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J. Risk Financial Manag., Volume 11, Issue 4 (December 2018)

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Open AccessArticle Bank Credit and Housing Prices in China: Evidence from a TVP-VAR Model with Stochastic Volatility
J. Risk Financial Manag. 2018, 11(4), 90; https://doi.org/10.3390/jrfm11040090
Received: 2 November 2018 / Revised: 30 November 2018 / Accepted: 11 December 2018 / Published: 15 December 2018
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Abstract
Housing prices in China have been rising rapidly in recent years, which is a cause for concern for China’s housing market. Does bank credit influence housing prices? If so, how? Will the housing prices affect the bank credit system if the market collapses?
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Housing prices in China have been rising rapidly in recent years, which is a cause for concern for China’s housing market. Does bank credit influence housing prices? If so, how? Will the housing prices affect the bank credit system if the market collapses? We aim to study the dynamic relationship between housing prices and bank credit in China from the second quarter of 2005 to the fourth quarter of 2017 by using a time-varying parameter vector autoregression (VAR) model with stochastic volatility. Furthermore, we study the relationships between housing prices and housing loans on the demand side and real estate development loans on the supply side, separately. Finally, we obtain several findings. First, the relationship between housing prices and bank credit shows significant time-varying features; second, the mutual effects of housing prices and bank credit vary between the demand side and supply side; third, influences of housing prices on all kinds of bank credit are stronger than influences in the opposite direction. Full article
(This article belongs to the Special Issue Empirical Finance)
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Open AccessArticle Contagion Risks in Emerging Stock Markets: New Evidence from Asia and Latin America
J. Risk Financial Manag. 2018, 11(4), 89; https://doi.org/10.3390/jrfm11040089
Received: 26 September 2018 / Revised: 29 November 2018 / Accepted: 3 December 2018 / Published: 14 December 2018
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Abstract
The purpose of this study is to investigate whether contagion actually occurred during three well-known financial crises in 1990s and 2000s: Mexican “Tequila” crisis in 1994, Asian “flu” crisis in 1997 and US subprime crisis in 2007. We apply dynamic conditional correlation models
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The purpose of this study is to investigate whether contagion actually occurred during three well-known financial crises in 1990s and 2000s: Mexican “Tequila” crisis in 1994, Asian “flu” crisis in 1997 and US subprime crisis in 2007. We apply dynamic conditional correlation models (DCC-GARCH(1,1)) to daily stock-index returns of eight Asian stock markets, six Latin American stock markets and US stock market. Defining contagion as a significant increase of dynamic conditional correlations, we test for contagion by using a difference test for DCC means. The results obtained shows that there is a pure contagion from crisis-originating markets to other emerging stock markets during these three crisis. However, the contagion effects are different from one crisis to the other. Firstly, during the Mexican crisis, contagion is detected in only the Latin American region. Secondly, during the Asian crisis, we find evidence of contagion in some markets in both the Asian and Latin American regions. Finally, contagion is proved to be present in all stock markets with the only exception for Brazil during US subprime crisis. Full article
(This article belongs to the collection Trends in Emerging Markets Finance, Institutions and Money)
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Open AccessArticle Friendship of Stock Market Indices: A Cluster-Based Investigation of Stock Markets
J. Risk Financial Manag. 2018, 11(4), 88; https://doi.org/10.3390/jrfm11040088
Received: 3 November 2018 / Revised: 7 December 2018 / Accepted: 11 December 2018 / Published: 13 December 2018
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Abstract
This paper introduces a spectral clustering-based method to show that stock prices contain not only firm but also network-level information. We cluster different stock indices and reconstruct the equity index graph from historical daily closing prices. We show that tail events have a
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This paper introduces a spectral clustering-based method to show that stock prices contain not only firm but also network-level information. We cluster different stock indices and reconstruct the equity index graph from historical daily closing prices. We show that tail events have a minor effect on the equity index structure. Moreover, covariance and Shannon entropy do not provide enough information about the network. However, Gaussian clusters can explain a substantial part of the total variance. In addition, cluster-wise regressions provide significant and stationer results. Full article
(This article belongs to the Special Issue Mathematical Finance with Applications)
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Open AccessArticle On a New Corporate Bond Pricing Model with Potential Credit Rating Change and Stochastic Interest Rate
J. Risk Financial Manag. 2018, 11(4), 87; https://doi.org/10.3390/jrfm11040087
Received: 12 November 2018 / Accepted: 30 November 2018 / Published: 6 December 2018
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Abstract
In this paper, we consider a new corporate bond-pricing model with credit-rating migration risks and a stochastic interest rate. In the new model, the criterion for rating change is based on a predetermined ratio of the corporation’s total asset and debt. Moreover, the
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In this paper, we consider a new corporate bond-pricing model with credit-rating migration risks and a stochastic interest rate. In the new model, the criterion for rating change is based on a predetermined ratio of the corporation’s total asset and debt. Moreover, the rating changes are allowed to happen a finite number of times during the life-span of the bond. The volatility of a corporate bond price may have a jump when a credit rating for the bond is changed. Moreover, the volatility of the bond is also assumed to depend on the interest rate. This new model improves the previous existing bond models in which the rating change is only allowed to occur once with an interest-dependent volatility or multi-ratings with constant interest rate. By using a Feynman-Kac formula, we obtain a free boundary problem. Global existence and uniqueness are established when the interest rate follows a Vasicek’s stochastic process. Calibration of the model parameters and some numerical calculations are shown. Full article
(This article belongs to the Special Issue Corporate Debt)
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Open AccessArticle Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform
J. Risk Financial Manag. 2018, 11(4), 86; https://doi.org/10.3390/jrfm11040086
Received: 1 November 2018 / Revised: 27 November 2018 / Accepted: 1 December 2018 / Published: 4 December 2018
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Abstract
We propose a novel approach that combines random forests and the wavelet transform to model the prediction of currency crises. Our classification model of random forests, built using both standard predictors and wavelet predictors, and obtained from the wavelet transform, achieves a demonstrably
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We propose a novel approach that combines random forests and the wavelet transform to model the prediction of currency crises. Our classification model of random forests, built using both standard predictors and wavelet predictors, and obtained from the wavelet transform, achieves a demonstrably high level of predictive accuracy. We also use variable importance measures to find that wavelet predictors are key predictors of crises. In particular, we find that real exchange rate appreciation and overvaluation, which are measured over a horizon of 16–32 months, are the most important. Full article
(This article belongs to the Special Issue Empirical Finance)
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Open AccessArticle Assessment of Upstream Petroleum Fiscal Regimes in Myanmar
J. Risk Financial Manag. 2018, 11(4), 85; https://doi.org/10.3390/jrfm11040085
Received: 28 October 2018 / Revised: 25 November 2018 / Accepted: 28 November 2018 / Published: 1 December 2018
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Abstract
This study aims to assess Myanmar’s upstream petroleum fiscal regimes by applying comprehensive indicators to rank the level of attractiveness of Myanmar. The indicators include government take (GT), front loading index (FLI), and composite score (CS). The decision maker’s attitude for GT and
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This study aims to assess Myanmar’s upstream petroleum fiscal regimes by applying comprehensive indicators to rank the level of attractiveness of Myanmar. The indicators include government take (GT), front loading index (FLI), and composite score (CS). The decision maker’s attitude for GT and FLI were considered in CS linear weighting method in ranking the fiscal terms attractiveness. The results showed that Myanmar’s upstream petroleum fiscal regime has low attraction compared to its competing countries from the investor’s point of view, both in terms of the risk to the investor in the earlier part of the project and in terms of evaluation with or without the time value of money. Also, royalty and cost recovery were identified to have an impact on the attractiveness rank of petroleum fiscal regime in Myanmar. Therefore, Myanmar should consider improving its fiscal regimes that are not neutral—particularly, royalty, tax, profit split, and cost recovery—for a favorable investment climate. Full article
(This article belongs to the collection Trends in Emerging Markets Finance, Institutions and Money)
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Open AccessArticle Forecasting Volatility: Evidence from the Saudi Stock Market
J. Risk Financial Manag. 2018, 11(4), 84; https://doi.org/10.3390/jrfm11040084
Received: 24 October 2018 / Revised: 19 November 2018 / Accepted: 23 November 2018 / Published: 28 November 2018
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Abstract
The purpose of this paper is to evaluate the forecasting performance of linear and non-linear generalized autoregressive conditional heteroskedasticity (GARCH)–class models in terms of their in-sample and out-of-sample forecasting accuracy for the Tadawul All Share Index (TASI) and the Tadawul Industrial Petrochemical Industries
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The purpose of this paper is to evaluate the forecasting performance of linear and non-linear generalized autoregressive conditional heteroskedasticity (GARCH)–class models in terms of their in-sample and out-of-sample forecasting accuracy for the Tadawul All Share Index (TASI) and the Tadawul Industrial Petrochemical Industries Share Index (TIPISI) for petrochemical industries. We use the daily price data of the TASI and the TIPISI for the period of 10 September 2007 to 26 February 2015. The results suggest that the Asymmetric Power of ARCH (APARCH) model is the most accurate model in the GARCH class for forecasting the volatility of both the TASI and the TIPISI in the context of petrochemical industries, as this model outperforms the other models in model estimation and daily out-of-sample volatility forecasting of the two indices. This study is useful for the dataset examined, because the results provide a basis for traders, policy-makers, and international investors to make decisions using this model to forecast the risks associated with investing in the Saudi stock market, within certain limitations. Full article
(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)
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Open AccessArticle Inflation Propensity of Collatz Orbits: A New Proof-of-Work for Blockchain Applications
J. Risk Financial Manag. 2018, 11(4), 83; https://doi.org/10.3390/jrfm11040083
Received: 20 September 2018 / Revised: 16 November 2018 / Accepted: 19 November 2018 / Published: 27 November 2018
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Abstract
Cryptocurrencies such as Bitcoin rely on a proof-of-work system to validate transactions and prevent attacks or double-spending. A new proof-of-work is introduced which seems to be the first number theoretic proof-of-work unrelated to primes: it is based on a new metric associated to
[...] Read more.
Cryptocurrencies such as Bitcoin rely on a proof-of-work system to validate transactions and prevent attacks or double-spending. A new proof-of-work is introduced which seems to be the first number theoretic proof-of-work unrelated to primes: it is based on a new metric associated to the Collatz algorithm whose natural generalization is algorithmically undecidable: the inflation propensity is defined as the cardinality of new maxima in a developing Collatz orbit. It is numerically verified that the distribution of inflation propensity slowly converges to a geometric distribution of parameter 0.714 ( π 1 ) 3 as the sample size increases. This pseudo-randomness opens the door to a new class of proofs-of-work based on congruential graphs. Full article
(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)
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Open AccessFeature PaperArticle Capital Allocation in Decentralized Businesses
J. Risk Financial Manag. 2018, 11(4), 82; https://doi.org/10.3390/jrfm11040082
Received: 23 October 2018 / Revised: 19 November 2018 / Accepted: 22 November 2018 / Published: 26 November 2018
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Abstract
This paper described a theory of capital allocation for decentralized businesses, taking into account the costs associated with risk capital. We derive an adjusted present value expression for making investment decisions, that incorporates the time varying profile of risk capital. We discuss the
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This paper described a theory of capital allocation for decentralized businesses, taking into account the costs associated with risk capital. We derive an adjusted present value expression for making investment decisions, that incorporates the time varying profile of risk capital. We discuss the implications for business performance measurement. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling)
Open AccessArticle The Relationship between Economic Freedom and FDI versus Economic Growth: Evidence from the GCC Countries
J. Risk Financial Manag. 2018, 11(4), 81; https://doi.org/10.3390/jrfm11040081
Received: 6 November 2018 / Revised: 16 November 2018 / Accepted: 18 November 2018 / Published: 22 November 2018
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Abstract
This study aims to explain the role of economic freedom in attracting foreign investments and thus raising the level of economic growth. Through a study based on a sample composed of the Gulf Cooperation Council (GCC) countries. A standard model consisting of GCC
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This study aims to explain the role of economic freedom in attracting foreign investments and thus raising the level of economic growth. Through a study based on a sample composed of the Gulf Cooperation Council (GCC) countries. A standard model consisting of GCC countries (Saudi Arabia, United Arab Emirates, Qatar, Kuwait, and Oman) was used during the period from 1995 to 2017. We based on the analytical descriptive and secondly, we used a multivariate analysis based on the panel unit root test, the cointegration and finally the regression Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) following the existence of a long-term integration, which includes the modern standard methods to determine the role of economic freedom in raising foreign direct investment and thus economic growth in the second stage. The research findings from GCC countries support the literature, suggesting that there are indeed some indications that greater levels of economic freedom support higher rates of economic growth in a country. Full article
(This article belongs to the Special Issue Applied Econometrics)
Open AccessArticle Blockchain-Based ICOs: Pure Hype or the Dawn of a New Era of Startup Financing?
J. Risk Financial Manag. 2018, 11(4), 80; https://doi.org/10.3390/jrfm11040080
Received: 23 October 2018 / Accepted: 13 November 2018 / Published: 21 November 2018
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Abstract
This study explores the determinants of initial coin offering (ICO) success, where success is defined as the amount of capital a project could raise. ICOs are a tool for startups in the blockchain ecosystem to raise early capital with relative ease. The market
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This study explores the determinants of initial coin offering (ICO) success, where success is defined as the amount of capital a project could raise. ICOs are a tool for startups in the blockchain ecosystem to raise early capital with relative ease. The market for ICOs has grown at a rapid pace since its start in 2013. We analyze a unique dataset of 278 projects that finished their ICOs by August 2017 to assess determinants of funding success that we derive from the crowdfunding and venture capital literature. Our results show that ICOs exhibit similarities to classical crowdfunding and venture capital markets. Specifically, we identify resemblances in determinants of funding success regarding human capital characteristics, business model quality, project elaboration, and social media activity. Full article
(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)
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Open AccessArticle Capital Adequacy, Deposit Insurance, and the Effect of Their Interaction on Bank Risk
J. Risk Financial Manag. 2018, 11(4), 79; https://doi.org/10.3390/jrfm11040079
Received: 20 September 2018 / Revised: 14 November 2018 / Accepted: 15 November 2018 / Published: 19 November 2018
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Abstract
This paper investigates how deposit insurance and capital adequacy affect bank risk for five developed and nine emerging markets over the period of 1992–2015. Although full coverage of deposit insurance induces moral hazard by banks, deposit insurance is still an effective tool, especially
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This paper investigates how deposit insurance and capital adequacy affect bank risk for five developed and nine emerging markets over the period of 1992–2015. Although full coverage of deposit insurance induces moral hazard by banks, deposit insurance is still an effective tool, especially during the time of crisis. On the contrary, capital adequacy by itself does not effectively perform the monitoring role and leads to the asset substitution problem. Implementing the safety nets of both deposit insurance and capital adequacy together could be a sustainable financial architecture. Immediate-effect analysis reveals that the interplay between deposit insurance and capital adequacy is indispensable for banking system stability. Full article
(This article belongs to the Special Issue Commercial Banking)
Open AccessArticle Incorporating Credit Quality in Bank Efficiency Measurements: A Directional Distance Function Approach
J. Risk Financial Manag. 2018, 11(4), 78; https://doi.org/10.3390/jrfm11040078
Received: 20 August 2018 / Revised: 28 October 2018 / Accepted: 6 November 2018 / Published: 9 November 2018
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Abstract
The objective of the study was to measure the risk-adjusted efficiency of banks in 24 emerging economies for the period of 1999–2013. A two-stage network data envelopment analysis (DEA), with separate deposit mobilization and loan financing stages was used. Efficiency was measured using
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The objective of the study was to measure the risk-adjusted efficiency of banks in 24 emerging economies for the period of 1999–2013. A two-stage network data envelopment analysis (DEA), with separate deposit mobilization and loan financing stages was used. Efficiency was measured using directional distance functions with DEA, featuring non-performing loans as undesirable outputs. The distributions of efficiency scores were different when credit quality was taken into account. The distribution of efficiency scores varied systematically with accumulation of non-performing loans across regions. The financial crisis of 2007–2008 impacted more adversely the regions that had higher proportions of non-performing loans in banks’ portfolios. The results of a follow-on non-parametric regression showed that smaller, better capitalized, and private banks were more efficient. The conditions conducive for high levels of technical efficiency by banks were found to be characterized by economic growth and low inflation. Full article
(This article belongs to the collection Trends in Emerging Markets Finance, Institutions and Money)
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Open AccessFeature PaperReview On the Rising Complexity of Bank Regulatory Capital Requirements: From Global Guidelines to their United States (US) Implementation
J. Risk Financial Manag. 2018, 11(4), 77; https://doi.org/10.3390/jrfm11040077
Received: 2 October 2018 / Revised: 19 October 2018 / Accepted: 30 October 2018 / Published: 1 November 2018
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Abstract
After the Latin American Debt Crisis of 1982, the official response worldwide turned to minimum capital standards to promote stable banking systems. Despite their existence, however, such standards have still not prevented periodic disruptions in the banking sectors of various countries. After the
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After the Latin American Debt Crisis of 1982, the official response worldwide turned to minimum capital standards to promote stable banking systems. Despite their existence, however, such standards have still not prevented periodic disruptions in the banking sectors of various countries. After the 2007–2009 crisis, bank capital requirements have, in some cases, increased and overall have become even more complex. This paper reviews (1) how Basel-style capital adequacy guidelines have evolved, becoming higher in some cases and overall more complex, (2) how the United States (US) implementation of these guidelines has contributed to regulatory complexity, even when omitting other bank capital regulations that are specific to the US, and (3) how the US regulatory measures still do not provide equally valuable information about whether a bank is adequately capitalized. Full article
(This article belongs to the Special Issue Financial Crises, Macroeconomic Management, and Financial Regulation)
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Open AccessArticle Stock Market Volatility and Trading Volume: A Special Case in Hong Kong With Stock Connect Turnover
J. Risk Financial Manag. 2018, 11(4), 76; https://doi.org/10.3390/jrfm11040076
Received: 5 September 2018 / Revised: 18 October 2018 / Accepted: 25 October 2018 / Published: 31 October 2018
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Abstract
The cross-boundary Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connect provides a special data set to study the dynamic relationships among volatility, trading volume and turnover among three stock markets, namely Shanghai, Shenzhen, and Hong Kong. We employ the Granger Causality test with the
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The cross-boundary Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connect provides a special data set to study the dynamic relationships among volatility, trading volume and turnover among three stock markets, namely Shanghai, Shenzhen, and Hong Kong. We employ the Granger Causality test with the vector autoregressive model (VAR) to examine whether Stock Connect turnover contributes to future realized volatility and market volume of these three markets. Our results support the evidence of causality from Stock Connect turnover to market volatility and trading volume. The finding of this causality is consistent with the implication of the sequential information arrival model in the literature. Full article
(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)
Open AccessArticle Insurance Risks Management Methodology
J. Risk Financial Manag. 2018, 11(4), 75; https://doi.org/10.3390/jrfm11040075
Received: 12 September 2018 / Revised: 24 October 2018 / Accepted: 26 October 2018 / Published: 30 October 2018
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Abstract
The purposes of the study are to substantiate the influence of the specific features of insurance on the set of management accounting objects and to develop a mechanism of preparing the relevant information for insurance risk management. Management accounting allows generating reports, specially
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The purposes of the study are to substantiate the influence of the specific features of insurance on the set of management accounting objects and to develop a mechanism of preparing the relevant information for insurance risk management. Management accounting allows generating reports, specially prepared for managers of various levels of control (in contrast to financial accounting, which considers information on the basis of general accounting rules). This allows realizing the main goal of management accounting; that is, providing information support for management decisions aimed at maximizing the organization’s profits. The object of research is management accounting in the information system of an insurance company. The stages in the execution of accounting procedures in a management accounting system are defined in the form of a diagram, the features of insurance affecting the organization of management accounting are classified, and an intracompany ledger of the connection between the segments of activity and responsibility centers is developed for insurance companies. Full article
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Open AccessArticle Measuring Financial Fragmentation in the Euro Area Corporate Bond Market
J. Risk Financial Manag. 2018, 11(4), 74; https://doi.org/10.3390/jrfm11040074
Received: 30 September 2018 / Revised: 20 October 2018 / Accepted: 24 October 2018 / Published: 29 October 2018
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Abstract
This paper analyses the determinants of euro area non-financial corporate bonds since the early 2000s, so as to gauge deviations from the law of one price. We decompose the spread between the yield of German, French, Italian and Spanish corporate bonds vis-à-vis the
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This paper analyses the determinants of euro area non-financial corporate bonds since the early 2000s, so as to gauge deviations from the law of one price. We decompose the spread between the yield of German, French, Italian and Spanish corporate bonds vis-à-vis the German Bund of similar maturity into country, credit and duration risk premia components via dummy regressions. We highlight three main findings. First, the initial phase of the financial crisis (2008–2009) caused an overall increase in credit risk premia. Since the beginning of 2013 credit risk premia are back to levels comparable to those preceding the financial crisis. Second, at the height of the euro area sovereign crisis (2011–2012), high credit risk premia were accompanied by strong and persistent signs of market fragmentation in Italy and Spain (but not in France). This fragmentation has reached its peak in the second half of 2012 and has started to recede only after the announcement of the OMT. Third, we provide a simple measure of financial integration across the big 4 member states of the euro area. Full article
(This article belongs to the Special Issue Corporate Debt)
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Open AccessArticle Price Discovery and the Accuracy of Consolidated Data Feeds in the U.S. Equity Markets
J. Risk Financial Manag. 2018, 11(4), 73; https://doi.org/10.3390/jrfm11040073
Received: 30 September 2018 / Revised: 25 October 2018 / Accepted: 25 October 2018 / Published: 28 October 2018
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Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor—the data feed consolidating all trades and quotes across the US stock market. Rather than the speed of the Securities Information Processor (SIP), we
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Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor—the data feed consolidating all trades and quotes across the US stock market. Rather than the speed of the Securities Information Processor (SIP), we focus here on its accuracy. Relying on Trade and Quote data, we provide various measures of SIP latency relative to high-speed data feeds between exchanges, known as direct feeds. We use first differences to highlight not only the divergence between the direct feeds and the SIP, but also the fundamental inaccuracy of the SIP. We find that as many as 60% or more of trades are reported out of sequence for stocks with high trade volume, therefore skewing simple measures, such as returns. While not yet definitive, this analysis supports our preliminary conclusion that the underlying infrastructure of the SIP is currently unable to keep pace with the trading activity in today’s stock market. Full article
(This article belongs to the Special Issue Empirical Finance)
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Open AccessArticle Volatility Spillovers Arising from the Financialization of Commodities
J. Risk Financial Manag. 2018, 11(4), 72; https://doi.org/10.3390/jrfm11040072
Received: 7 September 2018 / Revised: 17 October 2018 / Accepted: 25 October 2018 / Published: 27 October 2018
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Abstract
This paper examines whether the proliferation of new index products, such as commodity-tracking exchange-traded funds (ETFs), amplified the volatility transmission channel introduced by financialization. This paper focuses on the volatility spillover effects among crude oil, metals, agriculture, and non-energy commodity markets. The results
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This paper examines whether the proliferation of new index products, such as commodity-tracking exchange-traded funds (ETFs), amplified the volatility transmission channel introduced by financialization. This paper focuses on the volatility spillover effects among crude oil, metals, agriculture, and non-energy commodity markets. The results show financialization has an impact on the volatility of commodity prices, predominantly for non-energy commodities. However, the impact on volatility is not symmetric across all commodities. The analysis of index investment and investors’ positions in futures markets shows that, when a relationship exists, it is generally negatively correlated with the realized volatility of non-energy commodities. Using realized volatility in the difference-in-difference model provides estimates that are inconsistent with other findings that non-energy commodities, traded as a part of indices, have experienced higher volatility. The results are similar to the index investment and futures market analysis, where increased participation by investors through new investment products has put download pressure on realized volatility. Full article
(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)
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Open AccessArticle Unconventional U.S. Monetary Policy: New Tools, Same Channels?
J. Risk Financial Manag. 2018, 11(4), 71; https://doi.org/10.3390/jrfm11040071
Received: 1 October 2018 / Revised: 18 October 2018 / Accepted: 25 October 2018 / Published: 27 October 2018
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Abstract
In this paper, we compare the transmission of a conventional monetary policy shock with that of an unexpected decrease in the term spread, which mirrors quantitative easing. Employing a time-varying vector autoregression with stochastic volatility, our results are two-fold: First, the spread shock
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In this paper, we compare the transmission of a conventional monetary policy shock with that of an unexpected decrease in the term spread, which mirrors quantitative easing. Employing a time-varying vector autoregression with stochastic volatility, our results are two-fold: First, the spread shock works mainly through a boost to consumer wealth growth, while a conventional monetary policy shock affects real output growth via a broad credit/bank lending channel. Second, both shocks exhibit a distinct pattern over our sample period. More specifically, we find small output effects of a conventional monetary policy shock during the period of the global financial crisis and stronger effects in its aftermath. This might imply that when the central bank has left the policy rate unaltered for an extended period of time, a policy surprise might boost output particularly strongly. By contrast, the spread shock has affected output growth most strongly during the period of the global financial crisis and less so thereafter. This might point to diminishing effects of large-scale asset purchase programs. Full article
(This article belongs to the Special Issue Bayesian Econometrics)
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Open AccessArticle Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning
J. Risk Financial Manag. 2018, 11(4), 70; https://doi.org/10.3390/jrfm11040070
Received: 30 September 2018 / Revised: 14 October 2018 / Accepted: 24 October 2018 / Published: 26 October 2018
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Abstract
Since not all suppliers are to be managed in the same way, a purchasing strategy requires proper supplier segmentation so that the most suitable strategies can be used for different segments. Most existing methods for supplier segmentation, however, either depend on subjective judgements
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Since not all suppliers are to be managed in the same way, a purchasing strategy requires proper supplier segmentation so that the most suitable strategies can be used for different segments. Most existing methods for supplier segmentation, however, either depend on subjective judgements or require significant efforts. To overcome the limitations, this paper proposes a novel approach for supplier segmentation. The objective of this paper is to develop an automated and effective way to identify core suppliers, whose profit impact on a buyer is significant. To achieve this objective, the application of a supervised machine learning technique, Random Forests (RF), to e-invoice data is proposed. To validate the effectiveness, the proposed method has been applied to real e-invoice data obtained from an automobile parts manufacturer. Results of high accuracy and the area under the curve (AUC) attest to the applicability of our approach. Our method is envisioned to be of value for automating the identification of core suppliers. The main benefits of the proposed approach include the enhanced efficiency of supplier segmentation procedures. Besides, by utilizing a machine learning method to e-invoice data, our method results in more reliable segmentation in terms of selecting and weighting variables. Full article
(This article belongs to the collection Supply Chain Management)
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Open AccessArticle Risk Assessment of Housing Market Segments: The Lender’s Perspective
J. Risk Financial Manag. 2018, 11(4), 69; https://doi.org/10.3390/jrfm11040069
Received: 30 August 2018 / Revised: 8 October 2018 / Accepted: 14 October 2018 / Published: 26 October 2018
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It is well known that risk factors influence how investment portfolios perform from a lender’s perspective; therefore, a thorough risk assessment of the housing market is vital. The aim of this paper was to analyze the risks from housing apartments in different housing
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It is well known that risk factors influence how investment portfolios perform from a lender’s perspective; therefore, a thorough risk assessment of the housing market is vital. The aim of this paper was to analyze the risks from housing apartments in different housing market segments by using the Stockholm, Sweden, owner-occupied apartment market as a case study. By applying quantitative and systems engineering methods, we (1) established the relationship between the overall housing market and several housing market segments, (2) analyzed the results from the quantitative model, and (3) finally provided a feasible portfolio regarding risk control based on the given data. The goal was to determine how different housing segment factors could reveal risk towards the overall market and offer better outlooks for risk management when it comes to housing apartments. The results indicated that the risk could be reduced at the same time as the return increased. From a lender’s perspective, this could reduce the overall risk. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling)
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Open AccessBook Review Book Review for “Credit Default Swap Markets in the Global Economy” by Go Tamakoshi and Shigeyuki Hamori. Routledge: Oxford, UK, 2018; ISBN: 9781138244726
J. Risk Financial Manag. 2018, 11(4), 68; https://doi.org/10.3390/jrfm11040068
Received: 22 October 2018 / Accepted: 24 October 2018 / Published: 25 October 2018
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Abstract
Credit default swaps (CDS) came into existence in 1994 when they were invented by JP Morgan, then it became popular in the early 2000s, and by 2007, the outstanding credit default swaps balance reached $62 trillion. [...] Full article
(This article belongs to the Special Issue Empirical Finance)
Open AccessArticle Between ℙ and ℚ: The ℙ Measure for Pricing in Asset Liability Management
J. Risk Financial Manag. 2018, 11(4), 67; https://doi.org/10.3390/jrfm11040067
Received: 17 September 2018 / Revised: 18 October 2018 / Accepted: 21 October 2018 / Published: 24 October 2018
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Abstract
Insurance companies issue guarantees that need to be valued according to the market expectations. By calibrating option pricing models to the available implied volatility surfaces, one deals with the so-called risk-neutral measure Q, which can be used to generate market consistent values
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Insurance companies issue guarantees that need to be valued according to the market expectations. By calibrating option pricing models to the available implied volatility surfaces, one deals with the so-called risk-neutral measure Q , which can be used to generate market consistent values for these guarantees. For asset liability management, insurers also need future values of these guarantees. Next to that, new regulations require insurance companies to value their positions on a one-year horizon. As the option prices at t = 1 are unknown, it is common practice to assume that the parameters of these option pricing models are constant, i.e., the calibrated parameters from time t = 0 are also used to value the guarantees at t = 1 . However, it is well-known that the parameters are not constant and may depend on the state of the market which evolves under the real-world measure P . In this paper, we propose improved regression models that, given a set of market variables such as the VIX index and risk-free interest rates, estimate the calibrated parameters. When the market variables are included in a real-world simulation, one is able to assess the calibrated parameters (and consequently the implied volatility surface) in line with the simulated state of the market. By performing a regression, we are able to predict out-of-sample implied volatility surfaces accurately. Moreover, the impact on the Solvency Capital Requirement has been evaluated for different points in time. The impact depends on the initial state of the market and may vary between −46% and +52%. Full article
(This article belongs to the Special Issue Computational Finance)
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Open AccessArticle Are There Any Volatility Spill-Over Effects among Cryptocurrencies and Widely Traded Asset Classes?
J. Risk Financial Manag. 2018, 11(4), 66; https://doi.org/10.3390/jrfm11040066
Received: 4 September 2018 / Revised: 14 October 2018 / Accepted: 18 October 2018 / Published: 23 October 2018
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Abstract
In the present paper, we investigate connectedness within cryptocurrency markets as well as across the Bitcoin index (hereafter, BPI) and widely traded asset classes such as traditional currencies, stock market indices and commodities, such as gold and Brent oil. A spill over index
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In the present paper, we investigate connectedness within cryptocurrency markets as well as across the Bitcoin index (hereafter, BPI) and widely traded asset classes such as traditional currencies, stock market indices and commodities, such as gold and Brent oil. A spill over index approach with the spectral representation of variance decomposition networks, is employed to measure connectedness. Results show no significant spillover effects between the nascent market of cryptocurrencies and other financial markets. We suggest that cryptocurrencies are real independent financial instruments that pose no danger to financial system stability. Concerning the connectedness within the cryptocurrency markets, we report a time–frequency–dynamics connectedness nature. Moreover, the decomposition of the total spill over index is mostly dominated by a short frequency component (2–4 days) leading to the conclusion that this nascent market is highly speculative at present. These findings provide insights for regulators and potential international investors. Full article
(This article belongs to the Special Issue Blockchain and Cryptocurrencies)
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Open AccessArticle Systemic Approach to Management Control through Determining Factors
J. Risk Financial Manag. 2018, 11(4), 65; https://doi.org/10.3390/jrfm11040065
Received: 28 September 2018 / Revised: 11 October 2018 / Accepted: 17 October 2018 / Published: 22 October 2018
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Abstract
This article aimed to analyse the influence of the main factors on management control used in optimization activities, in order to reach the strategic goals of a company. Agency, transactional costs and contingency theories have been analysed from the traditional perspective. This study
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This article aimed to analyse the influence of the main factors on management control used in optimization activities, in order to reach the strategic goals of a company. Agency, transactional costs and contingency theories have been analysed from the traditional perspective. This study reviewed resource-based, institutional, planned behaviour and upper echelon theories, and underlined the main features of management control processes. Empirical evaluation was conducted using data collected from interviews of top management of the main and secondary segments of the Bucharest Stock Exchange. Consequently, we showed the specific features of the systemic approach to management control by means of its determining factors: control environment, management strategies and budgetary system, operational control and the performance appraisal system. Full article
(This article belongs to the Special Issue Applied Econometrics)
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Open AccessFeature PaperArticle Forecast Combinations for Structural Breaks in Volatility: Evidence from BRICS Countries
J. Risk Financial Manag. 2018, 11(4), 64; https://doi.org/10.3390/jrfm11040064
Received: 4 October 2018 / Revised: 16 October 2018 / Accepted: 17 October 2018 / Published: 21 October 2018
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Abstract
The aim of this paper is to investigate the relevance of structural breaks for forecasting the volatility of daily returns on BRICS countries (Brazil, Russia, India, China and South Africa). The data set used in the analysis is the Morgan Stanley Capital International
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The aim of this paper is to investigate the relevance of structural breaks for forecasting the volatility of daily returns on BRICS countries (Brazil, Russia, India, China and South Africa). The data set used in the analysis is the Morgan Stanley Capital International MSCI daily returns and covers the period from 19 July 1999 to 16 July 2015. To identify structural breaks in the unconditional variance, a binary segmentation algorithm with a test, which considers both the fourth order moment of the process and persistence in the variance, has been implemented. Some forecast combinations that account for the identified structural breaks have been introduced and their performance has been evaluated and compared by using the Model Confidence Set (MCS). The results give significant evidence of the relevance of the structural breaks. In particular, in the regimes identified by the structural breaks, a substantial change in the unconditional variance is quite evident. In forecasting volatility, the combination that averages forecasts obtained using different rolling estimation windows outperforms all the other combinations Full article
(This article belongs to the Special Issue Financial Time Series: Methods & Models)
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Open AccessArticle An Analysis of Bitcoin’s Price Dynamics
J. Risk Financial Manag. 2018, 11(4), 63; https://doi.org/10.3390/jrfm11040063
Received: 20 September 2018 / Revised: 8 October 2018 / Accepted: 11 October 2018 / Published: 15 October 2018
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Abstract
This paper aims to enhance the understanding of which factors affect the price development of Bitcoin in order for investors to make sound investment decisions. Previous literature has covered only a small extent of the highly volatile period during the last months of
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This paper aims to enhance the understanding of which factors affect the price development of Bitcoin in order for investors to make sound investment decisions. Previous literature has covered only a small extent of the highly volatile period during the last months of 2017 and the beginning of 2018. To examine the potential price drivers, we use the Autoregressive Distributed Lag and Generalized Autoregressive Conditional Heteroscedasticity approach. Our study identifies the technological factor Hashrate as irrelevant for modeling Bitcoin price dynamics. This irrelevance is due to the underlying code that makes the supply of Bitcoins deterministic, and it stands in contrast to previous literature that has included Hashrate as a crucial independent variable. Moreover, the empirical findings indicate that the price of Bitcoin is affected by returns on the S&P 500 and Google searches, showing consistency with results from previous literature. In contrast to previous literature, we find the CBOE volatility index (VIX), oil, gold, and Bitcoin transaction volume to be insignificant. Full article
(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)
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Open AccessArticle Market Reactions to Supply Chain Management Excellence
J. Risk Financial Manag. 2018, 11(4), 62; https://doi.org/10.3390/jrfm11040062
Received: 29 September 2018 / Revised: 9 October 2018 / Accepted: 10 October 2018 / Published: 12 October 2018
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Abstract
A highly-respected public recognition of supply chain management (SCM) excellence is the Supply Chain Top 25 List, published annually by AMR Research. By employing event study method, this study extensively examined stock market reactions to annual announcements of the AMR Supply Chain Top
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A highly-respected public recognition of supply chain management (SCM) excellence is the Supply Chain Top 25 List, published annually by AMR Research. By employing event study method, this study extensively examined stock market reactions to annual announcements of the AMR Supply Chain Top 25 List, under various market scenarios. The results showed that SCM leading firms consistently outperform market portfolios around annual press-release dates. The mean abnormal returns observed in the event window (0, +1) were positive and statistically significant. In addition, the findings were robust across different estimation models and various market indexes adopted in the event study. At the same time, it is worth noting that the event effect on market performance was temporary and diminished within 5 trading days. This study makes contributions to the growing body of knowledge on the strategic values of firm reputation in general, and for SCM excellence in particular. Full article
(This article belongs to the collection Supply Chain Management)
Open AccessFeature PaperArticle Forecasting of Realised Volatility with the Random Forests Algorithm
J. Risk Financial Manag. 2018, 11(4), 61; https://doi.org/10.3390/jrfm11040061
Received: 8 September 2018 / Revised: 8 October 2018 / Accepted: 9 October 2018 / Published: 11 October 2018
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Abstract
The paper addresses the forecasting of realised volatility for financial time series using the heterogeneous autoregressive model (HAR) and machine learning techniques. We consider an extended version of the existing HAR model with included purified implied volatility. For this extended model, we apply
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The paper addresses the forecasting of realised volatility for financial time series using the heterogeneous autoregressive model (HAR) and machine learning techniques. We consider an extended version of the existing HAR model with included purified implied volatility. For this extended model, we apply the random forests algorithm for the forecasting of the direction and the magnitude of the realised volatility. In experiments with historical high frequency data, we demonstrate improvements of forecast accuracy for the proposed model. Full article
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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