journal: Studies in Nonlinear Dynamics & Econometrics

Impact Factor: 0.7

Published since April 1, 1996

Studies in Nonlinear Dynamics & Econometrics

ISSN: 1558-3708

Edited by: Jeremy Piger

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About this journal

Objective
A peer-reviewed journal since 1996, Studies in Nonlinear Dynamics & Econometrics (SNDE) is at the forefront of statistical and theoretical approaches to economics. The journal studies ways in which econometrics and dynamical systems theory increase our understanding of economic and financial markets. The journal disseminates authors' algorithms, programs, and data sets, allowing other scholars to replicate empirical results. Authors include econometricians such as Clive Granger, James Hamilton, and Halbert White, and theorists Jess Benhabib, Alan Kirman, and Kazuo Nishimura.

Best Paper Award
Since 2015, the Society has awarded $500 each year to the best paper published in the Society’s journal: Studies in Nonlinear Dynamics and Econometrics.

The Best Paper in 2023 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Thomas Lux for his paper: "Approximate Bayesian Inference for Agent-Based Models in Economics: A Case Study." Studies in Nonlinear Dynamics & Econometrics, vol. 27, no. 4, 2023, pp. 423-447.

The Best Paper in 2022 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Szabolcs Blazsek, Alvaro Escribano and Adrian Licht. for their paper: "Multivariate Markov-switching score-driven models: an application to the global crude oil market" Studies in Nonlinear Dynamics & Econometrics, vol. 26, no. 3, 2022, pp. 313-335.

The Best Paper in 2021 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Eric Mark Aldrich and Howard Kung for their paper: “Computational Methods for Production-Based Asset Pricing Models with Recursive Utility”, published in Volume 25, Issue 1.

The Best Paper in 2020 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Gregor Bäurle, Daniel Kaufmann, Sylvia Kaufmann and Rodney Strachan for their paper: “Constrained interest rates and changing dynamics at the zero lower bound”, published in Volume 24, Issue 2.

The Best Paper in 2019 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Chang-Jin Kim and Yunmi Kim for their paper: “A unified framework jointly explaining business conditions, stock returns, volatility and “volatility feedback news” effects”, published in Volume 23, Issue 2.

The Best Paper in 2018 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Luiggi Donayre, Yunjong Eo and James Morley for their paper: “Improving likelihood-ratio-based confidence intervals for threshold parameters in finite samples”,published in Volume 22, Issue 1 (Feb 2018)

The Best Paper in 2017 for Studies in Nonlinear Dynamics and Econometrics has been awarded to Julien Chevallier and Stephane Goutte for their paper: “On the estimation of regime-switching Levy models”, published in Volume 21, Issue 1 (Feb 2017).

The Best Paper in 2016 for Studies in Nonlinear Dynamics and Econometrics has been awarded to Mark J. Jensen for his paper: “Robust estimation of nonstationary, fractionally integrated, autoregressive, stochastic volatility?”, published in Volume 20, Issue 4 (Sept. 2016).

The Best Paper in 2015 for Studies in Nonlinear Dynamics and Econometrics has been awarded to Markus Jochmann and Gary Koop for their paper: “Regime-switching cointegration?”, published in Volume 19, Issue 1 (Feb 2015).

Topics

Probability
Statistics
Macroeconomics
Finance
Forecasting
Econometrics

Article formats
Research articles

Information on submission process

Your Benefits

Your benefits:

Theoretical and applied studies that characterize and motivate nonlinear phenomena
State-of the-art research results that increase understanding of economic and financial markets
Novel tools that allow replication of empirical results
High quality peer-review
International and renowned editorial board

History

Content available since 1996 (Volume 1, Issue 1)

Abstracting and Indexing

Studies in Nonlinear Dynamics & Econometrics is covered by the following services:

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Supplementary Materials

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Online Submission of Manuscripts

Ahead of Print / Just Accepted

Ahead of Print / Just Accepted

Volume 28 (2024)

Volume 27 (2023)

Volume 26 (2022)

Volume 25 (2021)

Volume 24 (2020)

Volume 23 (2019)

Volume 22 (2018)

Volume 21 (2017)

Volume 20 (2016)

Volume 19 (2015)

Volume 18 (2014)

Volume 17 (2013)

Volume 16 (2012)

Volume 15 (2011)

Volume 14 (2010)

Volume 13 (2009)

Volume 12 (2008)

Volume 11 (2007)

Volume 10 (2006)

Volume 9 (2005)

Volume 8 (2004)

Volume 7 (2003)

Volume 6 (2003)

Volume 5 (2002)

Volume 4 (2001)

Volume 3 (1999)

Volume 2 (1998)

Volume 1 (1997)

Studies in Nonlinear Dynamics & Econometrics publishes special and thematic issues focussed on important and emerging topics in the field of study. The journal has established a rigorous process to ensure that any special issue manuscripts follow the same high-quality standards and peer review processes as regular manuscripts. For further information on the journal’s peer review policy please see the "Instructions for Authors".

Recent Special Issues

Special Issue in Honor of Herman van Dijk; Guest Editors: Gary Koop and Francesco Ravazzolo [Issue 2, Volume 28 (2024)]

Journal Impact Factor	0.7	2023, Journal Citation Reports (Clarivate, 2024)
5-year Journal Impact Factor	0.7	2023, Journal Citation Reports (Clarivate, 2024)
Journal Citation Indicator	0.28	2023, Journal Citation Reports (Clarivate, 2024)
CiteScore	1.4	2023, Scopus (Elsevier B.V., 2024)
SCImago Journal Rank	0.339	2023, SJR (Scimago Lab, 2024; Data Source: Scopus)
Source Normalized Impact per Paper	0.582	2023, CWTS Journal Indicators (CWTS B.V., 2024; Data Source: Scopus)
Mathematical Citation Quotient	0.06

More information about journal rankings

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Submit

You can easily submit your manuscript online. Simply go to https://mc.manuscriptcentral.com/dgsnde　and you will be guided through the entire submission and publishing process.
Prepare your manuscript according to the Instructions for Authors and Submission Checklist.
Studies in Nonlinear Dynamics & Econometrics (SNDE) follows Tier 2 of our Data Sharing Policy. Authors are required to provide a data availability statement. Please refer to the Data Sharing Policy for guidance on how to write a data availability statement.
As a condition of submission, the Template for Ethical and Legal Declarations must be customized by the submitting author on behalf of all others and uploaded as a separate Word file.
If you have any general questions please visit our FAQ page for authors.

Your benefits of publishing with us

Rapid online publication ahead-of-print with short turnaround times
The Best Paper Award starting from 2015
High quality manuscript processing through ScholarOne Manuscripts™
Optional open access publication
Accepted papers will be published online first as DOI-citable, forward-linked articles for quickest possible visibility for the scientific community
Every article easily discoverable because of Search Engine Optimization (SEO) and comprehensive abstracting and indexing services
Secure archiving by De Gruyter and the independent archiving service Portico
Professional sales and marketing activities

Submission process

Submission of your paper via our submission management tool
Peer review process (you will be guided through every step)
If accepted: you have the option to publish it open access
Publication online and in print

Please note

Manuscripts must be written in clear and concise English
Before submitting your article please have a look at Ethical Guidelines and our Copyright Transfer Agreement
Once your article is accepted you have the option to publish it open access
Our Repository Policy allows you to distribute 30 PDF copies of your published article to colleagues (the PDF has to include the information that it is an author's copy). Please also feel free to distribute the link to the online abstract
If you have any general questions please visit our FAQ for authors

We look forward to receiving your manuscript!

Editorial

Editor-in-Chief
Jeremy Piger, University of Oregon

Associate Editors
Heather Anderson, Monash University
Robert A. Becker, Indiana University
Hilde C. Bjørnland, BI Norwegian Business School
Francesco Ravazzolo, University of Bolzano
Yoosoon Chang, Indiana University
Robert de Jong, Ohio State University
Cees Diks, CeNDEF, U. of Amsterdam
Alvaro Escribano, Universidad Carlos III de Madrid
Massimo Guidolin, Bocconi University
Dimitris Korobilis, University of Glasgow
Tae-Hwy Lee, University of California-Riverside
Jun Ma, Northeastern University
Vance Martin, University of Melbourne
J. Isaac (Zack) Miller, University of Missouri
Stefan Mittnik, University of Munich
Bruce Mizrach, Rutgers University
James Morley, University of New South Wales
Michael T. Owyang, Assistant Vice President, Federal Reserve Bank of St. Louis
Philip Rothman, East Carolina University
Tatevik Sekhposyan, Texas A&M University
Martin Sola, Birkbeck College
Gerhard Sorger, University of Vienna
Ruey Tsay, University of Chicago
Dick van Dijk, Erasmus University
Anastasios Xepapadeas, Athens University of Economics and Business

Advisory Panel

Jess Benhabib, New York University
William A. Brock, University of Wisconsin-Madison
Jean-Michel Grandmont, CREST-CNRS
James D. Hamilton, University of California-San Diego
Jose Scheinkman, Princeton University

Volume 28 Issue 5

November 2024

Publicly Available December 5, 2024

Frontmatter

Page range: i-ii

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Research Articles

November 16, 2023

Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros

Francisco Blasques, Vladimír Holý, Petra Tomanová

Page range: 673-702

Abstract

In finance, durations between successive transactions are usually modeled by the autoregressive conditional duration model based on a continuous distribution omitting zero values. Zero or close-to-zero durations can be caused by either split transactions or independent transactions. We propose a discrete model allowing for excessive zero values based on the zero-inflated negative binomial distribution with score dynamics. This model allows to distinguish between the processes generating split and standard transactions. We use the existing theory on score models to establish the invertibility of the score filter and verify that sufficient conditions hold for the consistency and asymptotic normality of the maximum likelihood of the model parameters. In an empirical study, we find that split transactions cause between 92 % and 98 % of zero and close-to-zero values. Furthermore, the loss of decimal places in the proposed approach is less severe than the incorrect treatment of zero values in continuous models.

November 27, 2023

Investor Sentiment Mining Based on Bi-LSTM Model and its Impact on Stock Price Bubbles

Haiyuan Yin, Qingsong Yang

Page range: 703-724

Abstract

We built a Bi-Directional long-term and short-term memory (Bi-LSTM) model to identify and classify the Chinese posting text of stocks on the Eastmoney website in China and constructed the daily index of Chinese investors’ sentiment. Furthermore, based on the GSADF method, we examine the stock price bubbles and study the impact of investor sentiment and stock price bubbles. We found investor sentiment has a positive effect on the existence of stock bubbles, as well as their intensity. This effect is more significant in small-scale, high-equity concentration, and non-state-owned enterprises. Investor sentiment has an impact on stock price bubbles through volatility, and stock price bubbles are often accompanied by higher premium risk. The conclusion is helpful to understand the mechanism of investor sentiment on stock bubbles from a micro perspective, and it also can be a reference in identifying stock bubbles from the viewpoint of investor sentiment.

December 4, 2023

Should You Use GARCH Models for Forecasting Volatility? A Comparison to GRU Neural Networks

Alberto Pallotta, Vito Ciciretti

Page range: 725-738

Abstract

The GARCH model is the most used technique for forecasting conditional volatility. However, the nearly integrated behaviour of the conditional variance originates from structural changes which are not accounted for by standard GARCH models. We compare the forecasting performance of the GARCH model to three regime switching models: namely, the Markov Switching GARCH, the Hidden Markov Model, and the Gated Recurrent Unit neural network. We define the number of optimal states by means of three methods: piecewise linear regression, Baum–Welch algorithm and Markov Chain Monte Carlo. Since forecasting volatility models face the bias-variance trade-off, we compare their out-of-sample forecasting performance via a walk-forward methodology. Moreover, we provide a robustness check for the results by applying k-fold cross-validation to the original time series. The Gated Recurrent Unit network is the best suited for volatility forecasting, while the Hidden Markov Model is the best at discerning the market regimes.

November 7, 2023

Determination of the Number of Breaks in High-Dimensional Factor Models via Cross-Validation

Ruichao Zhou, Lu Wang, Jianhong Wu

Page range: 739-750

Abstract

This paper proposes a cross-validation method to estimate the number of breaks in high-dimensional factor models. To preserve the original change structure, the parity-splitting strategy is adopted when employing the cross-validation method. The consistency of the estimator is established under some mild conditions. Simulation results show desired finite sample performance of the proposed method, especially in comparison with methods that need to predetermine the tuning parameters.

December 8, 2023

Artificial Neural Networks and Time Series of Counts: A Class of Nonlinear INGARCH Models

Malte Jahn

Page range: 751-765

Abstract

Time series of counts are frequently analyzed using generalized integer-valued autoregressive models with conditional heteroskedasticity (INGARCH). These models employ response functions to map a vector of past observations and past conditional expectations to the conditional expectation of the present observation. In this paper, it is shown how INGARCH models can be combined with artificial neural network (ANN) response functions to obtain a class of nonlinear INGARCH models. The ANN framework allows for the interpretation of many existing INGARCH models as a degenerate version of a corresponding neural model. Details on maximum likelihood estimation, marginal effects and confidence intervals are given. The empirical analysis of time series of bounded and unbounded counts reveals that the neural INGARCH models are able to outperform reasonable degenerate competitor models in terms of the information loss.

November 28, 2023

Commitment Issues: Does the Fed Have an Inflation Incentive to Commit?

C. Patrick Scott

Page range: 767-784

Abstract

Long-run results indicate that for price and wage inflation there is little disincentive for discretionary policy when monetary policy is at or near the zero-lower bound. Optimal commitment and discretionary policy are examined in a popular DSGE framework. The monetary authority targets a convex combination of price and wage inflationary gaps around time-varying inflation targets. A joint hypothesis test is derived to determine if the central bank faces an inflationary disincentive for activist policy. Considering price and wage inflation separately, there are significant short-run disincentives to discretionary policy. Discretion and commitment policies are not different for price and wage inflation when nominal interest rates are persistently low.

December 1, 2023

Volatility Forecasting Using Quasi-Score-Driven Models with an Application to the Coronavirus Pandemic Period

Astrid Ayala, Szabolcs Blazsek, Adrian Licht

Page range: 785-805

Abstract

We study the statistical and volatility forecasting performances of the recent quasi-score-driven EGARCH (exponential generalized autoregressive conditional heteroscedasticity) models. We compare the quasi-score-driven EGARCH models with GARCH, asymmetric power ARCH (A-PARCH), and all relevant score-driven EGARCH models of the literature. For score-driven and quasi-score-driven EGARCH, we use the following seven score-driven probability distributions: Student’s t -distribution; general error distribution (GED); generalized t -distribution (Gen- t ); skewed generalized t -distribution (Skew-Gen- t ); exponential generalized beta distribution of the second kind (EGB2); normal-inverse Gaussian distribution (NIG); Meixner distribution (MXN). We use all combinations of those distributions for (i) the probability distribution of the dependent variable, and (ii) the probability distribution which defines the quasi-score function updating term of the quasi-score-driven filters. We use daily data for the Standard & Poor’s 500 (S&P 500) index. We find that both in-sample and out-of-sample, quasi-score-driven EGARCH is superior to GARCH, A-PARCH, and score-driven EGARCH. We report in-sample results for the period of January 2000 to December 2020, providing evidence in favor of the quasi-score-driven EGARCH model for the last two decades. We report out-of-sample volatility forecasting results for a period within the coronavirus disease 2019 (COVID-19) pandemic, providing evidence in favor of the quasi-score-driven EGARCH model for a crisis period.

(Deutsch)

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Online ISSN: 1558-3708
Type: Journal
Language: English
Publisher: De Gruyter
First published: April 1, 1996
Publication Frequency: 5 Issues per Year
Audience: Researchers and professionals in the field of econometrics, economic and financial markets