected discounted penalty function. As applications, the probabilities of ruin and the joint distributions of the surplus one period to ruin and the deficit at ruin are investigated. Numerical illustrations are also given. Full article
Open AccessArticle
An Empirical Analysis for the Prediction of a Financial Crisis in Turkey through the Use of Forecast Error Measures
J. Risk Financial Manag. 2015, 8(3), 337-354; doi:10.3390/jrfm8030337 -
Abstract
In this study, we try to examine whether the forecast errors obtained by the ANN models affect the breakout of financial crises. Additionally, we try to investigate how much the asymmetric information and forecast errors are reflected on the output values. In our
[...] Read more.
In this study, we try to examine whether the forecast errors obtained by the ANN models affect the breakout of financial crises. Additionally, we try to investigate how much the asymmetric information and forecast errors are reflected on the output values. In our study, we used the exchange rate of USD/TRY (USD), the Borsa Istanbul 100 Index (BIST), and gold price (GP) as our output variables of our Artificial Neural Network (ANN) models. We observe that the predicted ANN model has a strong explanation capability for the 2001 and 2008 crises. Our calculations of some symmetry measures such as mean absolute percentage error (MAPE), symmetric mean absolute percentage error (sMAPE), and Shannon entropy (SE), clearly demonstrate the degree of asymmetric information and the deterioration of the financial system prior to, during, and after the financial crisis. We found that the asymmetric information prior to crisis is larger as compared to other periods. This situation can be interpreted as early warning signals before the potential crises. This evidence seems to favor an asymmetric information view of financial crises. Full article
Open AccessArticle
Volatility Forecast in Crises and Expansions
J. Risk Financial Manag. 2015, 8(3), 311-336; doi:10.3390/jrfm8030311 -
Abstract
We build a discrete-time non-linear model for volatility forecasting purposes. This model belongs to the class of threshold-autoregressive models, where changes in regimes are governed by past returns. The ability to capture changes in volatility regimes and using more accurate volatility measures allow
[...] Read more.
We build a discrete-time non-linear model for volatility forecasting purposes. This model belongs to the class of threshold-autoregressive models, where changes in regimes are governed by past returns. The ability to capture changes in volatility regimes and using more accurate volatility measures allow outperforming other benchmark models, such as linear heterogeneous autoregressive model and GARCH specifications. Finally, we show how to derive closed-form expression for multiple-step-ahead forecasting by exploiting information about the conditional distribution of returns. Full article
Open AccessArticle
Inflation and Speculation in a Dynamic Macroeconomic Model
J. Risk Financial Manag. 2015, 8(3), 285-310; doi:10.3390/jrfm8030285 -
Abstract
We study a monetary version of the Keen model by merging two alternative extensions, namely the addition of a dynamic price level and the introduction of speculation. We recall and study old and new equilibria, together with their local stability analysis. This includes
[...] Read more.
We study a monetary version of the Keen model by merging two alternative extensions, namely the addition of a dynamic price level and the introduction of speculation. We recall and study old and new equilibria, together with their local stability analysis. This includes a state of recession associated with a deflationary regime and characterized by falling employment but constant wage shares, with or without an accompanying debt crisis. Full article
Open AccessArticle
Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information
J. Risk Financial Manag. 2015, 8(2), 266-284; doi:10.3390/jrfm8020266 -
Abstract
Analyzing social systems, particularly financial markets, using a complex network approach has become one of the most popular fields within econophysics. A similar trend is currently appearing within the econometrics and finance communities, as well. In this study, we present a state-of-the-artmethod for
[...] Read more.
Analyzing social systems, particularly financial markets, using a complex network approach has become one of the most popular fields within econophysics. A similar trend is currently appearing within the econometrics and finance communities, as well. In this study, we present a state-of-the-artmethod for analyzing the structure and risk within stockmarkets, treating them as complex networks using model-free, nonlinear dependency measures based on information theory. This study is the first network analysis of the stockmarket in Shanghai using a nonlinear network methodology. Further, it is often assumed that markets outside the United States and Western Europe are inherently riskier. We find that the Chinese stock market is not structurally risky, contradicting this popular opinion. We use partial mutual information to create filtered networks representing the Shanghai stock exchange, comparing them to networks based on Pearson’s correlation. Consequently, we discuss the structure and characteristics of both the presented methods and the Shanghai stock exchange. This paper provides an insight into the cutting edge methodology designed for analyzing complex financial networks, as well as analyzing the structure of the market in Shanghai and, as such, is of interest to both researchers and financial analysts. Full article
Open AccessArticle
Dependency Relations among International Stock Market Indices
J. Risk Financial Manag. 2015, 8(2), 227-265; doi:10.3390/jrfm8020227 -
Abstract
We develop networks of international stock market indices using information and correlation based measures. We use 83 stock market indices of a diversity of countries, as well as their single day lagged values, to probe the correlation and the flow of information from
[...] Read more.
We develop networks of international stock market indices using information and correlation based measures. We use 83 stock market indices of a diversity of countries, as well as their single day lagged values, to probe the correlation and the flow of information from one stock index to another taking into account different operating hours. Additionally, we apply the formalism of partial correlations to build the dependency network of the data, and calculate the partial Transfer Entropy to quantify the indirect influence that indices have on one another. We find that Transfer Entropy is an effective way to quantify the flow of information between indices, and that a high degree of information flow between indices lagged by one day coincides to same day correlation between them. Full article