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Financial Impact of Trust and Institutional Quality around the World
Kapounek, S.; Kočenda, Evžen; Kouba, L.
2022 - English
We investigate the financial impact of social trust, institutional quality, and regulations. As a testing ground we employ a unique, large, and hand-crafted dataset of more than 850 000 lending-based crowdfunding projects from 155 platforms across 55 countries during 2005–2018. We show that the impact of social trust is positive but economically less pronounced than that of institutional trust proxied by legal and property rights protection and regulation. Moreover, the financial impact of social trust is greater at the national level, while impact of institutional quality dominates at the international level. Nevertheless, the financial impact of trust and institutional quality around the world is positive, which is an encouraging implication under increasing anonymity and internationalization of financial environment.
Keywords:
social capital; social trust; institutional trust; uncertainty; crowdfunding; financial markets
Fulltext is available at external website.
Financial Impact of Trust and Institutional Quality around the World
We investigate the financial impact of social trust, institutional quality, and regulations. As a testing ground we employ a unique, large, and hand-crafted dataset of more than 850 000 lending-based ...
Bank Survival Around the World: A Meta-Analytic Review
Kočenda, Evžen; Iwasaki, I.
2021 - English
Bank survival is essential to economic growth and development because banks mediate the financing of the economy. A bank’s overall condition is often assessed by a supervisory rating system called CAMELS, an acronym for the components Capital adequacy, Asset quality, Management quality, Earnings, Liquidity, and Sensitivity to market risk. Estimates of the impact of CAMELS components on bank survival vary widely. We perform a meta-synthesis and meta-regression analysis (MRA) using 2120 estimates collected from 50 studies. In the MRA, we account for uncertainty in moderator selection by employing Bayesian model averaging. The results of the synthesis indicate an economically negligible impact of CAMELS variables on bank survival; in addition, the effect of bank-specific, (macro)economic, and market factors is virtually absent. The results of the heterogeneity analysis and publication bias analysis are consistent in terms that they do not find an economically significant impact of the CAMELS variables. Moreover, best practice estimates show a small economic impact of CAMELS components and no impact of other factors. The study concludes that caution should be exercised when using CAMELS rating to predict bank survival or failure.
Keywords:
bank survival; bank failure; CAMELS; meta-analysis; publication selection bias
Fulltext is available at external website.
Bank Survival Around the World: A Meta-Analytic Review
Bank survival is essential to economic growth and development because banks mediate the financing of the economy. A bank’s overall condition is often assessed by a supervisory rating system called ...
Does the Spillover Index Respond Significantly to Systemic Shocks? A Bootstrap-Based Probabilistic Analysis
Greenwood-Nimmo, M.; Kočenda, Evžen; Nguyen, V. H.
2021 - English
The spillover index developed by Diebold and Yilmaz (Economic Journal, 2009, vol. 119, pp. 158-171) is widely used to measure connectedness in economic and financial networks. Abrupt increases in the spillover index are typically thought to result from systemic events, but evidence of the statistical significance of this relationship is largely absent from the literature. We develop a new bootstrap-based technique to evaluate the probability that the value of the spillover index changes over an arbitrary time period following an exogenously defined event. We apply our framework to the original dataset studied by Diebold and Yilmaz and obtain qualified support for the notion that the spillover index increases in a timely and statistically significant manner in the wake of systemic shocks.
Keywords:
Spillover index; systemic events; bootstrap-after-bootstrap procedure
Fulltext is available at external website.
Does the Spillover Index Respond Significantly to Systemic Shocks? A Bootstrap-Based Probabilistic Analysis
The spillover index developed by Diebold and Yilmaz (Economic Journal, 2009, vol. 119, pp. 158-171) is widely used to measure connectedness in economic and financial networks. Abrupt increases in the ...
Distributed Sequential Zero-Inflated Poisson Regression
Žemlička, R.; Dedecius, Kamil
2021 - English
The zero-inflated Poisson regression model is a generalized linear model (GLM) for non-negative count variables with an excessive number of zeros. This letter proposes its low-cost distributed sequential inference from streaming data in networks with information diffusion. The model is viewed as a probabilistic mixture of a Poisson and a zero-located Dirac component, whose probabilities are estimated using a quasi-Bayesian procedure. The regression coefficients are inferred by means of a weighted Bayesian update. The network nodes share their posterior distributions using the diffusion protocol.\n
Keywords:
Poisson regression; zero inflation; GLM
Fulltext is available at external website.
Distributed Sequential Zero-Inflated Poisson Regression
The zero-inflated Poisson regression model is a generalized linear model (GLM) for non-negative count variables with an excessive number of zeros. This letter proposes its low-cost distributed ...
Media Treatment of Monetary Policy Surprises and Their Impact on Firms’ and Consumers’ Expectations
Pinter, J.; Kočenda, Evžen
2021 - English
We empirically investigate whether monetary policy announcements affect firms’ and consumers’ expectations by taking into account media treatments of monetary policy announcements. To identify exogenous changes in monetary policy stances, we use the standard financial monetary policy surprise measures in the euro area. We then analyze how a general newspaper and a financial newspaper (Le Monde and The Financial Times) report on announcements. We find that 87 % of monetary policy surprises are either not associated with the general newspaper reporting a change in the monetary policy stance to their readers or have a sign that is inconsistent with the media report of the announcement. When we use the raw monetary policy surprises variable as an independent variable in the link between monetary policy announcements and firms’/consumers’ expectations, we mostly do not find, in line with several previous studies, any statistically significant association. When we take only monetary policy surprises that are consistent with the general newspaper report, in almost all cases we find that monetary policy surprises on the immediate monetary policy stance do affect expectations. Surprises related to future policy inclination and information shocks usually do not appear to matter. The results appear to be in line with rational inattention theories and highlight the need for caution in the use of monetary policy surprise measures for macroeconomic investigations.
Keywords:
firm expectations; consumer expectations; monetary policy surprises; European Central Bank; information effect
Fulltext is available at external website.
Media Treatment of Monetary Policy Surprises and Their Impact on Firms’ and Consumers’ Expectations
We empirically investigate whether monetary policy announcements affect firms’ and consumers’ expectations by taking into account media treatments of monetary policy announcements. To identify ...
Central Moments and Risk-Sensitive Optimality in Markov Reward Processes
Sladký, Karel
2021 - English
In this note we consider discrete- and continuous-time Markov decision processes with finite state space. There is no doubt that usual optimality criteria examined in the literature on optimization of Markov reward processes, e.g. total discounted or mean reward, may be quite insufficient to select more sophisticated criteria that reflect also the variability-risk features of the problem. In this note we focus on models where the stream of rewards generated by the Markov process is evaluated by an exponential utility function with a given risk sensitivity coefficient (so-called risk-sensitive models).For the risk sensitive case, i.e. if the considered risk-sensitivity coefficient is non-zero, we establish explicit formulas for growth rate of expectation of the exponential utility function. Recall that in this case along with the total reward also it higher moments are taken into account. Using Taylor expansion of the utility function we present explicit formulas for calculating variance a higher central moments of the total reward generated by the |Markov reward process along with its asymptotic behaviour.
Keywords:
discrete- and continuous-time Markov reward chains; exponential utility; moment generating functions
Fulltext is available at external website.
Central Moments and Risk-Sensitive Optimality in Markov Reward Processes
In this note we consider discrete- and continuous-time Markov decision processes with finite state space. There is no doubt that usual optimality criteria examined in the literature on optimization of ...
Mathematics and Optimal control theory meet Pharmacy: Towards application of special techniques in modeling, control and optimization of biochemical networks
Papáček, Štěpán; Matonoha, Ctirad; Duintjer Tebbens, Jurjen
2021 - English
Similarly to other scienti c domains, the expenses related to in silico modeling in pharmacology need not be extensively apologized. Vis a vis both in vitro and in vivo experiments, physiologically-based pharmacokinetic (PBPK) and pharmacodynamic models represent an important tool for the assessment of drug safety before its approval, as well as a viable option in designing dosing regimens. In this contribution, some special techniques related to the mathematical modeling, control and optimization of biochemical networks are presented on a paradigmatic example of enzyme kinetics.
Keywords:
Dynamical system; Systems pharmacology; Biochemical network; Input-output regulation; Optimization
Fulltext is available at external website.
Mathematics and Optimal control theory meet Pharmacy: Towards application of special techniques in modeling, control and optimization of biochemical networks
Similarly to other scienti c domains, the expenses related to in silico modeling in pharmacology need not be extensively apologized. Vis a vis both in vitro and in vivo experiments, ...
Multimodal data fusion in remote sensing
Greško, Šimon; Zitová, Barbara
2021 - English
With fast development of remote sensing technologies, demand for remote sensing data started to be increasing. Providing space resolution of these data may not be sufficient for target application. For that reason the methods for fusing remote sensing images were developed. This issue appears in the question of demand for high resolution thermal data. Sentinel-3 provides free remote sensing thermal data with space resolution 1000x1000 m, which may be insufficient for some specialized applications. In this thesis, standard methods of image fusion are presented as well as specialized methods for thermal sharpening, their modifications, implementation and results including validation and comparison.
Keywords:
remote sensing; thermal sharpening; sentinel
Fulltext is available at external website.
Multimodal data fusion in remote sensing
With fast development of remote sensing technologies, demand for remote sensing data started to be increasing. Providing space resolution of these data may not be sufficient for target application. ...
Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election
Kalina, Jan; Vidnerová, P.
2021 - English
Regression quantiles can be characterized as popular tools for a complex modeling of a continuous response variable conditioning on one or more given independent variables. Because they are however vulnerable to leverage points in the regression model, an alternative approach denoted as implicitly weighted regression quantiles have been proposed. The aim of current work is to apply them to the results of the second round of the 2018 presidential election in the Czech Republic. The election results are modeled as a response of 4 demographic or economic predictors over the 77 Czech counties. The analysis represents the first application of the implicitly weighted regression quantiles to data with more than one regressor. The results reveal the implicitly weighted regression quantiles to be indeed more robust with respect to leverage points compared to standard regression quantiles. If however the model does not contain leverage points, both versions of the regression quantiles yield very similar results. Thus, the election dataset serves here as an illustration of the usefulness of the implicitly weighted regression quantiles.
Keywords:
linear regression; quantile regression; robustness, outliers; elections results
Fulltext is available at external website.
Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election
Regression quantiles can be characterized as popular tools for a complex modeling of a continuous response variable conditioning on one or more given independent variables. Because they are however ...
Transferring Improved Local Kernel Design in Multi-Source Bayesian Transfer Learning, with an application in Air Pollution Monitoring in India
Nugent, Sh.; Quinn, Anthony
2021 - English
Existing frameworks for multi-task learning [1],[2] often rely on completely modelled relationships between tasks, which may not be available. Recent work [3], [4] has been undertaken on approaches to fully probabilistic methods for transfer learning between two Gaussian Process (GP) tasks. There, the target algorithm accepts source knowledge in the form of a probabilistic prior from a source algorithm, without requiring the target to model their interaction with the source. These strategies have offered robust improvements on current state of the art algorithms, such as the Intrinsic Coregionalization Model. The Bayesian Transfer Learning algorithm proposed in [4], was found to provide robust, positive\ntransfer. This algorithm was then extended to accommodate knowledge transfer from multiple source modellers [5]. Improved predictive performance was observed from increases in the number of sources. This report reviews the multi-source transfer findings in [5] and applies it to a real world problem of pollution modelling in India, using public-domain data.
Keywords:
fully probabilistic methods; Bayesian Transfer Learning algorithm; Gaussian Process; Intrinsic Coregionalization Model; pollution modelling
Fulltext is available at external website.
Transferring Improved Local Kernel Design in Multi-Source Bayesian Transfer Learning, with an application in Air Pollution Monitoring in India
Existing frameworks for multi-task learning [1],[2] often rely on completely modelled relationships between tasks, which may not be available. Recent work [3], [4] has been undertaken on approaches to ...
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