Special Issue "Bayesian Econometrics"

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematics and Finance".

Deadline for manuscript submissions: closed (30 April 2019).

Special Issue Editors

Dr. Mauro Bernardi
Website
Guest Editor
Department of Statistical Sciences, University of Padova, Italy
Interests: Bayesian statistics; time series analysis; financial econometrics
Dr. Stefano Grassi
Website
Guest Editor
Dipartimento di Economia e Finanza, University of Rome 'Tor Vergata', Rome, Italy
Interests: Bayesian econometrics; financial econometrics and macroeconometrics
Prof. Dr. Francesco Ravazzolo
Website
Guest Editor
Associate Professor at Faculty of Economics and Management at Free University of Bozen/Bolzano, Italy
Interests: Bayesian econometrics; financial econometrics and macroeconometrics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed to a large and growing number of applications. One of the main advantages of Bayesian inference is to deal with different and many sources of uncertainty, including data, model, parameter, parameter restriction uncertainties, in a unified and coherent framework. This Special Issue focuses on exercises where one or more of these features are crucial. Applications include risk measurement in international and financial markets, forecasting, assessment of policy effectiveness in macro and monetary economics. Papers that contain original research on this theme are actively solicited.

Dr. Mauro Bernardi
Dr. Stefano Grassi
Prof. Dr. Francesco Ravazzolo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bayesian econometrics
  • Risk measurement
  • Forecasting
  • MCMC methods
  • Parallel computing

Published Papers (7 papers)

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Editorial

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Open AccessEditorial
Bayesian Econometrics
J. Risk Financial Manag. 2020, 13(11), 257; https://doi.org/10.3390/jrfm13110257 - 29 Oct 2020
Abstract
The computational revolution in simulation techniques has shown to become a key ingredient in the field of Bayesian econometrics and opened new possibilities to study complex economic and financial phenomena. Applications include risk measurement, forecasting, assessment of policy effectiveness in macro, finance, marketing [...] Read more.
The computational revolution in simulation techniques has shown to become a key ingredient in the field of Bayesian econometrics and opened new possibilities to study complex economic and financial phenomena. Applications include risk measurement, forecasting, assessment of policy effectiveness in macro, finance, marketing and monetary economics. Full article
(This article belongs to the Special Issue Bayesian Econometrics)

Research

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Open AccessArticle
How to Explain When the ES Is Lower Than One? A Bayesian Nonlinear Mixed-Effects Approach
J. Risk Financial Manag. 2020, 13(2), 21; https://doi.org/10.3390/jrfm13020021 - 01 Feb 2020
Cited by 3
Abstract
Most studies in Vietnam use the Cobb-Douglas production function and its modifications for economic analysis. Extremely rigid presumptions are a main weak point of this functional form, particularly if the elasticity of factor substitution (ES) is equal to one, which hides the role [...] Read more.
Most studies in Vietnam use the Cobb-Douglas production function and its modifications for economic analysis. Extremely rigid presumptions are a main weak point of this functional form, particularly if the elasticity of factor substitution (ES) is equal to one, which hides the role of the ES for economic growth. The CES (constant elasticity of substitution) production function with more flexible presumptions, concretely its ES, is not unitary, and has been used more and more widely in economic investigations. So, this study is conducted to estimate the average ES through the specification of an aggregate CES function for the Vietnamese nonfinancial enterprises. By performing Bayesian nonlinear mixed-effects regression via Random-walk Metropolis Hastings (MH) algorithm, based on the data set of the listed nonfinancial enterprises of Vietnam, the author found that the CES function estimated for the researched enterprises has an ES lower than one, i.e., capital and labor are complimentary. This finding shows that Vietnamese nonfinancial enterprises can confront a downward trend of output growth. Full article
(This article belongs to the Special Issue Bayesian Econometrics)
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Open AccessArticle
Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models
J. Risk Financial Manag. 2019, 12(3), 150; https://doi.org/10.3390/jrfm12030150 - 18 Sep 2019
Cited by 5
Abstract
This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalised cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. Different Bayesian models are compared, including models with constant and time-varying volatility, such as stochastic volatility and GARCH. Moreover, [...] Read more.
This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalised cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. Different Bayesian models are compared, including models with constant and time-varying volatility, such as stochastic volatility and GARCH. Moreover, some cryptopredictors are included in the analysis, such as S&P 500 and Nikkei 225. In this paper, the results show that stochastic volatility is significantly outperforming the benchmark of VAR in both point and density forecasting. Using a different type of distribution, for the errors of the stochastic volatility, the student-t distribution is shown to outperform the standard normal approach. Full article
(This article belongs to the Special Issue Bayesian Econometrics)
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Open AccessArticle
Disentangling Civilian and Military Spending Shocks: A Bayesian DSGE Approach for the US Economy
J. Risk Financial Manag. 2019, 12(3), 141; https://doi.org/10.3390/jrfm12030141 - 01 Sep 2019
Cited by 3
Abstract
In this paper, we disentangle public spending components in order analyse their effects on the U.S. economy. Our Dynamic Stochastic General Equilibrium Model (DSGE) model includes both civilian and military expenditures. We take into account the changes in the effects of these public [...] Read more.
In this paper, we disentangle public spending components in order analyse their effects on the U.S. economy. Our Dynamic Stochastic General Equilibrium Model (DSGE) model includes both civilian and military expenditures. We take into account the changes in the effects of these public spending components before and after the structural break that occurred in the U.S. economy around 1980, namely financial liberalisation. Therefore, we estimate our model with Bayesian methods for two sample periods: 1954:3–1979:2 and 1983:1–2008:2. Our results suggest that total government spending has a positive effect on output, but it induces a fall in private consumption. Moreover, we find important differences between the effects of civilian and military spending. In the pre-1980 period, higher civilian spending induced a rise in private consumption, whereas military spending shocks systematically decreased it. Our findings indicate that civilian spending has a more positive impact on output than military expenditure. Our robustness analysis assesses the impact of public spending shocks under alternative monetary policy assumptions. Full article
(This article belongs to the Special Issue Bayesian Econometrics)
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Open AccessArticle
Is Bitcoin a Relevant Predictor of Standard & Poor’s 500?
J. Risk Financial Manag. 2019, 12(2), 93; https://doi.org/10.3390/jrfm12020093 - 31 May 2019
Cited by 3
Abstract
The paper investigates whether Bitcoin is a good predictor of the Standard & Poor’s 500 Index. To answer this question we compare alternative models using a point and density forecast relying on Dynamic Model Averaging (DMA) and Dynamic Model Selection (DMS). According to [...] Read more.
The paper investigates whether Bitcoin is a good predictor of the Standard & Poor’s 500 Index. To answer this question we compare alternative models using a point and density forecast relying on Dynamic Model Averaging (DMA) and Dynamic Model Selection (DMS). According to our results, Bitcoin does not show any direct impact on the predictability of Standard & Poor’s 500 for the considered sample. Full article
(This article belongs to the Special Issue Bayesian Econometrics)
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Open AccessArticle
Optimism in Financial Markets: Stock Market Returns and Investor Sentiments
J. Risk Financial Manag. 2019, 12(2), 85; https://doi.org/10.3390/jrfm12020085 - 13 May 2019
Cited by 2
Abstract
This paper investigates how investor sentiment affects stock market returns and evaluates the predictability power of sentiment indices on U.S. and EU stock market returns. As regards the American example, evidence shows that investor sentiment indices have an economic and statistical predictability power [...] Read more.
This paper investigates how investor sentiment affects stock market returns and evaluates the predictability power of sentiment indices on U.S. and EU stock market returns. As regards the American example, evidence shows that investor sentiment indices have an economic and statistical predictability power on stock market returns. Concerning the European market instead, investigation provides weak results. Moreover, comparing the two markets, where investor sentiment of U.S. market tries to predict the European stock market returns, and vice versa, the analyses indicate a spillover effect from the U.S. to Europe. Full article
(This article belongs to the Special Issue Bayesian Econometrics)
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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 - 27 Oct 2018
Cited by 7
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 [...] Read more.
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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