Special Issue "Financial Econometrics"

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

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

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Prof. Dr. Yiu-Kuen Tse
E-Mail Website
Guest Editor
School of Economics, Singapore Management University, Singapore 188065, Singapore
Interests: financial econometrics; applied econometrics; actuarial science

Special Issue Information

Dear Colleagues,

This Special Issue is concerned with the econometric analysis of financial data, both methodological and applied. The prevalence of large financial data sets and real time updates have opened up new developments in the area of financial econometrics. This issue welcomes studies on aspects of volatility modelling, large dimension financial data, high-frequency trading and data analysis, forecasting of asset returns, financial bubbles and contagion, financial engineering, nonlinear financial time series analysis, long memory time series, and analysis of tail risks and extreme events. Papers for this issue may focus on the econometric study of new financial markets, new financial products and frontier development of econometric methods for financial data.

Prof. Dr. Yiu-Kuen Tse
Guest Editor

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 1000 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

  • Volatility modelling
  • High-frequency financial data
  • Financial time series
  • Forecasting asset returns
  • Tail risks and jump risks
  • Financial analytics and trading strategies
  • Financial bubbles and contagion

Published Papers (7 papers)

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Editorial

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Open AccessEditorial
Editorial for the Special Issue on Financial Econometrics
J. Risk Financial Manag. 2019, 12(3), 153; https://doi.org/10.3390/jrfm12030153 - 19 Sep 2019
Abstract
Financial econometrics has developed into a very fruitful and vibrant research area in the last two decades [...] Full article
(This article belongs to the Special Issue Financial Econometrics) Printed Edition available

Research

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Open AccessArticle
Forecasting Realized Volatility Using a Nonnegative Semiparametric Model
J. Risk Financial Manag. 2019, 12(3), 139; https://doi.org/10.3390/jrfm12030139 - 29 Aug 2019
Cited by 1
Abstract
This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new model extends a related linear nonnegative autoregressive model previously used in the volatility literature by way of a power transformation. It is semiparametric in the sense that [...] Read more.
This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new model extends a related linear nonnegative autoregressive model previously used in the volatility literature by way of a power transformation. It is semiparametric in the sense that the distributional and functional form of its error component is partially unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Simulation studies validate the new method and suggest that it works reasonably well in finite samples. The out-of-sample forecasting performance of the proposed model is evaluated against a number of standard models, using data on S&P 500 monthly realized volatilities. Some commonly used loss functions are employed to evaluate the predictive accuracy of the alternative models. It is found that the new model generally generates highly competitive forecasts. Full article
(This article belongs to the Special Issue Financial Econometrics) Printed Edition available
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Open AccessArticle
Some Dynamic and Steady-State Properties of Threshold Auto-Regressions with Applications to Stationarity and Local Explosivity
J. Risk Financial Manag. 2019, 12(3), 123; https://doi.org/10.3390/jrfm12030123 - 22 Jul 2019
Cited by 1
Abstract
The purpose of this paper is to investigate the dynamics and steady-state properties of threshold autoregressive models with exogenous states that follow Markovian processes. Markovian processes are widely used in applied economics although their statistical properties have not been explored in detail. We [...] Read more.
The purpose of this paper is to investigate the dynamics and steady-state properties of threshold autoregressive models with exogenous states that follow Markovian processes. Markovian processes are widely used in applied economics although their statistical properties have not been explored in detail. We use characteristic functions to carry out the analysis, and this allows us to describe limiting distributions for processes not considered in the literature previously. We also calculate analytical expressions for some moments. Furthermore, we see that we can have locally explosive processes that are explosive in one regime whilst being strongly stationary overall. This is explored through simulation analysis, where we also show how the distribution changes when the explosive state becomes more frequent although the overall process remains stationary. In doing so, we are able to relate our analysis to asset prices which exhibit similar distributional properties. Full article
(This article belongs to the Special Issue Financial Econometrics) Printed Edition available
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Open AccessArticle
On Tuning Parameter Selection in Model Selection and Model Averaging: A Monte Carlo Study
J. Risk Financial Manag. 2019, 12(3), 109; https://doi.org/10.3390/jrfm12030109 - 26 Jun 2019
Cited by 1
Abstract
Model selection and model averaging are popular approaches for handling modeling uncertainties. The existing literature offers a unified framework for variable selection via penalized likelihood and the tuning parameter selection is vital for consistent selection and optimal estimation. Few studies have explored the [...] Read more.
Model selection and model averaging are popular approaches for handling modeling uncertainties. The existing literature offers a unified framework for variable selection via penalized likelihood and the tuning parameter selection is vital for consistent selection and optimal estimation. Few studies have explored the finite sample performances of the class of ordinary least squares (OLS) post-selection estimators with the tuning parameter determined by different selection approaches. We aim to supplement the literature by studying the class of OLS post-selection estimators. Inspired by the shrinkage averaging estimator (SAE) and the Mallows model averaging (MMA) estimator, we further propose a shrinkage MMA (SMMA) estimator for averaging high-dimensional sparse models. Our Monte Carlo design features an expanding sparse parameter space and further considers the effect of the effective sample size and the degree of model sparsity on the finite sample performances of estimators. We find that the OLS post-smoothly clipped absolute deviation (SCAD) estimator with the tuning parameter selected by the Bayesian information criterion (BIC) in finite sample outperforms most penalized estimators and that the SMMA performs better when averaging high-dimensional sparse models. Full article
(This article belongs to the Special Issue Financial Econometrics) Printed Edition available
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Open AccessArticle
Threshold Stochastic Conditional Duration Model for Financial Transaction Data
J. Risk Financial Manag. 2019, 12(2), 88; https://doi.org/10.3390/jrfm12020088 - 14 May 2019
Cited by 1
Abstract
This paper proposes a variant of a threshold stochastic conditional duration (TSCD) model for financial data at the transaction level. It assumes that the innovations of the duration process follow a threshold distribution with a positive support. In addition, it also assumes that [...] Read more.
This paper proposes a variant of a threshold stochastic conditional duration (TSCD) model for financial data at the transaction level. It assumes that the innovations of the duration process follow a threshold distribution with a positive support. In addition, it also assumes that the latent first-order autoregressive process of the log conditional durations switches between two regimes. The regimes are determined by the levels of the observed durations and the TSCD model is specified to be self-excited. A novel Markov-Chain Monte Carlo method (MCMC) is developed for parameter estimation of the model. For model discrimination, we employ deviance information criteria, which does not depend on the number of model parameters directly. Duration forecasting is constructed by using an auxiliary particle filter based on the fitted models. Simulation studies demonstrate that the proposed TSCD model and MCMC method work well in terms of parameter estimation and duration forecasting. Lastly, the proposed model and method are applied to two classic data sets that have been studied in the literature, namely IBM and Boeing transaction data. Full article
(This article belongs to the Special Issue Financial Econometrics) Printed Edition available
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Open AccessArticle
Bond Risk Premia and Restrictions on Risk Prices
J. Risk Financial Manag. 2018, 11(4), 60; https://doi.org/10.3390/jrfm11040060 - 03 Oct 2018
Cited by 1
Abstract
Researchers who estimate affine term structure models often impose overidentifying restrictions (restrictions on parameters beyond those necessary for identification) for a variety of reasons. While some of those restrictions seem to have minor effects on the extracted factors and some measures of risk [...] Read more.
Researchers who estimate affine term structure models often impose overidentifying restrictions (restrictions on parameters beyond those necessary for identification) for a variety of reasons. While some of those restrictions seem to have minor effects on the extracted factors and some measures of risk premia, such as the forward risk premium, they may have a large impact on other measures of risk premia that is often ignored. In this paper, we analyze how apparently innocuous overidentifying restrictions imposed on affine term structure models can lead to large differences in several measures of risk premiums. Full article
(This article belongs to the Special Issue Financial Econometrics) Printed Edition available
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Open AccessArticle
Stationary Threshold Vector Autoregressive Models
J. Risk Financial Manag. 2018, 11(3), 45; https://doi.org/10.3390/jrfm11030045 - 05 Aug 2018
Cited by 2
Abstract
This paper examines the steady state properties of the Threshold Vector Autoregressive model. Assuming that the trigger variable is exogenous and the regime process follows a Bernoulli distribution, necessary and sufficient conditions for the existence of stationary distribution are derived. A situation related [...] Read more.
This paper examines the steady state properties of the Threshold Vector Autoregressive model. Assuming that the trigger variable is exogenous and the regime process follows a Bernoulli distribution, necessary and sufficient conditions for the existence of stationary distribution are derived. A situation related to so-called “locally explosive models”, where the stationary distribution exists though the model is explosive in one regime, is analysed. Simulations show that locally explosive models can generate some of the key properties of financial and economic data. They also show that assessing the stationarity of threshold models based on simulations might well lead to wrong conclusions. Full article
(This article belongs to the Special Issue Financial Econometrics) Printed Edition available
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