Next Issue
Volume 10, September
Previous Issue
Volume 10, March
 
 

J. Risk Financial Manag., Volume 10, Issue 2 (June 2017) – 6 articles

  • 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 Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
1198 KiB  
Article
OTC Derivatives and Global Economic Activity: An Empirical Analysis
by Gordon Bodnar, Jonathan Fortun and Jaime Marquez
J. Risk Financial Manag. 2017, 10(2), 13; https://doi.org/10.3390/jrfm10020013 - 14 Jun 2017
Cited by 1 | Viewed by 5919
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. [...] Read more.
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
(This article belongs to the Special Issue Financial Derivatives and Hedging)
Show Figures

Figure 1

1147 KiB  
Article
A Statistical Analysis of Cryptocurrencies
by Stephen Chan, Jeffrey Chu, Saralees Nadarajah and Joerg Osterrieder
J. Risk Financial Manag. 2017, 10(2), 12; https://doi.org/10.3390/jrfm10020012 - 31 May 2017
Cited by 91 | Viewed by 24430
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, [...] Read more.
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
Show Figures

Figure 1

254 KiB  
Article
The Solvency II Standard Formula, Linear Geometry, and Diversification
by Joachim Paulusch
J. Risk Financial Manag. 2017, 10(2), 11; https://doi.org/10.3390/jrfm10020011 - 18 May 2017
Cited by 2 | Viewed by 5347
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 [...] Read more.
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 L p 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
1432 KiB  
Article
A Risk Management Framework for Cloud Migration Decision Support
by Shareeful Islam, Stefan Fenz, Edgar Weippl and Haralambos Mouratidis
J. Risk Financial Manag. 2017, 10(2), 10; https://doi.org/10.3390/jrfm10020010 - 22 Apr 2017
Cited by 21 | Viewed by 13709
Abstract
Keywords: risk management framework; risk assessment; cloud migration; security; analytic hierarchy process (AHP); business value Full article
(This article belongs to the Special Issue Risk Management Based on Intelligent Information Processing)
Show Figures

Figure 1

654 KiB  
Article
Capital Regulation, the Cost of Financial Intermediation and Bank Profitability: Evidence from Bangladesh
by Changjun Zheng, Mohammed Mizanur Rahman, Munni Begum and Badar Nadeem Ashraf
J. Risk Financial Manag. 2017, 10(2), 9; https://doi.org/10.3390/jrfm10020009 - 17 Apr 2017
Cited by 22 | Viewed by 7036
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 [...] Read more.
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
(This article belongs to the Special Issue Financial Stability and Regulation / Basel III)
Show Figures

Figure 1

671 KiB  
Article
An Empirical Study on the Impact of Basel III Standards on Banks’ Default Risk: The Case of Luxembourg
by Gastón Andrés Giordana and Ingmar Schumacher
J. Risk Financial Manag. 2017, 10(2), 8; https://doi.org/10.3390/jrfm10020008 - 12 Apr 2017
Cited by 23 | Viewed by 10799
Abstract
We study how the Basel III regulations, namely the Capital-to-Assets Ratio (CAR), the Net Stable Funding Ratio (NSFR) and the Liquidity Coverage Ratio (LCR), are likely to impact banks’ profitability (i.e., ROA), capital levels and default. We estimate historical series of the new [...] Read more.
We study how the Basel III regulations, namely the Capital-to-Assets Ratio (CAR), the Net Stable Funding Ratio (NSFR) and the Liquidity Coverage Ratio (LCR), are likely to impact banks’ profitability (i.e., ROA), capital levels and default. We estimate historical series of the new Basel III regulations for a panel of Luxembourgish banks for a period covering 2003q2–2011q3. We econometrically investigate whether historical LCR and NSFR components, as well as CAR positions are able to explain the variation in a measure of a bank’s default risk (approximated by Z-score) and how these effects make their way through banks’ ROA and CAR.We find that the liquidity regulations induce a decrease in average probabilities of default. We find that the liquidity regulation focusing on maturity mismatches (i.e., NSFR) induces a decrease in average probabilities of default. Conversely, the impact on banks’ profitability is less clear-cut; what seems to matter is banks’ funding structure rather than the characteristics of the portfolio of assets. Additionally, we use a model of bank behavior to simulate the banks’ optimal adjustments of their balance sheets as if they had to adhere to the regulations starting in 2003q2. Then, we predict, using our preferred econometric model and based on the simulated data, the banks’ Z-score and ROA. The simulation exercise suggests that basically all banks would have seen a decrease in their default risk during a crisis episode if they had previously adhered to Basel III. Full article
(This article belongs to the Special Issue Financial Stability and Regulation / Basel III)
Show Figures

Figure 1

Previous Issue
Back to TopTop