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Challenges and Opportunities for Twenty First Century Bayesian Econometricians: A Personal View Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2024-03-11 Herman K. van Dijk
This essay is about Bayesian econometrics with a purpose. Specifically, six societal challenges and research opportunities that confront twenty first century Bayesian econometricians are discussed using an important feature of modern Bayesian econometrics: conditional probabilities of a wide range of economic events of interest can be evaluated by using simulation-based Bayesian inference. The enormous
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Generalized Autoregressive Conditional Betas: A New Multivariate Score-Driven Filter Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2024-03-07 Szabolcs Blazsek, August Jörding, Simran Rai
In this paper, we extend the recent Gaussian autoregressive conditional beta (Gaussian-ACB) model from the literature on score-driven models. In the new asset pricing model, named the t generalized ACB (t-GACB) model, a multivariate score-driven filter for the t-distribution drives dynamic interaction effects among the beta coefficients. We present the econometric formulation and statistical inference
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Information Content of Inflation Expectations: A Copula-Based Model Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2024-03-01 Omid M. Ardakani
This paper introduces a holistic framework that integrates copula modeling and information-theoretic measures to examine the information content of inflation expectations. Copulas are used to capture the dynamic dependence between inflation and expectations, accounting for extreme events and tail dependence. Information-theoretic measures are employed to quantify the information that expectations provide
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Controlling Chaotic Fluctuations through Monetary Policy Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2024-01-09 Takao Asano, Akihisa Shibata, Masanori Yokoo
This paper applies the chaos control method (the OGY method) proposed by Ott, E., C. Grebogi, and J. A. Yorke. (1990. “Controlling Chaos.” Physical Review Letters 64: 1196–9) to policy-making in macroeconomics. This paper demonstrates that the monetary equilibrium paths in a discrete-time, two-dimensional overlapping generations model exhibit chaotic fluctuations depending on the money supply rate
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Investigating the Impact of Consumption Distribution on CRRA Estimation: Quantile-CCAPM-Based Approach Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2024-01-08 Sofia B. Ramos, Abderrahim Taamouti, Helena Veiga
Using quantile maximization decision theory, this paper considers a quantile-based Euler equation that states that the asset price is a function of the quantiles of the payoff, consumption growth, the stochastic discount factor, risk aversion, and the distribution of the consumption growth rate. We use a more general distribution assumption (log-elliptical distributions) than the log-normality of the
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Examining the Impact of Energy Policies on CO2 Emissions with Information and Communication Technologies and Renewable Energy Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2024-01-04 Mei Xue, Daniela Mihai, Madalina Brutu, Luigi Popescu, Crenguta Ileana Sinisi, Ajay Bansal, Mady A. A. Mohammad, Taseer Muhammad, Malik Shahzad Shabbir
The world today presents significant environmental concerns for humans, such as smog and warmer temperatures, but we also need to think about how to accomplish economic growth that is sustainable. Therefore, this exploration researches the asymmetric effect of renewable energy consumption, economic growth and financial development on carbon emanation in the emerging economies. For this reason, this
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Bayesian Reconciliation of Return Predictability Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-12-26 Borys Koval, Sylvia Frühwirth-Schnatter, Leopold Sögner
This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The bivariate VAR system comprises asset returns and a further prediction variable, such as the dividend-price ratio, and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this
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Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-12-22 Liana Jacobi, Chun Fung Kwok, Andrés Ramírez-Hassan, Nhung Nghiem
Increases in the use of Bayesian inference in applied analysis, the complexity of estimated models, and the popularity of efficient Markov chain Monte Carlo (MCMC) inference under conjugate priors have led to more scrutiny regarding the specification of the parameters in prior distributions. Impact of prior parameter assumptions on posterior statistics is commonly investigated in terms of local or
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Financial Condition Indices in an Incomplete Data Environment Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-12-20 Miguel C. Herculano, Punnoose Jacob
We construct a Financial Conditions Index (FCI) for the United States using a dataset that features many missing observations. The novel combination of probabilistic principal component techniques and a Bayesian factor-augmented VAR model resolves the challenges posed by data points being unavailable within a high-frequency dataset. Even with up to 62 % of the data missing, the new approach yields
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Power of Unit Root Tests Against Nonlinear and Noncausal Alternatives with an Application to the Brent Crude Oil Price Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-12-14 Frédérique Bec, Alain Guay, Heino Bohn Nielsen, Sarra Saïdi
The increasing sophistication of economic and financial time series modelling creates a need for a test of the time dependence structure of the series which does not require a proper specification of the alternative. Indeed, the latter is unknown beforehand. Yet, the stationarity has to be established before proceeding to the estimation and testing of causal/noncausal or linear/nonlinear models as
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Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-12-14 Tony Chernis
Bayesian Predictive Synthesis is a flexible method of combining density predictions. The flexibility comes from the ability to choose an arbitrary synthesis function to combine predictions. I study choice of synthesis function when combining large numbers of predictions – a common occurrence in macroeconomics. Estimating combination weights with many predictions is difficult, so I consider shrinkage
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Modeling Corporate CDS Spreads Using Markov Switching Regressions Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-12-11 Ovielt Baltodano López, Giacomo Bulfone, Roberto Casarin, Francesco Ravazzolo
This paper investigates the determinants of the European iTraxx corporate CDS index considering a large set of explanatory variables within a Markov switching model framework. The influence of financial and economic variables on CDS spreads are compared using linear, two, three, and four-regime models in a sample post-subprime financial crisis up to the COVID-19 pandemic. Results indicate that four
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Artificial Neural Networks and Time Series of Counts: A Class of Nonlinear INGARCH Models Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-12-07 Malte Jahn
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
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Should You Use GARCH Models for Forecasting Volatility? A Comparison to GRU Neural Networks Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-12-04 Alberto Pallotta, Vito Ciciretti
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
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Interfuel Substitution and Inflation Dynamics in India Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-11-30 Anirban Sengupta, Apostolos Serletis, Libo Xu
This paper uses neoclassical microeconomic theory to investigate the demand for energy and interfuel substitution in India at the sectoral level. It makes full use of the relevant economic theory and econometrics and generates inference in terms of Allen and Morishima elasticities of substitution that are internally consistent with the data and nonlinear models used. The results indicate that the interfuel
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Volatility Forecasting Using Quasi-Score-Driven Models with an Application to the Coronavirus Pandemic Period Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-11-30 Astrid Ayala, Szabolcs Blazsek, Adrian Licht
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
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Commitment Issues: Does the Fed Have an Inflation Incentive to Commit? Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-11-27 C. Patrick Scott
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
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Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-11-27 Shih-Tang Hwu, Chang-Jin Kim
The basic Markov-switching model has been extended in various ways ever since the seminal work of Hamilton (1989. “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica 57: 357–84). However, the estimation of Markov-switching models in the literature has relied upon parametric assumptions on the distribution of the error term. In this paper, we present
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A Dynamic Latent-Space Model for Asset Clustering Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-11-23 Roberto Casarin, Antonio Peruzzi
Periods of financial turmoil are not only characterized by higher correlation across assets but also by modifications in their overall clustering structure. In this work, we develop a dynamic Latent-Space mixture model for capturing changes in the clustering structure of financial assets at a fine scale. Through this model, we are able to project stocks onto a lower dimensional manifold and detect
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Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-11-15 Francisco Blasques, Vladimír Holý, Petra Tomanová
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
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Stability in Threshold VAR Models Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-11-09 Pu Chen, Willi Semmler
This paper investigates the stability of threshold autoregressive models. We review recent research on stability issues from both a theoretical and empirical standpoint. We provide a sufficient condition for the stationarity and ergodicity of threshold autoregressive models by applying the concept of joint spectral radius to the switching system. The joint spectral radius criterion offers a generally
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Determination of the Number of Breaks in High-Dimensional Factor Models via Cross-Validation Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-11-07 Ruichao Zhou, Lu Wang, Jianhong Wu
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
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Comparison of Score-Driven Equity-Gold Portfolios During the COVID-19 Pandemic Using Model Confidence Sets Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-11-07 Astrid Ayala, Szabolcs Blazsek, Adrian Licht
Gold may have a hedge, safe haven, or diversifier property when added to stock portfolios. Motivated by the favorable statistical properties and out-of-sample performance of score-driven models, we investigate for equity-gold portfolios whether score-driven mean, volatility, and copula models can improve the performances of DCC (dynamic conditional correlation) portfolios, the naïve portfolio strategy
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Co-Jumping of Treasury Yield Curve Rates Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-11-07 Jozef Baruník, Pavel Fišer
We study the role of co-jumps in the interest rate futures markets. To disentangle continuous part of quadratic covariation from co-jumps, we localize the co-jumps precisely through wavelet coefficients and identify statistically significant ones. Using high frequency data about U.S. and European yield curves we quantify the effect of co-jumps on their correlation structure. Empirical findings reveal
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Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-10-31 Niko Hauzenberger, Florian Huber, Gary Koop
Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demands of Markov Chain Monte Carlo (MCMC) methods mean their use is limited to the case where the number of predictors is not too large. In light of these two concerns, this paper proposes
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Bayesian inference for non-anonymous growth incidence curves using Bernstein polynomials: an application to academic wage dynamics Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-07-21 Edwin Fourrier-Nicolaï, Michel Lubrano
The paper examines the question of non-anonymous Growth Incidence Curves (na-GIC) from a Bayesian inferential point of view. Building on the notion of conditional quantiles of Barnett (1976. “The Ordering of Multivariate Data.” Journal of the Royal Statistical Society: Series A 139: 318–55), we show that removing the anonymity axiom leads to a complex and shaky curve that has to be smoothed, using
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Matrix autoregressive models: generalization and Bayesian estimation Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-07-04 Alessandro Celani, Paolo Pagnottoni
The issue of modelling observations generated in matrix form over time is key in economics, finance and many domains of application. While it is common to model vectors of observations through standard vector time series analysis, original matrix-valued data often reflect different types of structures of time series observations which can be further exploited to model interdependencies. In this paper
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HPX filter: a hybrid of Hodrick–Prescott filter and multiple regression Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-06-23 Hiroshi Yamada
This paper considers an extension of Hodrick–Prescott (HP) filter. It is a hybrid of HP filter and multiple regression. We refer to the filter as “HPX filter”. It is well known that HP filter has a unique global minimizer and the solution can be represented in matrix notation explicitly. Does HPX filter also have a unique global minimizer? Is it accomplished without any additional assumptions? Can
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Age and gender differentials in unemployment and hysteresis Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-06-07 Amy Y. Guisinger, Laura E. Jackson, Michael T. Owyang
We use a time-varying panel unobserved components model to estimate unemployment gaps disaggregated by age and gender. Recessions before COVID affected men’s labor market outcomes more than women’s; however, the reverse was true for the COVID recession, with effects amplified for younger workers. We introduce time-variation in both the hysteresis dynamics and the Phillips-curve coefficients on labor
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Welfare cost of inflation, when credit card transaction services are included among monetary services Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-05-29 William A. Barnett, Sohee Park
We investigate the welfare cost of anticipated inflation, when the volume of credit card transactions is included in measured monetary service flows. We use the credit-card-augmented Divisia monetary aggregates in a nonlinear dynamic stochastic general equilibrium (DSGE) New Keynesian model and calculate the welfare costs of inflation. The welfare costs of inflation with credit card services included
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Sequential Monte Carlo with model tempering Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-05-05 Marko Mlikota, Frank Schorfheide
Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential
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Have European natural gas prices decoupled from crude oil prices? Evidence from TVP-VAR analysis Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-04-29 Karol Szafranek, Michał Rubaszek
Unprecedented increases in European natural gas prices observed between late 2021 and mid 2022 raise a question about the sources of these events. In this article we investigate this topic using a time-varying parameters structural vector autoregressive model for crude oil, US and European natural gas prices. This flexible framework allows us to measure how disturbances specific to the analyzed markets
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Integrated variance of irregularly spaced high-frequency data: A state space approach based on pre-averaging Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-03-22 Vitali Alexeev, Jun Chen, Katja Ignatieva
We propose a new state space model to estimate the Integrated Variance (IV) in the presence of microstructure noise. Applying the pre-averaging sampling scheme to the irregularly spaced high-frequency data, we derive equidistant efficient price approximations to calculate the noise-contaminated realised variance (NCRV), which is used as an IV estimator. The theoretical properties of the new volatility
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On testing for bubbles during hyperinflations Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-03-13 Rubens Morita, Zacharias Psaradakis, Martin Sola, Patricio Yunis
We consider testing for the presence of rational bubbles during hyperinflations via an analysis of the non-stationarity properties of relevant observable time series. The test procedure is based on a Markov regime-switching model with independent stochastic changes in its intercept, error variance and autoregressive coefficients. This model formulation allow us to disentangle fundamentals-driven changes
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Multi-kernel property in high-frequency price dynamics under Hawkes model Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-03-10 Kyungsub Lee
This study investigates and uses multi-kernel Hawkes models to describe a high-frequency mid-price process. Each kernel represents a different responsive speed of market participants. Using the conditional Hessian, we examine whether the numerical optimizer effectively finds the global maximum of the log-likelihood function under complicated modeling. Empirical studies that use stock prices in the
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Analysis of heterogeneous duopoly game with information asymmetry based on extrapolative mechanism Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-03-03 Jing Yuan, Jianjun Zhu
Information plays an important role in decision-making process in oligopoly market. This paper establishes two Cournot duopoly games with information asymmetry based on extrapolative mechanism, and focus on the impacts of information asymmetry from the perspective of stability, complexity and profit. The results show that the extrapolative mechanism plays a different role for heterogeneous expectation
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Estimation and testing of the factor-augmented panel regression models with missing data Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-03-02 Difa Xiao, Lu Wang, Jianhong Wu
This paper focuses on the factor-augmented panel regression models with missing data and individual-varying factors. A so-called CCEM estimator for the slope coefficient is proposed and its asymptotic properties are investigated under some regularity conditions. Furthermore, a joint test statistic is constructed for serial correlation and heteroscedasticity in the idiosyncratic errors. Under the null
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Causal relationships between cryptocurrencies: the effects of sampling interval and sample size Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-02-27 Nezir Köse, Emre Ünal
For this paper, the relationship between seventeen popular cryptocurrencies was analyzed by multivariate Granger causality tests and simple linear regression, using data spanning the period 1 September 2020 to 8 December 2021. The novelty of this work is that it studies the effects of sampling interval and sample size in cryptocurrency markets, which can yield significantly different results. Minute-by-minute
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Panel threshold model with covariate-dependent thresholds and its application to the cash flow/investment relationship Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-02-27 Lixiong Yang
This paper introduces a panel threshold model with covariate-dependent and time-varying thresholds (PTCT), which extends the classical panel threshold model of Hansen, B. E. 1999. “Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference.” Journal of Econometrics 93: 345–68 to a framework with multiple covariate-dependent and time-varying thresholds. Based on the within-group transformation
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Volatility and dependence in cryptocurrency and financial markets: a copula approach Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2023-01-09 Jinan Liu, Apostolos Serletis
We use a semiparametric GARCH-in-Mean copula model to examine the volatility dynamics and tail dependence between cryptocurrency markets and financial markets. We do not find any statistically significant tail dependence between the financial and cryptocurrency markets, but we find lower tail dependence between Bitcoin and stock returns. There is lower tail dependence among Bitcoin, Ethereum, and Litecoin
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Bayesian VARs and prior calibration in times of COVID-19 Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-12-26 Benny Hartwig
This paper investigates the ability of several generalized Bayesian vector autoregressions to cope with the extreme COVID-19 observations and discusses their impact on prior calibration for inference and forecasting purposes. It shows that the preferred model interprets the pandemic episode as a rare event rather than a persistent increase in macroeconomic volatility. For forecasting, the choice among
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A new test for non-linear hypotheses under distributional and local parametric misspecification Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-11-30 Anil K. Bera, Osman Doğan, Süleyman Taşpınar
In this paper, we develop a new version of Rao’s score (RS) statistic for testing a non-linear hypothesis under both distributional and local parametric misspecification. Our suggested test statistic is constructed through a size correction approach so that it becomes robust to both types of misspecification. We establish the asymptotic properties of the robust test statistic and provide several examples
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Optimization study of momentum investment strategies under asymmetric power-law distribution of return rate Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-11-11 Xu Wu, Kun Wang, Linlin Zhang, Chong Peng
In the context that the tails of security returns obey an asymmetric power-law distribution, this paper constructs two fractal statistical measures based on fractal theory: fractal expectation and fractal variance. Subsequently, a new momentum strategy is constructed by introducing the fractal measures into the momentum strategy as measures of returns and risks to optimize the selection criterion.
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Estimating uncertainty spillover effects across euro area using a regime dependent VAR model Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-11-02 Giovanni Angelini, Mauro Costantini, Joshy Easaw
This paper investigates macroeconomic uncertainty spillover effects across countries and their impact on real economic activity in different economic periods, i.e. pre-crisis and during the recent financial crisis. The analysis is initially carried out using Monte Carlo simulations and, subsequently, real data for four euro zone economies, namely Italy, France, Germany, and Spain. The Monte Carlo findings
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Bayesian inference for order determination of double threshold variables autoregressive models Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-11-02 Xiaobing Zheng, Qiang Xia, Rubing Liang
The reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm can generate a jump Markov chain in the parameter space of different dimensions, and select a suitable model effectively. In this paper, when the order of the double threshold variables autoregressive (DT-AR) is unknown, the RJMCMC method is designed to identify the order of the DT-AR model in this paper. The simulation experiments and
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Unrestricted, restricted, and regularized models for forecasting multivariate volatility Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-04-21 Stanislav Anatolyev,Filip Staněk
Abstract We perform an extensive investigation of different specifications of the BEKK-type multivariate volatility models for a moderate number of assets, focusing on how the degree of parametrization affects forecasting performance. Because the unrestricted specification may be too generously parameterized, often one imposes restrictions on coefficient matrices constraining them to have a diagonal
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Controlling chaos in New Keynesian macroeconomics Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-04-21 William A. Barnett,Giovanni Bella,Taniya Ghosh,Paolo Mattana,Beatrice Venturi
Abstract In a New Keynesian model, it is believed that combining active monetary policy using a Taylor rule with a passive fiscal rule can achieve local equilibrium determinacy. However, even with such policies, indeterminacy can occur from the emergence of a Shilnikov chaotic attractor in the region of the feasible parameter space. That result, shown by Barnett et al. (2022a), “Shilnikov Chaos, Low
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Financial crisis spread, economic growth and unemployment: a mathematical model Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-04-21 Calvin Tadmon,Eric Rostand Njike Tchaptchet
Abstract The unemployment is the main channel through which the economic and financial crises influence the social development. In this paper, we propose a mathematical model to study the interactions between financial crisis spread, economic growth and unemployment. We also solve an optimal control problem focusing on the minimization, at the lowest cost, of the adverse effects of the financial crisis
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Asymmetry in stochastic volatility models with threshold and time-dependent correlation Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-04-13 Torben Schäfers,Long Teng
Abstract In this work we study the effects by including threshold, constant and time-dependent correlation in stochastic volatility (SV) models to capture the asymmetry relationship between stock returns and volatility. We develop SV models which include only time-dependent correlated innovations and both threshold and time-dependent correlation, respectively. It has been shown in literature that the
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Conservatorship, quantitative easing, and mortgage spreads: a new multi-equation score-driven model of policy actions Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-03-31 Szabolcs Blazsek,Virag Blazsek,Adam Kobor
Abstract In this paper, the effects of United States (US) policy actions on mortgage-backed security and mortgage loan spreads are measured, by using data before, during, and after the US subprime mortgage crisis. We study the effects of the following policy actions: (i) the placement of Fannie Mae and Freddie Mac into US Government conservatorship; (ii) the US Federal Reserve quantitative easing (QE)
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Estimation and forecasting of long memory stochastic volatility models Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-03-25 Omar Abbara,Mauricio Zevallos
Abstract Stochastic Volatility (SV) models are an alternative to GARCH models for estimating volatility and several empirical studies have indicated that volatility exhibits long-memory behavior. The main objective of this work is to propose a new method to estimate a univariate long-memory stochastic volatility (LMSV) model. For this purpose we formulate the LMSV model in a state-space representation
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A Gini estimator for regression with autocorrelated errors Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-03-24 Ndéné Ka,Stéphane Mussard
Abstract The widely used Prais–Winsten technique for estimating parameters of linear regression model with serial correlation is sensitive to outliers. In this paper, an alternative method based on Gini mean difference (GMD) is proposed. A Monte Carlo simulation is used to show that the Gini estimator is more robust than the general least squares one when the data are contaminated by outliers.
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Score-driven location plus scale models: asymptotic theory and an application to forecasting Dow Jones volatility Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-03-07 Szabolcs Blazsek,Alvaro Escribano,Adrian Licht
Abstract We present the Beta-t-QVAR (quasi-vector autoregression) model for the joint modelling of score-driven location plus scale of strictly stationary and ergodic variables. Beta-t-QVAR is an extension of Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) and Beta-t-EGARCH-M (Beta-t-EGARCH-in-mean). We prove the asymptotic properties of the maximum likelihood
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Expected, unexpected, good and bad aggregate uncertainty Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2022-02-02 Jorge M. Uribe,Helena Chuliá
Abstract We study aggregate uncertainty and its linear and nonlinear impact on real and financial markets. By distinguishing between four general notions of aggregate uncertainty (good-expected, bad-expected, good-unexpected, bad-unexpected) within a simple, common framework, we show that it is bad-unexpected uncertainty shocks that generate a negative reaction of economic variables (such as investment
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Instability in regime switching models Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2021-11-01 Pu Chen,Chih-Ying Hsiao,Willi Semmler
Abstract In this paper, we look at the instability of a self-exciting regime-switching autoregressive model, specifically regime-switching models that are locally stable in each of their regimes. It turns out that the local stability of each regime is insufficient to ensure the overall stability of the model. The instability’s mechanism is described, and a sufficient condition for the instability is
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Transition from the Taylor rule to the zero lower bound Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2021-10-25 Stan Hurn,Nicholas Johnson,Annastiina Silvennoinen,Timo Teräsvirta
Abstract This paper examines the Taylor rule in the context of United States monetary policy since 1965, particularly with respect to the zero-lower-bound era of the federal funds rate from 2009 to 2016. A nonlinear Taylor rule is developed which features smooth transitions in the first two moments of the federal funds rate. This flexible specification is found to usefully capture observed nonlinearity
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A family of nonparametric unit root tests for processes driven by infinite variance innovations Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2021-10-20 Kemal Caglar Gogebakan
Abstract This paper presents extensions to the family of nonparametric fractional variance ratio (FVR) unit root tests of Nielsen (2009. “A Powerful Test of the Autoregressive Unit Root Hypothesis Based on a Tuning Parameter Free Statistic.” Econometric Theory 25: 1515–44) under heavy tailed (infinite variance) innovations. In this regard, we first develop the asymptotic theory for these FVR tests
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Recovering cointegration via wavelets in the presence of non-linear patterns Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2021-10-15 Jorge Martínez Compains,Ignacio Rodríguez Carreño,Ramazan Gençay,Tommaso Trani,Daniel Ramos Vilardell
Abstract Johansen’s Cointegration Test (JCT) performs remarkably well in finding stable bivariate cointegration relationships. Nonetheless, the JCT is not necessarily designed to detect such relationships in presence of non-linear patterns such as structural breaks or cycles that fall in the low frequency portion of the spectrum. Seasonal adjustment procedures might not detect such non-linear patterns
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Forecasting transaction counts with integer-valued GARCH models Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2021-09-13 Abdelhakim Aknouche,Bader S. Almohaimeed,Stefanos Dimitrakopoulos
Abstract Using numerous transaction data on the number of stock trades, we conduct a forecasting exercise with INGARCH models, governed by various conditional distributions; the Poisson, the linear and quadratic negative binomial, the double Poisson and the generalized Poisson. The model parameters are estimated with efficient Markov Chain Monte Carlo methods, while forecast evaluation is done by calculating
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Consumption, aggregate wealth and expected stock returns: a quantile cointegration approach Studies in Nonlinear Dynamics & Econometrics (IF 1.032) Pub Date : 2021-09-13 Ricardo Quineche
Abstract This paper empirically examines the long-run relationship between consumption, asset wealth and labor income (i.e., cay) in the United States through the lens of a quantile cointegration approach. The advantage of using this approach is that it allows for a nonlinear relationship between these variables depending on the level of consumption. We estimate the coefficients using a Phillips–Hansen