Special Issue "Bayesian Econometrics"

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

Deadline for manuscript submissions: closed (30 April 2019)

Special Issue Editors

Guest Editor
Dr. Mauro Bernardi

Department of Statistical Sciences, University of Padova, Italy
Website | E-Mail
Interests: Bayesian statistics; time series analysis; financial econometrics
Guest Editor
Dr. Stefano Grassi

Dipartimento di Economia e Finanza, University of Rome 'Tor Vergata', Rome, Italy
Website | E-Mail
Interests: Bayesian econometrics; financial econometrics and macroeconometrics
Guest Editor
Prof. Dr. Francesco Ravazzolo

Associate Professor at Faculty of Economics and Management at Free University of Bozen/Bolzano, Italy
Website | E-Mail
Interests: Bayesian econometrics; financial econometrics and macroeconometrics

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 quarterly 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 350 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 (3 papers)

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Research

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
Received: 14 May 2019 / Revised: 14 May 2019 / Accepted: 17 May 2019 / Published: 31 May 2019
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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
Received: 8 April 2019 / Revised: 30 April 2019 / Accepted: 7 May 2019 / Published: 13 May 2019
PDF Full-text (921 KB) | HTML Full-text | XML Full-text
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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Figure 1

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
Cited by 3 | PDF Full-text (589 KB) | HTML Full-text | XML Full-text
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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Graphical abstract

J. Risk Financial Manag. EISSN 1911-8074 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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