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

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Research

Open AccessArticle An Empirical Study on the Impact of Basel III Standards on Banks’ Default Risk: The Case of Luxembourg
J. Risk Financial Manag. 2017, 10(2), 8; doi:10.3390/jrfm10020008
Received: 10 February 2017 / Revised: 31 March 2017 / Accepted: 1 April 2017 / Published: 12 April 2017
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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
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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)
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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
Received: 31 December 2016 / Revised: 10 April 2017 / Accepted: 12 April 2017 / Published: 17 April 2017
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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
(This article belongs to the Special Issue Financial Stability and Regulation / Basel III)
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Open AccessArticle A Risk Management Framework for Cloud Migration Decision Support
J. Risk Financial Manag. 2017, 10(2), 10; doi:10.3390/jrfm10020010
Received: 22 February 2017 / Revised: 6 April 2017 / Accepted: 10 April 2017 / Published: 22 April 2017
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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)
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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
Received: 28 February 2017 / Revised: 8 May 2017 / Accepted: 10 May 2017 / Published: 18 May 2017
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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 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
Open AccessArticle A Statistical Analysis of Cryptocurrencies
J. Risk Financial Manag. 2017, 10(2), 12; doi:10.3390/jrfm10020012
Received: 7 April 2017 / Revised: 23 May 2017 / Accepted: 27 May 2017 / Published: 31 May 2017
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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 OTC Derivatives and Global Economic Activity: An Empirical Analysis
J. Risk Financial Manag. 2017, 10(2), 13; doi:10.3390/jrfm10020013
Received: 6 April 2017 / Revised: 25 May 2017 / Accepted: 7 June 2017 / Published: 14 June 2017
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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
(This article belongs to the Special Issue Financial Derivatives and Hedging)
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