Next Issue
Previous Issue

Table of Contents

J. Risk Financial Manag., Volume 10, Issue 1 (March 2017)

  • 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 1-7
Export citation of selected articles as:

Editorial

Jump to: Research

Open AccessEditorial Acknowledgement to Reviewers of the Journal of Risk and Financial Management in 2016
J. Risk Financial Manag. 2017, 10(1), 2; doi:10.3390/jrfm10010002
Received: 10 January 2017 / Revised: 10 January 2017 / Accepted: 10 January 2017 / Published: 10 January 2017
PDF Full-text (184 KB) | HTML Full-text | XML Full-text
Abstract
The editors of the Journal of Risk and Financial Management would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2016.[...] Full article

Research

Jump to: Editorial

Open AccessArticle Portfolio Optimization and Mortgage Choice
J. Risk Financial Manag. 2017, 10(1), 1; doi:10.3390/jrfm10010001
Received: 6 October 2016 / Revised: 23 December 2016 / Accepted: 27 December 2016 / Published: 3 January 2017
Cited by 1 | PDF Full-text (1119 KB) | HTML Full-text | XML Full-text
Abstract
This paper studies the optimal mortgage choice of an investor in a simple bond market with a stochastic interest rate and access to term life insurance. The study is based on advances in stochastic control theory, which provides analytical solutions to portfolio problems
[...] Read more.
This paper studies the optimal mortgage choice of an investor in a simple bond market with a stochastic interest rate and access to term life insurance. The study is based on advances in stochastic control theory, which provides analytical solutions to portfolio problems with a stochastic interest rate. We derive the optimal portfolio of a mortgagor in a simple framework and formulate stylized versions of mortgage products offered in the market today. This allows us to analyze the optimal investment strategy in terms of optimal mortgage choice. We conclude that certain extreme investors optimally choose either a traditional fixed rate mortgage or an adjustable rate mortgage, while investors with moderate risk aversion and income prefer a mix of the two. By matching specific investor characteristics to existing mortgage products, our study provides a better understanding of the complex and yet restricted mortgage choice faced by many household investors. In addition, the simple analytical framework enables a detailed analysis of how changes to market, income and preference parameters affect the optimal mortgage choice. Full article
Figures

Figure 1

Open AccessArticle Capital Structure Arbitrage under a Risk-Neutral Calibration
J. Risk Financial Manag. 2017, 10(1), 3; doi:10.3390/jrfm10010003
Received: 18 October 2016 / Revised: 7 January 2017 / Accepted: 10 January 2017 / Published: 19 January 2017
PDF Full-text (3881 KB) | HTML Full-text | XML Full-text
Abstract
By reinterpreting the calibration of structural models, a reassessment of the importance of the input variables is undertaken. The analysis shows that volatility is the key parameter to any calibration exercise, by several orders of magnitude. To maximize the sensitivity to volatility, a
[...] Read more.
By reinterpreting the calibration of structural models, a reassessment of the importance of the input variables is undertaken. The analysis shows that volatility is the key parameter to any calibration exercise, by several orders of magnitude. To maximize the sensitivity to volatility, a simple formulation of Merton’s model is proposed that employs deep out-of-the-money option implied volatilities. The methodology also eliminates the use of historic data to specify the default barrier, thereby leading to a full risk-neutral calibration. Subsequently, a new technique for identifying and hedging capital structure arbitrage opportunities is illustrated. The approach seeks to hedge the volatility risk, or vega, as opposed to the exposure from the underlying equity itself, or delta. The results question the efficacy of the common arbitrage strategy of only executing the delta hedge. Full article
(This article belongs to the Special Issue Financial Derivatives and Hedging)
Figures

Figure 1

Open AccessArticle Determination of the Optimal Retention Level Based on Different Measures
J. Risk Financial Manag. 2017, 10(1), 4; doi:10.3390/jrfm10010004
Received: 6 December 2016 / Revised: 17 January 2017 / Accepted: 18 January 2017 / Published: 25 January 2017
PDF Full-text (326 KB) | HTML Full-text | XML Full-text
Abstract
This paper deals with the optimal retention level under four competitive criteria: survival probability, expected profit, variance and expected shortfall of the insurer’s risk. The aggregate claim amounts are assumed to be distributed as compound Poisson, and the individual claim amounts are distributed
[...] Read more.
This paper deals with the optimal retention level under four competitive criteria: survival probability, expected profit, variance and expected shortfall of the insurer’s risk. The aggregate claim amounts are assumed to be distributed as compound Poisson, and the individual claim amounts are distributed exponentially. We present an approach to determine the optimal retention level that maximizes the expected profit and the survival probability, whereas minimizing the variance and the expected shortfall of the insurer’s risk. In the decision making process, we concentrate on multi-attribute decision making methods: the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods with their extended versions. We also provide comprehensive analysis for the determination of the optimal retention level under both the expected value and standard deviation premium principles. Full article
Figures

Figure 1

Open AccessArticle Accurate Evaluation of Expected Shortfall for Linear Portfolios with Elliptically Distributed Risk Factors
J. Risk Financial Manag. 2017, 10(1), 5; doi:10.3390/jrfm10010005
Received: 31 July 2016 / Revised: 28 December 2016 / Accepted: 24 January 2017 / Published: 2 February 2017
PDF Full-text (774 KB) | HTML Full-text | XML Full-text
Abstract
We provide an accurate closed-form expression for the expected shortfall of linear portfolios with elliptically distributed risk factors. Our results aim to correct inaccuracies that originate in Kamdem (2005) and are present also in at least thirty other papers referencing it, including the
[...] Read more.
We provide an accurate closed-form expression for the expected shortfall of linear portfolios with elliptically distributed risk factors. Our results aim to correct inaccuracies that originate in Kamdem (2005) and are present also in at least thirty other papers referencing it, including the recent survey by Nadarajah et al. (2014) on estimation methods for expected shortfall. In particular, we show that the correction we provide in the popular multivariate Student t setting eliminates understatement of expected shortfall by a factor varying from at least four to more than 100 across different tail quantiles and degrees of freedom. As such, the resulting economic impact in financial risk management applications could be significant. We further correct such errors encountered also in closely related results in Kamdem (2007 and 2009) for mixtures of elliptical distributions. More generally, our findings point to the extra scrutiny required when deploying new methods for expected shortfall estimation in practice. Full article
(This article belongs to the Special Issue Advances in Modeling Value at Risk and Expected Shortfall)
Figures

Figure 1

Open AccessArticle Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model
J. Risk Financial Manag. 2017, 10(1), 6; doi:10.3390/jrfm10010006
Received: 22 August 2016 / Revised: 18 January 2017 / Accepted: 19 January 2017 / Published: 5 February 2017
PDF Full-text (3438 KB) | HTML Full-text | XML Full-text
Abstract
Neural networks are well suited to predict future results of time series for various data types. This paper proposes a hybrid neural network model to describe the results of the database of the New York Stock Exchange (NYSE). This hybrid model brings together
[...] Read more.
Neural networks are well suited to predict future results of time series for various data types. This paper proposes a hybrid neural network model to describe the results of the database of the New York Stock Exchange (NYSE). This hybrid model brings together a self organizing map (SOM) with a multilayer perceptron with back propagation algorithm (MLP-BP). The SOM aims to segment the database into different clusters, where the differences between them are highlighted. The MLP-BP is used to construct a descriptive mathematical model that describes the relationship between the indicators and the closing value of each cluster. The model was developed from a database consisting of the NYSE Composite US 100 Index over the period of 2 April 2004 to 31 December 2015. As input variables for neural networks, ten technical financial indicators were used. The model results were fairly accurate, with a mean absolute percentage error varying between 0.16% and 0.38%. Full article
Figures

Figure 1

Open AccessArticle On the Power and Size Properties of Cointegration Tests in the Light of High-Frequency Stylized Facts
J. Risk Financial Manag. 2017, 10(1), 7; doi:10.3390/jrfm10010007
Received: 10 October 2016 / Revised: 12 December 2016 / Accepted: 31 January 2017 / Published: 7 February 2017
Cited by 2 | PDF Full-text (813 KB) | HTML Full-text | XML Full-text
Abstract
This paper establishes a selection of stylized facts for high-frequency cointegrated processes, based on one-minute-binned transaction data. A methodology is introduced to simulate cointegrated stock pairs, following none, some or all of these stylized facts. AR(1)-GARCH(1,1) and MR(3)-STAR(1)-GARCH(1,1) processes contaminated with reversible and
[...] Read more.
This paper establishes a selection of stylized facts for high-frequency cointegrated processes, based on one-minute-binned transaction data. A methodology is introduced to simulate cointegrated stock pairs, following none, some or all of these stylized facts. AR(1)-GARCH(1,1) and MR(3)-STAR(1)-GARCH(1,1) processes contaminated with reversible and non-reversible jumps are used to model the cointegration relationship. In a Monte Carlo simulation, the power and size properties of ten cointegration tests are assessed. We find that in high-frequency settings typical for stock price data, power is still acceptable, with the exception of strong or very frequent non-reversible jumps. Phillips–Perron and PGFF tests perform best. Full article
Figures

Figure 1

Back to Top