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Econometrics
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Topics in Computational Econometrics and Finance: Theory and Applications
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Topics in Computational Econometrics and Finance: Theory and Applications

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 34446

Special Issue Editor

Prof. Dr. Fredj Jawadi

E-Mail Website
Guest Editor
Institut d'Administration des Entreprises de Lille (IAE), University of Lille Nord de France, Lille, France
Interests: applied econometrics; empirical finance; empirical macroeconomics

Special Issue Information

Dear Colleagues,

Since the aftermath of the global financial crisis (2008–2009), various macroeconomic and financial models have been questioned, in particular because of the inability of these models to forecast the financial crisis. This has yielded an ongoing challenge to improve these models in order to better reproduce the properties of macro and financial data and to improve their modeling and forecasting. Accordingly, these models have been the subject of different empirical investigations. To this end, some econometric models have been extended. These extensions have implied the development/extension of parametric tests (time series modeling, high frequency modeling, panel data, multivariate analysis, etc.) as well as nonparametric tests (wavelet, spectral analysis, etc.). The application of new tests has improved the modeling of macroeconomic and financial data in different ways, and the related simulations of these methods have shown their viability. Consequently, a growing research agenda has been defined to tackle these questions with these new econometric methods.

This Special Issue aims to focus on the recent topics on empirical macroeconomic and financial modeling as well as on these extended econometric techniques that were introduced to extend the original framework of macro-econometrics. Accordingly, this Special Issue will present theoretical, methodological, as well as empirical, research in empirical macroeconomics and finance, with a focus on the econometrics modeling steps.

This Special Issue is associated with the 6th International Symposium in Computational Economics and Finance (ISCEF2020, www.iscef.com). The conference will be held from 29 to 31 October 2020, in Paris.

Prof. Dr. Fredj Jawadi
Guest Editor

Manuscript Submission Information

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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. Econometrics 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 1400 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

  • empirical finance
  • empirical macroeconomics
  • computational finance
  • time series
  • nonlinearity
  • nonparametric econometrics
  • forecasting

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Published Papers (8 papers)

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Research

26 pages, 1914 KiB  
Article
Parameter Estimation of the Heston Volatility Model with Jumps in the Asset Prices
by Jarosław Gruszka  and Janusz Szwabiński
Econometrics 2023, 11(2), 15; https://doi.org/10.3390/econometrics11020015 - 2 Jun 2023
Cited by 1 | Viewed by 3916
Abstract
The parametric estimation of stochastic differential equations (SDEs) has been the subject of intense studies already for several decades. The Heston model, for instance, is based on two coupled SDEs and is often used in financial mathematics for the dynamics of asset prices [...] Read more.
The parametric estimation of stochastic differential equations (SDEs) has been the subject of intense studies already for several decades. The Heston model, for instance, is based on two coupled SDEs and is often used in financial mathematics for the dynamics of asset prices and their volatility. Calibrating it to real data would be very useful in many practical scenarios. It is very challenging, however, since the volatility is not directly observable. In this paper, a complete estimation procedure of the Heston model without and with jumps in the asset prices is presented. Bayesian regression combined with the particle filtering method is used as the estimation framework. Within the framework, we propose a novel approach to handle jumps in order to neutralise their negative impact on the estimates of the key parameters of the model. An improvement in the sampling in the particle filtering method is discussed as well. Our analysis is supported by numerical simulations of the Heston model to investigate the performance of the estimators. In addition, a practical follow-along recipe is given to allow finding adequate estimates from any given data. Full article
(This article belongs to the Special Issue Topics in Computational Econometrics and Finance: Theory and Applications)
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27 pages, 1943 KiB  
Article
Local Gaussian Cross-Spectrum Analysis
by Lars Arne Jordanger and Dag Tjøstheim
Econometrics 2023, 11(2), 12; https://doi.org/10.3390/econometrics11020012 - 21 Apr 2023
Cited by 1 | Viewed by 2325
Abstract
The ordinary spectrum is restricted in its applications, since it is based on the second-order moments (auto- and cross-covariances). Alternative approaches to spectrum analysis have been investigated based on other measures of dependence. One such approach was developed for univariate time series by [...] Read more.
The ordinary spectrum is restricted in its applications, since it is based on the second-order moments (auto- and cross-covariances). Alternative approaches to spectrum analysis have been investigated based on other measures of dependence. One such approach was developed for univariate time series by the authors of this paper using the local Gaussian auto-spectrum based on the local Gaussian auto-correlations. This makes it possible to detect local structures in univariate time series that look similar to white noise when investigated by the ordinary auto-spectrum. In this paper, the local Gaussian approach is extended to a local Gaussian cross-spectrum for multivariate time series. The local Gaussian cross-spectrum has the desirable property that it coincides with the ordinary cross-spectrum for Gaussian time series, which implies that it can be used to detect non-Gaussian traits in the time series under investigation. In particular, if the ordinary spectrum is flat, then peaks and troughs of the local Gaussian spectrum can indicate nonlinear traits, which potentially might reveal local periodic phenomena that are undetected in an ordinary spectral analysis. Full article
(This article belongs to the Special Issue Topics in Computational Econometrics and Finance: Theory and Applications)
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18 pages, 772 KiB  
Article
Nonfractional Long-Range Dependence: Long Memory, Antipersistence, and Aggregation
by J. Eduardo Vera-Valdés
Econometrics 2021, 9(4), 39; https://doi.org/10.3390/econometrics9040039 - 19 Oct 2021
Cited by 5 | Viewed by 3079
Abstract
This paper used cross-sectional aggregation as the inspiration for a model with long-range dependence that arises in actual data. One of the advantages of our model is that it is less brittle than fractionally integrated processes. In particular, we showed that the antipersistent [...] Read more.
This paper used cross-sectional aggregation as the inspiration for a model with long-range dependence that arises in actual data. One of the advantages of our model is that it is less brittle than fractionally integrated processes. In particular, we showed that the antipersistent phenomenon is not present for the cross-sectionally aggregated process. We proved that this has implications for estimators of long-range dependence in the frequency domain, which will be misspecified for nonfractional long-range-dependent processes with negative degrees of persistence. As an application, we showed how we can approximate a fractionally differenced process using theoretically-motivated cross-sectional aggregated long-range-dependent processes. An example with temperature data showed that our framework provides a better fit to the data than the fractional difference operator. Full article
(This article belongs to the Special Issue Topics in Computational Econometrics and Finance: Theory and Applications)
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15 pages, 546 KiB  
Article
Inference Using Simulated Neural Moments
by Michael Creel
Econometrics 2021, 9(4), 35; https://doi.org/10.3390/econometrics9040035 - 24 Sep 2021
Viewed by 3223
Abstract
This paper studies method of simulated moments (MSM) estimators that are implemented using Bayesian methods, specifically Markov chain Monte Carlo (MCMC). Motivation and theory for the methods is provided by Chernozhukov and Hong (2003). The paper shows, experimentally, that confidence intervals using these [...] Read more.
This paper studies method of simulated moments (MSM) estimators that are implemented using Bayesian methods, specifically Markov chain Monte Carlo (MCMC). Motivation and theory for the methods is provided by Chernozhukov and Hong (2003). The paper shows, experimentally, that confidence intervals using these methods may have coverage which is far from the nominal level, a result which has parallels in the literature that studies overidentified GMM estimators. A neural network may be used to reduce the dimension of an initial set of moments to the minimum number that maintains identification, as in Creel (2017). When MSM-MCMC estimation and inference is based on such moments, and using a continuously updating criteria function, confidence intervals have statistically correct coverage in all cases studied. The methods are illustrated by application to several test models, including a small DSGE model, and to a jump-diffusion model for returns of the S&P 500 index. Full article
(This article belongs to the Special Issue Topics in Computational Econometrics and Finance: Theory and Applications)
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17 pages, 1424 KiB  
Article
Multivariate Analysis of Cryptocurrencies
by Vincenzo Candila
Econometrics 2021, 9(3), 28; https://doi.org/10.3390/econometrics9030028 - 1 Jul 2021
Cited by 8 | Viewed by 7622
Abstract
Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are [...] Read more.
Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are more than 8525 cryptocurrencies with a market value of approximately USD 1676 billion. These particular assets can be used to diversify the portfolio as well as for speculative actions. For this reason, investigating the daily volatility and co-volatility of cryptocurrencies is crucial for investors and portfolio managers. In this work, the interdependencies among a panel of the most traded digital currencies are explored and evaluated from statistical and economic points of view. Taking advantage of the monthly Google queries (which appear to be the factors driving the price dynamics) on cryptocurrencies, we adopted a mixed-frequency approach within the Dynamic Conditional Correlation (DCC) model. In particular, we introduced the Double Asymmetric GARCH–MIDAS model in the DCC framework. Full article
(This article belongs to the Special Issue Topics in Computational Econometrics and Finance: Theory and Applications)
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20 pages, 589 KiB  
Article
Semiparametric Estimation of a Corporate Bond Rating Model
by Yixiao Jiang
Econometrics 2021, 9(2), 23; https://doi.org/10.3390/econometrics9020023 - 28 May 2021
Cited by 3 | Viewed by 4075
Abstract
This paper investigates the incentive of credit rating agencies (CRAs) to bias ratings using a semiparametric, ordered-response model. The proposed model explicitly takes conflicts of interest into account and allows the ratings to depend flexibly on risk attributes through a semiparametric index structure. [...] Read more.
This paper investigates the incentive of credit rating agencies (CRAs) to bias ratings using a semiparametric, ordered-response model. The proposed model explicitly takes conflicts of interest into account and allows the ratings to depend flexibly on risk attributes through a semiparametric index structure. Asymptotic normality for the estimator is derived after using several bias correction techniques. Using Moody’s rating data from 2001 to 2016, I found that firms related to Moody’s shareholders were more likely to receive better ratings. Such favorable treatments were more pronounced in investment grade bonds compared with high yield bonds, with the 2007–2009 financial crisis being an exception. Parametric models, such as the ordered-probit, failed to identify this heterogeneity of the rating bias across different bond categories. Full article
(This article belongs to the Special Issue Topics in Computational Econometrics and Finance: Theory and Applications)
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35 pages, 12864 KiB  
Article
Structural Panel Bayesian VAR with Multivariate Time-Varying Volatility to Jointly Deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues
by Antonio Pacifico
Econometrics 2021, 9(2), 20; https://doi.org/10.3390/econometrics9020020 - 2 May 2021
Cited by 5 | Viewed by 4088
Abstract
This paper improves a standard Structural Panel Bayesian Vector Autoregression model in order to jointly deal with issues of endogeneity, because of omitted factors and unobserved heterogeneity, and volatility, because of policy regime shifts and structural changes. Bayesian methods are used to select [...] Read more.
This paper improves a standard Structural Panel Bayesian Vector Autoregression model in order to jointly deal with issues of endogeneity, because of omitted factors and unobserved heterogeneity, and volatility, because of policy regime shifts and structural changes. Bayesian methods are used to select the best model solution for examining if international spillovers come from multivariate volatility, time variation, or contemporaneous relationship. An empirical application among Central-Eastern and Western Europe economies is conducted to describe the performance of the methodology, with particular emphasis on the Great Recession and post-crisis periods. A simulated example is also addressed to highlight the performance of the estimating procedure. Findings from evidence-based forecasting are also addressed to evaluate the impact of an ongoing pandemic crisis on the global economy. Full article
(This article belongs to the Special Issue Topics in Computational Econometrics and Finance: Theory and Applications)
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23 pages, 442 KiB  
Article
Enhanced Methods of Seasonal Adjustment
by D. Stephen G. Pollock
Econometrics 2021, 9(1), 3; https://doi.org/10.3390/econometrics9010003 - 5 Jan 2021
Cited by 6 | Viewed by 3554
Abstract
The effect of the conventional model-based methods of seasonal adjustment is to nullify the elements of the data that reside at the seasonal frequencies and to attenuate the elements at the adjacent frequencies. It may be desirable to nullify some of the adjacent [...] Read more.
The effect of the conventional model-based methods of seasonal adjustment is to nullify the elements of the data that reside at the seasonal frequencies and to attenuate the elements at the adjacent frequencies. It may be desirable to nullify some of the adjacent elements instead of merely attenuating them. For this purpose, two alternative sets of procedures are presented that have been implemented in a computer program named SEASCAPE. In the first set of procedures, a basic seasonal adjustment filter is augmented by additional filters that are targeted at the adjacent frequencies. In the second set of procedures, a Fourier transform of the data is exploited to allow the elements in the vicinities of the seasonal frequencies to be eliminated or attenuated at will. The question is raised of whether an estimated trend-cycle trajectory that is devoid of high-frequency noise can serve in place of the seasonally adjusted data. Full article
(This article belongs to the Special Issue Topics in Computational Econometrics and Finance: Theory and Applications)
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