Open AccessArticle
Modified Stieltjes Transform and Generalized Convolutions of Probability Distributions
J. Risk Financial Manag. 2018, 11(1), 5; doi:10.3390/jrfm11010005 -
Abstract
The classical Stieltjes transform is modified in such a way as to generalize both Stieltjes and Fourier transforms. This transform allows the introduction of new classes of commutative and non-commutative generalized convolutions. A particular case of such a convolution for degenerate distributions appears
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The classical Stieltjes transform is modified in such a way as to generalize both Stieltjes and Fourier transforms. This transform allows the introduction of new classes of commutative and non-commutative generalized convolutions. A particular case of such a convolution for degenerate distributions appears to be the Wigner semicircle distribution. Full article
Open AccessEditorial
Acknowledgement to Reviewers of Journal of Risk and Financial Management in 2017
J. Risk Financial Manag. 2018, 11(1), 4; doi:10.3390/jrfm11010004 -
Open AccessFeature PaperArticle
Models of Investor Forecasting Behavior — Experimental Evidence
J. Risk Financial Manag. 2018, 11(1), 3; doi:10.3390/jrfm11010003 -
Abstract
Different forecasting behaviors affect investors’ trading decisions and lead to qualitatively different asset price trajectories. It has been shown in the literature that the weights that investors place on observed asset price changes when forecasting future price changes, and the nature of their
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Different forecasting behaviors affect investors’ trading decisions and lead to qualitatively different asset price trajectories. It has been shown in the literature that the weights that investors place on observed asset price changes when forecasting future price changes, and the nature of their confidence when price changes are forecast, determine whether price bubbles, price crashes, and unpredictable price cycles occur. In this paper, we report the results of behavioral experiments involving multiple investors who participated in a market for a virtual asset. Our goal is to study investors’ forecast formation. We conducted three experimental sessions with different participants in each session. We fit different models of forecast formation to the observed data. There is strong evidence that the investors forecast future prices by extrapolating past price changes, even when they know the fundamental value of the asset exactly and the extrapolated forecasts differ significantly from the fundamental value. The rational expectations hypothesis seems inconsistent with the observed forecasts. The forecasting models of all participants that best fit the observed forecasting data were of the type that cause price bubbles and cycles in dynamical systems models, and price bubbles and cycles ended up occurring in all three sessions. Full article
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Open AccessArticle
FHA Loans in Foreclosure Proceedings: Distinguishing Sources of Interdependence in Competing Risks
J. Risk Financial Manag. 2018, 11(1), 2; doi:10.3390/jrfm11010002 -
Abstract
A mortgage borrower has several options once a foreclosure proceedings is initiated, mainly default and prepayment. Using a sample of FHA mortgage loans, we develop a dependent competing risks framework to examine the determinants of time to default and time to prepayment once
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A mortgage borrower has several options once a foreclosure proceedings is initiated, mainly default and prepayment. Using a sample of FHA mortgage loans, we develop a dependent competing risks framework to examine the determinants of time to default and time to prepayment once the foreclosure proceedings is initiated. More importantly, we examine the interdependence between default and prepayment, through both the correlation of the unobserved heterogeneity terms and the preventive behavior of the individual mortgage borrowers. We find that time to default and time to prepayment are affected by several factors, such as the Loan-To-Value ratio (LTV), FICO score and unemployment rate. In addition, we find strong evidence that supports the existence of interdependence between the default and prepayment hazards through both the correlation of the unobserved heterogeneity terms and the preventive behavior of individual mortgage borrowers. We show that neglecting the interdependence through the preventive behavior of the individual mortgage borrowers can lead to biased estimates and misleading inference. Full article
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Open AccessArticle
The Burr X Pareto Distribution: Properties, Applications and VaR Estimation
J. Risk Financial Manag. 2018, 11(1), 1; doi:10.3390/jrfm11010001 -
Abstract
In this paper, a new three-parameter Pareto distribution is introduced and studied. We discuss various mathematical and statistical properties of the new model. Some estimation methods of the model parameters are performed. Moreover, the peaks-over-threshold method is used to estimate Value-at-Risk (VaR) by
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In this paper, a new three-parameter Pareto distribution is introduced and studied. We discuss various mathematical and statistical properties of the new model. Some estimation methods of the model parameters are performed. Moreover, the peaks-over-threshold method is used to estimate Value-at-Risk (VaR) by means of the proposed distribution. We compare the distribution with a few other models to show its versatility in modelling data with heavy tails. VaR estimation with the Burr X Pareto distribution is presented using time series data, and the new model could be considered as an alternative VaR model against the generalized Pareto model for financial institutions. Full article
Open AccessFeature PaperArticle
Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models
J. Risk Financial Manag. 2017, 10(4), 23; doi:10.3390/jrfm10040023 -
Abstract
This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the corresponding statistical properties of this model,
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This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the corresponding statistical properties of this model, discuss the spectral likelihood estimation and investigate the finite sample properties via Monte Carlo experiments. We provide empirical evidence by applying the GLMSV model to three exchange rate return series and conjecture that the results of out-of-sample forecasts adequately confirm the use of GLMSV model in certain financial applications. Full article
Open AccessArticle
Intelligent Decision Support in Proportional–Stop-Loss Reinsurance Using Multiple Attribute Decision-Making (MADM)
J. Risk Financial Manag. 2017, 10(4), 22; doi:10.3390/jrfm10040022 -
Abstract
This article addresses the possibility of incorporating intelligent decision support systems into reinsurance decision-making. This involves the insurance company and the reinsurance company, and is negotiated through reinsurance intermediaries. The article proposes a decision flow to model the reinsurance design and selection process.
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This article addresses the possibility of incorporating intelligent decision support systems into reinsurance decision-making. This involves the insurance company and the reinsurance company, and is negotiated through reinsurance intermediaries. The article proposes a decision flow to model the reinsurance design and selection process. This article focuses on adopting more than one optimality criteria under a more generic combinational design of commonly used reinsurance products, i.e., proportional reinsurance and stop-loss reinsurance. In terms of methodology, the significant contribution of the study the incorporation of the well-established decision analysis tool multiple-attribute decision-making (MADM) into the modelling of reinsurance selection. To illustrate the feasibility of incorporating intelligent decision supporting systems in the reinsurance market, the study includes a numerical case study using the simulation software @Risk in modeling insurance claims, as well as programming in MATLAB to realize MADM. A list of managerial implications could be drawn from the case study results. Most importantly, when choosing the most appropriate type of reinsurance, insurance companies should base their decisions on multiple measurements instead of single-criteria decision-making models so that their decisions may be more robust. Full article
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Open AccessFeature PaperArticle
Recovering Historical Inflation Data from Postage Stamps Prices
J. Risk Financial Manag. 2017, 10(4), 21; doi:10.3390/jrfm10040021 -
Abstract
For many developing countries, historical inflation figures are rarely available. We propose a simple method that aims to recover such figures of inflation using prices of postage stamps issued in earlier years. We illustrate our method for Suriname, where annual inflation rates are
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For many developing countries, historical inflation figures are rarely available. We propose a simple method that aims to recover such figures of inflation using prices of postage stamps issued in earlier years. We illustrate our method for Suriname, where annual inflation rates are available for 1961 until 2015, and where fluctuations in inflation rates are prominent. We estimate the inflation rates for the sample 1873 to 1960. Our main finding is that high inflation periods usually last no longer than 2 or 3 years. An Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model for the recent sample and for the full sample with the recovered inflation rates shows the relevance of adding the recovered data. Full article
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Open AccessFeature PaperArticle
A Risk Management Approach for a Sustainable Cloud Migration
J. Risk Financial Manag. 2017, 10(4), 20; doi:10.3390/jrfm10040020 -
Abstract
Cloud computing is not just about resource sharing, cost savings and optimisation of business performance; it also involves fundamental concerns on how businesses need to respond on the risks and challenges upon migration. Managing risks is critical for a sustainable cloud adoption. It
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Cloud computing is not just about resource sharing, cost savings and optimisation of business performance; it also involves fundamental concerns on how businesses need to respond on the risks and challenges upon migration. Managing risks is critical for a sustainable cloud adoption. It includes several dimensions such as cost, practising the concept of green IT, data quality, continuity of services to users and clients, guarantee tangible benefits. This paper presents a risk management approach for a sustainable cloud migration. We consider four dimensions of sustainability, i.e., economic, environmental, social and technology to determine the viability of cloud for the business context. The risks are systematically identified and analysed based on the existing in house controls and the cloud service provider offerings. We use Dempster Shafer (D-S) theory to measure the adequacy of controls and apply semi-quantitative approach to perform risk analysis based on the theory of belief. The risk exposure for each sustainability dimension allows us to determine the viability of cloud migration. A practical migration use case is considered to determine the applicability of our work. The results identify the risk exposure and recommended control for the risk mitigation. We conclude that risks depend on specific migration case and both Cloud Service Provider (CSP) and users are responsible for the risk mitigation. Inherent risks can evolve due to the cloud migration. Full article
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Open AccessArticle
Bivariate Kumaraswamy Models via Modified FGM Copulas: Properties and Applications
J. Risk Financial Manag. 2017, 10(4), 19; doi:10.3390/jrfm10040019 -
Abstract
A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of FGM (Farlie–Gumbel–Morgenstern) bivariate copula for constructing several different bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It
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A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of FGM (Farlie–Gumbel–Morgenstern) bivariate copula for constructing several different bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It is established that construction of bivariate distributions by this method allows for greater flexibility in the values of Spearman’s correlation coefficient, ρ and Kendall’s τ. Full article
Open AccessArticle
Financial Market Integration: Evidence from Cross-Listed French Firms
J. Risk Financial Manag. 2017, 10(4), 18; doi:10.3390/jrfm10040018 -
Abstract
Using high frequency data we investigate the behavior of the intraday volatility and the volume of eight cross-listed French firms. There is a two hour “overlap” period during which French firms are traded in Paris and their related American Depositary Receipts (ADRs) are
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Using high frequency data we investigate the behavior of the intraday volatility and the volume of eight cross-listed French firms. There is a two hour “overlap” period during which French firms are traded in Paris and their related American Depositary Receipts (ADRs) are traded in New York. Using concurrent 15-min returns, this article examines the extent of market integration—defined as prices in both markets reflecting the same fundamental information—involving these firms. Our results suggest that these markets are not perfectly integrated. A significant rise in volatility and volume is observed during the two hour “overlap” period. This suggests the existence of informed trading. An error correction model (ECM) is then used to examine changes in prices of French firms in Paris and New York. These temporary changes appear to converge over time. Full article
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Open AccessArticle
GARCH Modelling of Cryptocurrencies
J. Risk Financial Manag. 2017, 10(4), 17; doi:10.3390/jrfm10040017 -
Abstract
With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are
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With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms of five criteria. Conclusions are drawn on the best fitting models, forecasts and acceptability of value at risk estimates. Full article
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Open AccessLetter
Global Hedging through Post-Decision State Variables
J. Risk Financial Manag. 2017, 10(3), 16; doi:10.3390/jrfm10030016 -
Abstract
Unlike delta-hedging or similar methods based on Greeks, global hedging is an approach that optimizes some terminal criterion that depends on the difference between the value of a derivative security and that of its hedging portfolio at maturity or exercise. Global hedging methods
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Unlike delta-hedging or similar methods based on Greeks, global hedging is an approach that optimizes some terminal criterion that depends on the difference between the value of a derivative security and that of its hedging portfolio at maturity or exercise. Global hedging methods in discrete time can be implemented using dynamic programming. They provide optimal strategies at all rebalancing dates for all possible states of the world, and can easily accommodate transaction fees and other frictions. However, considering transaction fees in the dynamic programming model requires the inclusion of an additional state variable, which translates into a significant increase of the computational burden. In this short note, we show how a decomposition technique based on the concept of post-decision state variables can be used to reduce the complexity of the computations to the level of a problem without transaction fees. The latter complexity reduction allows for substantial gains in terms of computing time and should therefore contribute to increasing the applicability of global hedging schemes in practice where the timely execution of portfolio rebalancing trades is crucial. Full article
Open AccessArticle
Trade Openness and Bank Risk-Taking Behavior: Evidence from Emerging Economies
J. Risk Financial Manag. 2017, 10(3), 15; doi:10.3390/jrfm10030015 -
Abstract
In this paper, we examine the impact of trade openness on bank risk-taking behavior. Using a panel dataset of 291 banks from 37 emerging countries over the period from 1998 to 2012, we find that higher trade openness decreases bank risk-taking. The results
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In this paper, we examine the impact of trade openness on bank risk-taking behavior. Using a panel dataset of 291 banks from 37 emerging countries over the period from 1998 to 2012, we find that higher trade openness decreases bank risk-taking. The results are robust when we use alternative bank risk-taking proxies and alternative estimation methods. We argue that trade openness provides diversification opportunities to banks in lending activities, which decrease overall bank risk. Further to this end, we observe that higher trade openness helps domestic banks to smooth out income volatility and decreases the impact of a financial crisis on banks. Full article
Open AccessArticle
Safety Evaluation of Evacuation Routes in Central Tokyo Assuming a Large-Scale Evacuation in Case of Earthquake Disasters
J. Risk Financial Manag. 2017, 10(3), 14; doi:10.3390/jrfm10030014 -
Abstract
The present study aims to conduct a quantitative evaluation of evacuation route safety using the Ant Colony Optimization (ACO) algorithm for risk management in central Tokyo. Firstly, the similarity in safety was focused on while taking into consideration road blockage probability. Then, by
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The present study aims to conduct a quantitative evaluation of evacuation route safety using the Ant Colony Optimization (ACO) algorithm for risk management in central Tokyo. Firstly, the similarity in safety was focused on while taking into consideration road blockage probability. Then, by classifying roads by means of the hierarchical cluster analysis, the congestion rates of evacuation routes using ACO simulations were estimated. Based on these results, the multiple evacuation routes extracted were visualized on digital maps by means of Geographic Information Systems (GIS), and their safety was evaluated. Furthermore, the selection of safe evacuation routes between evacuation sites for cases when the possibility of large-scale evacuation after an earthquake disaster is high is made possible. As the evaluation method is based on public information, by obtaining the same geographic information as the present study, it is effective in other areas, regardless of whether the information is from the past or future. Therefore, in addition to spatial reproducibility, the evaluation method also has high temporal reproducibility. Because safety evaluations are conducted on evacuation routes based on quantified data, the selected highly safe evacuation routes have been quantitatively evaluated, and thus serve as an effective indicator when selecting evacuation routes. Full article
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Open AccessArticle
OTC Derivatives and Global Economic Activity: An Empirical Analysis
J. Risk Financial Manag. 2017, 10(2), 13; doi:10.3390/jrfm10020013 -
Abstract
That the global market for derivatives has expanded beyond recognition is well known. What is not know is how this market interacts with economic activity. We provide the first empirical characterization of interdependencies between OECD economic activity and the global OTC derivatives market.
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That the global market for derivatives has expanded beyond recognition is well known. What is not know is how this market interacts with economic activity. We provide the first empirical characterization of interdependencies between OECD economic activity and the global OTC derivatives market. To this end, we apply a vector-error correction model to OTC derivatives disaggregated across instruments and counterparties. The results indicate that with one exception, the heterogeneity of OTC contracts is too pronounced to be reliably summarized by our measures of economic activity. The one exception is interest-rate derivatives held by Other Financial Institutions. Full article
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Open AccessArticle
A Statistical Analysis of Cryptocurrencies
J. Risk Financial Manag. 2017, 10(2), 12; doi:10.3390/jrfm10020012 -
Abstract
We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal,
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We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal, however, no single distribution fits well jointly to all the cryptocurrencies analysed. We find that for the most popular currencies, such as Bitcoin and Litecoin, the generalized hyperbolic distribution gives the best fit, while for the smaller cryptocurrencies the normal inverse Gaussian distribution, generalized t distribution, and Laplace distribution give good fits. The results are important for investment and risk management purposes. Full article
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Open AccessArticle
The Solvency II Standard Formula, Linear Geometry, and Diversification
J. Risk Financial Manag. 2017, 10(2), 11; doi:10.3390/jrfm10020011 -
Abstract
The core of risk aggregation in the Solvency II Standard Formula is the so-called square root formula. We argue that it should be seen as a means for the aggregation of different risks to an overall risk rather than being associated with variance-covariance
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The core of risk aggregation in the Solvency II Standard Formula is the so-called square root formula. We argue that it should be seen as a means for the aggregation of different risks to an overall risk rather than being associated with variance-covariance based risk analysis. Considering the Solvency II Standard Formula from the viewpoint of linear geometry, we immediately find that it defines a norm and therefore provides a homogeneous and sub-additive tool for risk aggregation. Hence, Euler’s Principle for the reallocation of risk capital applies and yields explicit formulas for capital allocation in the framework given by the Solvency II Standard Formula. This gives rise to the definition of diversification functions, which we define as monotone, subadditive, and homogeneous functions on a convex cone. Diversification functions constitute a class of models for the study of the aggregation of risk and diversification. The aggregation of risk measures using a diversification function preserves the respective properties of these risk measures. Examples of diversification functions are given by seminorms, which are monotone on the convex cone of non-negative vectors. Each Lp norm has this property, and any scalar product given by a non-negative positive semidefinite matrix does as well. In particular, the Standard Formula is a diversification function and hence a risk measure that preserves homogeneity, subadditivity and convexity. Full article
Open AccessArticle
A Risk Management Framework for Cloud Migration Decision Support
J. Risk Financial Manag. 2017, 10(2), 10; doi:10.3390/jrfm10020010 -
Abstract
Keywords: risk management framework; risk assessment; cloud migration; security; analytic hierarchy process (AHP); business value Full article
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Open AccessArticle
Capital Regulation, the Cost of Financial Intermediation and Bank Profitability: Evidence from Bangladesh
J. Risk Financial Manag. 2017, 10(2), 9; doi:10.3390/jrfm10020009 -
Abstract
In response to the recent global financial crisis, the regulatory authorities in many countries have imposed stringent capital requirements in the form of the BASEL III Accord to ensure financial stability. On the other hand, bankers have criticized new regulation on the ground
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In response to the recent global financial crisis, the regulatory authorities in many countries have imposed stringent capital requirements in the form of the BASEL III Accord to ensure financial stability. On the other hand, bankers have criticized new regulation on the ground that it would enhance the cost of funds for bank borrowers and deteriorate the bank profitability. In this study, we examine the impact of capital requirements on the cost of financial intermediation and bank profitability using a panel dataset of 32 Bangladeshi banks over the period from 2000 to 2015. By employing a dynamic panel generalized method of moments (GMM) estimator, we find robust evidence that higher bank regulatory capital ratios reduce the cost of financial intermediation and increase bank profitability. The results hold when we use equity to total assets ratio as an alternative measure of bank capital. We also observe that switching from BASEL I to BASEL II has no measurable impact on the cost of financial intermediation and bank profitability in Bangladesh. In the empirical analysis, we further observe that higher bank management and cost efficiencies are associated with the lower cost of financial intermediation and higher bank profitability. These results have important implications for bank regulators, academicians, and bankers. Full article
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