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J. Risk Financial Manag., Volume 11, Issue 1 (March 2018)

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Open AccessFeature PaperReview
Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections
J. Risk Financial Manag. 2018, 11(1), 15; https://doi.org/10.3390/jrfm11010015
Received: 4 February 2018 / Revised: 7 March 2018 / Accepted: 13 March 2018 / Published: 20 March 2018
Cited by 6 | Viewed by 1729 | PDF Full-text (435 KB) | HTML Full-text | XML Full-text
Abstract
The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses research issues that are related to the various disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to [...] Read more.
The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses research issues that are related to the various disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models, as well as conduct simulation to examine whether the estimators in their theories on estimation and hypothesis testing have good size and high power. Thereafter, academics and practitioners could apply theory to analyse some interesting issues in the seven disciplines and cognate areas. Full article
(This article belongs to the collection Feature Papers of JRFM)
Open AccessFeature PaperArticle
Groups, Pricing, and Cost of Debt: Evidence from Turkey
J. Risk Financial Manag. 2018, 11(1), 14; https://doi.org/10.3390/jrfm11010014
Received: 13 February 2018 / Revised: 13 March 2018 / Accepted: 13 March 2018 / Published: 16 March 2018
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Abstract
The paper examines the impact of business group affiliation on cost of loans in an emerging market setting. It focuses on operational strategy, organizational structure and internationalization policies of business group firms and their impact on borrowing cost of affiliated firms. Bank loans [...] Read more.
The paper examines the impact of business group affiliation on cost of loans in an emerging market setting. It focuses on operational strategy, organizational structure and internationalization policies of business group firms and their impact on borrowing cost of affiliated firms. Bank loans are a dominant source of corporate funding in emerging markets, in which business groups exist as leading economic entities. Yet, the impact of belonging to a group on the firm’s cost of debt has not been studied in depth. Our results reveal that the extent of group affiliation, government ownership, and diversification increase the cost of loans. However, a group bank is advantageous in terms of borrowing, and decreases the cost of loans. While foreign ownership is beneficial in terms of pricing, being affiliated with a foreign group is not. Being a financial firm and being cross-listed are not significantly associated with bank loan terms. Borrowing costs are thus influenced in various ways by organizational structure, operational strategies, and global policies of business groups and affiliates. Therefore, business groups may benefit from strategically implementing policies and selecting loan applicant firms. Full article
(This article belongs to the collection Empirical Asset Pricing)
Open AccessArticle
Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis
J. Risk Financial Manag. 2018, 11(1), 13; https://doi.org/10.3390/jrfm11010013
Received: 30 December 2017 / Revised: 2 March 2018 / Accepted: 5 March 2018 / Published: 7 March 2018
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Abstract
In this study, we propose to apply the transmuted log-logistic (TLL) model which is a generalization of log-logistic model, in a Bayesian context. The log-logistic model has been used it is simple and has a unimodal hazard rate, important characteristic in survival analysis. [...] Read more.
In this study, we propose to apply the transmuted log-logistic (TLL) model which is a generalization of log-logistic model, in a Bayesian context. The log-logistic model has been used it is simple and has a unimodal hazard rate, important characteristic in survival analysis. Also, the TLL model was formulated by using the quadratic transmutation map, that is a simple way of derivating new distributions, and it adds a new parameter λ , which one introduces a skewness in the new distribution and preserves the moments of the baseline model. The Bayesian model was formulated by using the half-Cauchy prior which is an alternative prior to a inverse Gamma distribution. In order to fit the model, a real data set, which consist of the time up to first calving of polled Tabapua race, was used. Finally, after the model was fitted, an influential analysis was made and excluding only 0.1 % of observations (influential points), the reestimated model can fit the data better. Full article
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
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Open AccessArticle
Ensemble Learning or Deep Learning? Application to Default Risk Analysis
J. Risk Financial Manag. 2018, 11(1), 12; https://doi.org/10.3390/jrfm11010012
Received: 19 January 2018 / Revised: 24 February 2018 / Accepted: 28 February 2018 / Published: 5 March 2018
Cited by 3 | Viewed by 2313 | PDF Full-text (4488 KB) | HTML Full-text | XML Full-text
Abstract
Proper credit-risk management is essential for lending institutions, as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasingly important. This study analyzes default payment data and compares the prediction accuracy [...] Read more.
Proper credit-risk management is essential for lending institutions, as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasingly important. This study analyzes default payment data and compares the prediction accuracy and classification ability of three ensemble-learning methods—specifically, bagging, random forest, and boosting—with those of various neural-network methods, each of which has a different activation function. The results obtained indicate that the classification ability of boosting is superior to other machine-learning methods including neural networks. It is also found that the performance of neural-network models depends on the choice of activation function, the number of middle layers, and the inclusion of dropout. Full article
(This article belongs to the Special Issue Empirical Finance) Printed Edition available
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Open AccessArticle
Variance Swap Replication: Discrete or Continuous?
J. Risk Financial Manag. 2018, 11(1), 11; https://doi.org/10.3390/jrfm11010011
Received: 14 December 2017 / Revised: 1 February 2018 / Accepted: 5 February 2018 / Published: 12 February 2018
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Abstract
The popular replication formula to price variance swaps assumes continuity of traded option strikes. In practice, however, there is only a discrete set of option strikes traded on the market. We present here different discrete replication strategies and explain why the continuous replication [...] Read more.
The popular replication formula to price variance swaps assumes continuity of traded option strikes. In practice, however, there is only a discrete set of option strikes traded on the market. We present here different discrete replication strategies and explain why the continuous replication price is more relevant. Full article
(This article belongs to the Section Mathematical Finance)
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Open AccessArticle
A New Generalization of the Pareto Distribution and Its Application to Insurance Data
J. Risk Financial Manag. 2018, 11(1), 10; https://doi.org/10.3390/jrfm11010010
Received: 26 November 2017 / Revised: 28 January 2018 / Accepted: 2 February 2018 / Published: 7 February 2018
Cited by 1 | Viewed by 1436 | PDF Full-text (341 KB) | HTML Full-text | XML Full-text
Abstract
The Pareto classical distribution is one of the most attractive in statistics and particularly in the scenario of actuarial statistics and finance. For example, it is widely used when calculating reinsurance premiums. In the last years, many alternative distributions have been proposed to [...] Read more.
The Pareto classical distribution is one of the most attractive in statistics and particularly in the scenario of actuarial statistics and finance. For example, it is widely used when calculating reinsurance premiums. In the last years, many alternative distributions have been proposed to obtain better adjustments especially when the tail of the empirical distribution of the data is very long. In this work, an alternative generalization of the Pareto distribution is proposed and its properties are studied. Finally, application of the proposed model to the earthquake insurance data set is presented. Full article
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
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Open AccessArticle
Effectiveness of Interest Rate Policy of the Fed in Management of Subprime Mortgage Crisis
J. Risk Financial Manag. 2018, 11(1), 9; https://doi.org/10.3390/jrfm11010009
Received: 6 January 2018 / Revised: 1 February 2018 / Accepted: 2 February 2018 / Published: 6 February 2018
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Abstract
The federal funds rate is one of the most important monetary policy instruments of Federal Reserve Bank of America. In this study, we analyze the effectiveness of Fed interest rate policy on different markets in the period between 1976 and 2016 through Markov [...] Read more.
The federal funds rate is one of the most important monetary policy instruments of Federal Reserve Bank of America. In this study, we analyze the effectiveness of Fed interest rate policy on different markets in the period between 1976 and 2016 through Markov regime-switching regression analysis. Results indicate that Federal funds’ rate affects labor and housing markets with a few months’ lag. However, the influence of Federal funds rate on inflation rate is quite limited. It is most probable that Fed employs alternative monetary instruments to regulate inflation. The most interesting results are obtained in the domain of personal savings. The interaction of personal savings and Federal funds rate is significant during both expansion and recession regimes. Full article
(This article belongs to the Special Issue Financial Crises, Macroeconomic Management, and Financial Regulation)
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Open AccessFeature PaperArticle
Estimation of Cross-Lingual News Similarities Using Text-Mining Methods
J. Risk Financial Manag. 2018, 11(1), 8; https://doi.org/10.3390/jrfm11010008
Received: 31 December 2017 / Revised: 23 January 2018 / Accepted: 25 January 2018 / Published: 31 January 2018
Viewed by 1608 | PDF Full-text (1016 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the Internet, and [...] Read more.
In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the Internet, and these are written not only in English but also in other languages such as Chinese, Japanese, French, etc. By taking advantage of multi-lingual text resources provided by Thomson Reuters News, we developed two estimation algorithms for extracting cross-lingual news pairs from multilingual text resources. In our first method, we propose a novel structure that uses the word information and the machine learning method effectively in this task. Simultaneously, we developed a bidirectional Long Short-Term Memory (LSTM) based method to calculate cross-lingual semantic text similarity for long text and short text, respectively. Thus, when an important news article is published, users can read similar news articles that are written in their native language using our method. Full article
(This article belongs to the Special Issue Empirical Finance) Printed Edition available
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Open AccessArticle
Does the Assumption on Innovation Process Play an Important Role for Filtered Historical Simulation Model?
J. Risk Financial Manag. 2018, 11(1), 7; https://doi.org/10.3390/jrfm11010007
Received: 30 November 2017 / Revised: 17 January 2018 / Accepted: 22 January 2018 / Published: 23 January 2018
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Abstract
Most of the financial institutions compute the Value-at-Risk (VaR) of their trading portfolios using historical simulation-based methods. In this paper, we examine the Filtered Historical Simulation (FHS) model introduced by Barone-Adesi et al. (1999) theoretically and empirically. The main goal of [...] Read more.
Most of the financial institutions compute the Value-at-Risk (VaR) of their trading portfolios using historical simulation-based methods. In this paper, we examine the Filtered Historical Simulation (FHS) model introduced by Barone-Adesi et al. (1999) theoretically and empirically. The main goal of this study is to find an answer for the following question: “Does the assumption on innovation process play an important role for the Filtered Historical Simulation model?”. For this goal, we investigate the performance of FHS model with skewed and fat-tailed innovations distributions such as normal, skew normal, Student’s-t, skew-T, generalized error, and skewed generalized error distributions. The performances of FHS models are evaluated by means of unconditional and conditional likelihood ratio tests and loss functions. Based on the empirical results, we conclude that the FHS models with generalized error and skew-T distributions produce more accurate VaR forecasts. Full article
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
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Open AccessArticle
Negative Binomial Kumaraswamy-G Cure Rate Regression Model
J. Risk Financial Manag. 2018, 11(1), 6; https://doi.org/10.3390/jrfm11010006
Received: 8 December 2017 / Revised: 15 January 2018 / Accepted: 16 January 2018 / Published: 19 January 2018
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Abstract
In survival analysis, the presence of elements not susceptible to the event of interest is very common. These elements lead to what is called a fraction cure, cure rate, or even long-term survivors. In this paper, we propose a unified approach using the [...] Read more.
In survival analysis, the presence of elements not susceptible to the event of interest is very common. These elements lead to what is called a fraction cure, cure rate, or even long-term survivors. In this paper, we propose a unified approach using the negative binomial distribution for modeling cure rates under the Kumaraswamy family of distributions. The estimation is made by maximum likelihood. We checked the maximum likelihood asymptotic properties through some simulation setups. Furthermore, we propose an estimation strategy based on the Negative Binomial Kumaraswamy-G generalized linear model. Finally, we illustrate the distributions proposed using a real data set related to health risk. Full article
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
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Open AccessArticle
Modified Stieltjes Transform and Generalized Convolutions of Probability Distributions
J. Risk Financial Manag. 2018, 11(1), 5; https://doi.org/10.3390/jrfm11010005
Received: 3 November 2017 / Revised: 25 December 2017 / Accepted: 11 January 2018 / Published: 14 January 2018
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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 [...] Read more.
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
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
Open AccessEditorial
Acknowledgement to Reviewers of Journal of Risk and Financial Management in 2017
J. Risk Financial Manag. 2018, 11(1), 4; https://doi.org/10.3390/jrfm11010004
Received: 9 January 2018 / Revised: 9 January 2018 / Accepted: 9 January 2018 / Published: 10 January 2018
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Open AccessFeature PaperArticle
Models of Investor Forecasting Behavior — Experimental Evidence
J. Risk Financial Manag. 2018, 11(1), 3; https://doi.org/10.3390/jrfm11010003
Received: 1 November 2017 / Revised: 15 December 2017 / Accepted: 22 December 2017 / Published: 28 December 2017
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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 [...] Read more.
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
(This article belongs to the Special Issue Behavioral Finance)
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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; https://doi.org/10.3390/jrfm11010002
Received: 31 October 2017 / Revised: 15 December 2017 / Accepted: 18 December 2017 / Published: 28 December 2017
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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 [...] Read more.
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
(This article belongs to the Special Issue Applied Econometrics) Printed Edition available
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Open AccessArticle
The Burr X Pareto Distribution: Properties, Applications and VaR Estimation
J. Risk Financial Manag. 2018, 11(1), 1; https://doi.org/10.3390/jrfm11010001
Received: 31 October 2017 / Revised: 30 November 2017 / Accepted: 18 December 2017 / Published: 21 December 2017
Cited by 2 | Viewed by 1725 | PDF Full-text (463 KB) | HTML Full-text | XML Full-text
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 [...] Read more.
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
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
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J. Risk Financial Manag. EISSN 1911-8074 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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