journal: Studies in Nonlinear Dynamics & Econometrics

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Zeitschrift

Studies in Nonlinear Dynamics & Econometrics

Herausgegeben von: Jeremy Piger

Sprache: Englisch

Erstveröffentlichung: 1. April 1996

Erscheinungsweise: 5 Hefte pro Jahr

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Über diese Zeitschrift

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 2024 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Francisco Blasques, Vladimír Holý and Petra Tomanová for their paper: "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros." Studies in Nonlinear Dynamics & Econometrics, vol. 28, no. 5, 2024, pp. 673-702.

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

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Research articles

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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

Geschichte

Content available since 1996 (Volume 1, Issue 1)

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Conventional and Unconventional Monetary Policy Rate Uncertainty and Stock Market Volatility: A Forecasting Perspective

17. September 2025

Ruipeng Liu, Mawuli Segnon, Rangan Gupta, Elie Bouri

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Theory suggests the existence of a bi-directional relationship between stock market volatility and monetary policy rate uncertainty. In light of this, we forecast volatilities of equity markets and shadow short rates (SSR) – a common metric of both conventional and unconventional monetary policy decisions, by applying a bivariate Markov-switching multifractal (MSM) model. Using daily data of eight advanced economies (Australia, Canada, Euro area, Japan, New Zealand, Switzerland, the UK, and the US) over the period of January 1995 to February 2025, we find that the bivariate MSM model outperforms, in a statistically significant manner, not only the benchmark historical volatility and the univariate MSM models, but also the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) framework, particularly at longer forecast horizons. Our findings are robust to different out-of-sample periods, and superiority of the bivariate MSM is also confirmed relative to the corresponding Generalized Autoregressive Score (GAS) model. This finding confirms the bi-directional relationship between stock market volatility and uncertainty surrounding conventional and unconventional monetary policies, which in turn has important implications for academics, investors and policymakers.
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Trend Breaks and the Persistence of Closed-End Fund Discounts

5. September 2025

Nazif Durmaz, Hyeongwoo Kim, Hyejin Lee, Yanfei Sun

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Closed-end fund (CEF) prices often exhibit large and persistent deviations from their associated net asset values (NAVs). This occurrence is puzzling, given that NAVs are openly accessible to the public for CEFs, which essentially consist of repackaged financial assets. The persistence of these deviations is particularly notable when using linear models, suggesting the need for nonlinear models to comprehend this phenomenon known as the CEF discount puzzle. To unravel this puzzle, we employ the RALS-LM framework, enabling the identification of multiple endogenously chosen trend-breaks, and conduct an analysis utilizing data from 31 CEF discounts. Our findings reveal that CEF prices tend to fluctuate around time-varying trends, which aligns with the characteristics of regime switching models. Additionally, we demonstrate that incorporating non-normal errors through moment conditions enhances efficiency at the margin. Moreover, we establish that nonlinearity solely in the form of level shifts falls short in explaining the persistent nature of CEF discounts.
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A Historical Perspective on India’s Inflation Persistence: A Quantile Analysis

25. August 2025

Taniya Ghosh, Yadavindu Ajit

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This study investigates historical inflation persistence in India under three distinct regimes: monetary targeting, multiple indicators, and inflation targeting (IT). Previous studies on India often relied on mean-based techniques, which can be biased when inflation exhibits a skewed distribution. To address this, we employ a quantile-based approach to examine inflation persistence. Our findings reveal asymmetric inflation behavior, with varying persistence depending on the magnitude of the inflation-affecting shock. Inflation persistence was notably high during the multiple indicators regime but declined following the adoption of IT, especially in the pre-COVID period. While IT helped reduce persistence, it was less effective during COVID-19. Using a wavelet-based method, we also find that inflation persistence in India is closely linked to the persistence of food inflation. Given that food price shocks exhibit long-lasting effects, our findings support the case for targeting headline inflation, contributing to the policy debate in India on whether to target headline or core inflation. Overall, our results highlight the evolving nature of inflation persistence across regimes and emphasize the need for complementary supply-side measures to enhance the efficacy of monetary policy.
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Human Capital Externalities, Migration, and Wage Inequality

22. August 2025

Jiancai Pi, Shumin Yu, Ping Xu, Yanwei Fan

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This paper explores the impacts of human capital externalities on rural-urban migration and skilled-unskilled wage inequality. In the basic model with full employment, an increase in the intensity of human capital externalities will increase migration, and reduce wage inequality if the skilled sector is more capital intensive than the unskilled sector. The results of the basic model remain robust under various conditions, which include the incorporation of human capital externalities in the rural sector into the model, the use of the average human capital level to represent human capital externalities in the urban area. When there is an urban minimum wage in the economy (irrespective of whether an informal sector exists or not), wage inequality will be conditionally decreased, but the impact on migration remains unchanged. This also applies when human capital externalities affect skilled and unskilled sectors differently. In the case of a partially open capital market in the economy, migration will still be encouraged, yet wage inequality will be widened.
Open Access
A Novel Nonlinear Fertility Catastrophe Model Based on Thom’s Differential Equations of Morphogenesis and a Topological Alternative to the Micro-Macro Divide in Demography

21. August 2025

Rolando Gonzales Martinez

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This paper introduces a novel fertility model grounded in Thom’s nonlinear differential equations of morphogenesis. Through a three-dimensional catastrophe surface – representing the interaction between latent, continuous fertility determinants and abrupt catastrophic shocks – the model incorporates key socioeconomic and environmental variables that explain demographic patterns across macro-, meso-, and micro-levels, including the global fertility decline, regional population disparities, and localized phenomena such as teenage pregnancy. To demonstrate the model’s practical utility, an agent-based simulation is presented to show how latent factors – education, contraceptive use, and parental involvement – interact with disruptive influences such as socioeconomic deprivation, violence, and substance abuse. A bifurcation set analysis further reveals how minor shifts in socioeconomic conditions can trigger significant fertility changes and critical transition points in demographic dynamics. By unifying Thom’s morphogenetic framework with classical fertility theory, this study advances a pioneering approach to multi-layered demographic analysis and provides a morphogenetic topological alternative to the rigid micro-macro divide in demography. The results offer actionable insights for public health policy, equipping stakeholders to address both gradual trends and sudden demographic shifts.
Open Access
Difference in Differences, Ratio in Ratios, and Ratio in Odds Ratios for Limited Dependent Variables: A Review and More

19. August 2025

Sanghyeok Lee, Myoung-jae Lee

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Difference in differences (DD) is widely used to find policy/treatment effects with observational data, but applying DD to limited dependent variables (LDV’s) Y has been problematic. This paper addresses how to apply DD and related approaches, such as “ratio in ratios” and “ratio in odds ratios”, to LDV’s under the unifying framework of ‘generalized linear models with link functions’. We review and evaluate DD and the related approaches with simulation and empirical studies, and recommend ‘Poisson Quasi-MLE’ for non-negative (such as count or zero-censored) Y and (multinomial) logit MLE for binary, fractional or multinomial Y .
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Multiple Structural Breaks in Vector Error Correction Models

7. August 2025

Domenic Franjic, Markus Mößler, Karsten Schweikert

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The analysis of structural breaks in vector error correction models is often confined to possible level shifts and trend breaks. In contrast, only rudimentary tools are available to deal with breaks in the cointegration matrix and the adjustment towards equilibrium. Particularly, the possibility of multiple structural breaks during long sampling periods is often ignored which can lead to inconsistently estimated coefficients. In this paper, we study a two-step estimator based on a penalized regression to determine the number of structural breaks, their timing, and estimate the model’s coefficients for each regime. We focus on two important cases, namely, (i) constant dynamics but changing long-run equilibria, and (ii) convergence to new long-run equilibria with new adjustment dynamics. We use simulations to investigate the finite sample performance of the two-step estimator and provide an empirical illustration using data on the term structure of interest rates.
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Predictive Accuracy of Impulse Responses Estimated Using Local Projections and Vector Autoregressions

7. August 2025

Zacharias Psaradakis, Martin Sola, Nicola Spagnolo, Patricio Yunis

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We examine the finite-sample accuracy of impulse responses obtained using local projections (LP) and vector autoregressive (VAR) models. In view of the fact that impulse responses are differences between multistep predictors, we propose to assess the relative performance of impulse-response estimators using tests for equal predictive accuracy. In our Monte Carlo experiments, LP-based and VAR-based estimators are found to be equally accurate in large samples under a mean-squared-error risk function. VAR-based estimators tend to have an advantage over LP-based estimators in small and moderately sized samples, particularly at long horizons.
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Big Swings in the Data and Perceived Changes in the Risk Premia

22. Juli 2025

Martin Sola, Fabio Spagnolo, Francisco Terfi

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Stock markets experience periods where stocks or market returns are consistently higher than their mean and other periods where the individual stocks and markets’ volatility fluctuates from high to low. Since these periods do not necessarily coincide, a related question is whether periods where individual stock markets are higher than their mean, usually identified as αs different from zero in the conditional regressions, disappear once the researcher accounts for changing states of the economy. In this spirit, we develop and estimate a state-dependent version of the CAPM pricing model that accounts for considerable swings in the data. We use U.S. financial data to assess the model’s validity and find support for a state-dependent version of the CAPM for the data under consideration. We show how important it is to consider changes in stock and market returns and changes in their variance-covariances, and that, when not accounting for changes in market conditions, may spuriously yield significant α values. We stress that to assess changes in the risk premium, we should not only focus on βs but also allow for changes in the market premium; otherwise, changes in risk premia may be over- or underestimated. In addition, the classification between investment opportunities may be mistaken for a single regime model, even when rolling regressions are used.
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Covariance Matrix Estimation in Time-Varying Factor Models

14. Juli 2025

Jingming Yao, Jianhong Wu

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This paper considers the covariance matrix estimation in time-varying factor models. A so-called two-step method is proposed to estimate the covariance matrix, which is shown to be more accurate for high-dimensional data. Simulation results show that the proposed method has desired performance under finite samples, especially for the case with rapidly changing factor loadings. The empirical study shows that the minimum variance portfolio constructed by this method has desired performance in terms of both return and risk control.
Open Access
Quantifying Extreme Risks in High-Frequency Financial, Energy, and Commodity Markets

10. Juli 2025

Vitali Alexeev, Katja Ignatieva

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This paper develops a robust framework for tail risk assessment in financial, energy, and commodity markets leveraging Tail Conditional Expectation (TCE) within the versatile family of skewed Generalised Hyperbolic (GH) distributions. The GH distributions effectively capture the asymmetric, heavy-tailed behaviour of high-frequency market data, which, in combination with TCE, addresses the limitations of traditional risk measures like Value-at-Risk (VaR). Focusing on exchange-traded funds (ETFs) – USO (crude oil), GLD (gold), and SPY (S & P 500) – we extend the TCE measure to multivariate portfolios and decompose portfolio-level risk into individual asset contributions. This provides clear insights into each asset’s role in overall tail risk. To capture evolving market dynamics, we implement a rolling-window analysis for time-varying TCEs, validated through rigorous backtesting. The results demonstrate the model’s conservative and reliable performance in extreme risk prediction. By offering a flexible and accurate approach to quantify tail risks, our study empowers market participants to manage volatility, navigate systemic shocks, and design effective risk mitigation strategies across diverse financial environments.
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Uncertainty Unpacked: State-Level Housing Market Dynamics in the Face of Shocks

8. Juli 2025

MeiChi Huang

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This study examines the impacts of three varieties of uncertainties on the US state-level housing markets using the Stochastic Volatility in Mean Vector Autoregression (SVMVAR) framework. The findings reveal that all forms of uncertainty intensify in the COVID-19 period. Across most states, shocks to housing volume and price uncertainty generally influence housing volumes and prices in the same direction, although the magnitude of responses to housing price uncertainty differs significantly between housing prices and volumes. The uncertainty spillover is unidirectional: an increase in housing price uncertainty reduces fundamental uncertainty but amplifies volume uncertainty, but the reverse is not observed. The findings highlight the influential roles of housing price uncertainty in driving the real economy and housing markets at state levels.
Open Access
Two-Step Optimal Prediction Under Phillips Triangular Cointegrated System

26. Juni 2025

Yun-Yeong Kim

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This study proposes a two-step optimal best linear predictor (OBLP) under Phillips triangular cointegrated system, deduced from a two-step optimal forecasting method, for non-stationary level variables cointegrated with fundamental variables. In the first step, a cointegration equilibrium is estimated. The difference between the cointegration equilibrium and the other predicted variables is optimally forecasted in the second step, with conditional expectations estimated by the lagged fundamental differences and cointegration errors and summed with the cointegration equilibrium. We show that the OBLP has the lowest mean squared forecasting error among linear forecasting methods, such as random walk, cointegration, and augmented error correction models. In the second step, the cointegration error correction model is converted into a vector autoregression model consisting of the cointegration error and the fundamental differences of the variables and is used to estimate conditional expectations. Simulation results comparing the other predictors with the OBLP and forecast results for the US GDP and consumption applying the OBLP support the theoretical predictions of the forecasting efficiency of the OBLP.
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A Multivariate Nonlinear Analysis of China’s GDP and World Oil Price and Its Implications

26. Juni 2025

Fredj Jawadi, Ruey S. Tsay

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This paper reviews the relationship between China’s GDP and world oil price in recent decades to investigate whether the growth and evolution of the Chinese economy have a significant impact on the global oil market and to measure to what extent oil shocks affect China’s economy. To this end, we propose a multivariate threshold autoregressive model for the two time series and study the associated nonlinear impulse response functions. We also contrast the implications of the nonlinear model with those of the linear model and other alternative specifications. Our findings show certain strong nonlinear effects between China’s GDP and the oil sector. In particular, our nonlinear VAR model points to the presence of nonlinear causality effects when oil price declines, suggesting further evidence of asymmetrical interactions. Our results help to clarify previous studies on the effects of lead-lag between oil price and the real economy as a whole and China in particular. Our findings appear to be in line with Hamilton’s (1983. “Oil and the Macroeconomy Since World War II.” Journal of Political Economy 91 (2): 228–48) work, which points to significant nonlinearity in this relationship. In addition, our On/Off Threshold VAR model allows us to reproduce the time-varying relationship between oil price and China’s GDP.
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Panel Smooth Transition Model with Covariate-Dependent Thresholds and its Application to the Nexus between Investment and Cash Flow

18. Juni 2025

Lixiong Yang, Yanli Xie, Liangyan Yao

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This paper introduces a panel smooth transition model with a covariate-dependent threshold (PSTCT). To overcome the difficulty in estimating threshold parameters of covariate-dependent thresholds, we develop a new algorithm based on the within-group transformation and simulated annealing (SA) technique. We also suggest test statistics for threshold effect, threshold constancy and cross-section dependency in the proposed model. A variable selection procedure is suggested to choose the covariates that affect the threshold. An extension to the setting with multiple regimes is also discussed. Monte Carlo simulations are conducted to investigate the finite sample properties of the proposed estimation and testing procedures. The model is illustrated by revisiting the relationship between firms’ investment and cash flow using the dataset of González, A., T. Teräsvirta, D. van Dijk, and Y. Yang. 2017. “Panel Smooth Transition Regression Models.” CREATES Research Paper . Comparing the proposed model with PSTR model of González, A., T. Teräsvirta, D. van Dijk, and Y. Yang. 2017. “Panel Smooth Transition Regression Models.” CREATES Research Paper , we find different empirical results after incorporating the covariate-dependent thresholds setting, highlighting the importance of splitting the sample based on a number of indicator variables in the cash flow/investment relationship.
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Does Gender Parity in Higher Education Respond Asymmetrically to Remittances? Evidence from Morocco

12. Juni 2025

Oussama Zennati, Jamal Bouoiyour

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This study examines the dynamic relationship between migrant remittances and gender parity in higher education in Morocco from 1975 to 2023. While existing studies primarily focus on the gendered impact of remittances on primary and secondary education in home countries, the effects on tertiary education remain largely unexplored. Furthermore, these studies often assume a symmetrical impact of remittances, which may not be the case. Using linear ARDL modelling, we demonstrate that remittances have a positive and long-term impact on gender parity in higher education in Morocco. This finding underscores the role of remittances in reducing gender inequality in higher education in Morocco. To explore potential asymmetries, we employed a nonlinear ARDL model, which revealed a short-run asymmetrical impact of remittances on parity in higher education. Our findings are robust to alternative variables and demonstrate the potential of remittances to enhance human development indicators in Morocco. Additionally, remittances serve as an effective tool for achieving sustainable development goals in Morocco.
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Frequency Connectedness and Portfolio Implication Between Financial, Green and Commodity Markets: A TVP-VAR Approach

9. Juni 2025

Nawel Ben Amor, Amal Ghorbel, Slah Bahloul

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This paper examines the dynamic connectedness and optimal hedging strategy of the global stock markets indices, commodities and green bonds. We use the novel frequency TVP-VAR method, and then we apply different portfolio construction approaches, namely MVP, MCP and MCoP, and bivariate hedge ratio. We find that stock markets are more interconnected during coronavirus pandemic and Russo-Ukraine conflict. Furthermore, empirical results suggest that the connectivity between variables depends on short-term shock transmissions. Gold act always as the largest shock receiver during periods of crisis. While Platinum and Silver are the main nets-transmitters. Our results indicate that the dynamic portfolio weights for MCP and MCoP appear to be comparable, but notably distinct from the MVP approach. In general, our findings indicate that the majority of commodities offer substantial diversification advantages compared to various stock markets, particularly during crisis periods, surpassing the benefits provided by green bonds. Most of them provide hedging benefits, particularly in comparison to the global index of developed countries and especially during stressful period.
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Another Look into Tail Risk Connectedness Using Network Modelling: Evidence from European Stock Markets

28. Mai 2025

Raffaele Mattera, Javier Sanchez-Garcia

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This paper proposes an approach for measuring tail risk connectedness in financial networks by leveraging spatial methods, particularly the spatial autoregressive model, applied to Value-at-Risk (VaR) and Expected Shortfall (ES) estimates. Given the definition of the network structure, and departing from traditional methods reliant on volatility spillovers, our approach aims to capture the effect of the nature of the event on the tail dimension of market interconnectedness, offering insights beyond conventional metrics. Our results show that since the nature of the event substantially affects the convergence (divergence) of the response of the agents of the financial network to a shock, accounting for this effect is crucial to correctly measure its transmission. Since not all shocks are similar, events such as the Great Recession or the COVID-19 pandemic do not affect the tail risk of the financial network in a similar way, even when volatility spillovers increase in both cases. The relevance of considering the source of the shock is shown through an empirical analysis of the most important European stock markets, demonstrating the efficacy of the proposed approach in assessing systemic risks, therefore providing a valuable tool for policymakers, investors, and financial regulators.
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Estimation of High-Dimensional Matrix Factor Models with Change Points

23. Mai 2025

Lijie Peng, Guchu Zou, Jianhong Wu

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In this paper, we focus on the high-dimensional matrix factor models with change points. We first consider the matrix factor model with a single change point, and propose a least squares estimation method to identify the change point. When the number of change points is unknown, a two-step estimation procedure is developed to identify all the change points. Under some mild conditions, the estimator of the number of change points can be proved to be consistent, and the distance between the estimated and true change points can be proved to be stochastically bounded. Monte Carlo simulation study and real data analysis are carried out for illustration.
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Identifying Shock Propagation Mechanisms in Global Equity Markets

22. Mai 2025

Vance L. Martin, Saikat Sarkar

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The intertemporal capital asset pricing model with time-varying price and quantity risk factors, is used to study the propagation mechanisms linking expected risk premia with shocks in global equity markets. The model allows for linear and nonlinear propagation channels with the relationship between expected risk premia and world and country shocks characterized by a bivariate cubic. Using daily data on developed and emerging country equity returns, the empirical results show that country risk prices are exposed to world risks, although the signs differ between developed and emerging countries. Country risk factors are especially important for risk prices in Asia-Pacific countries as well as selected emerging countries. Of the financial risk factors investigated, volatility risk is important for all developed countries, and currency basis risk is important for all emerging countries. The nonlinear propagation mechanisms linking shocks and expected risk premia are economically significant and show that the size and sign of shocks are important. Implications of the empirical results for international portfolio diversification are also investigated using a range of simulation experiments.
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Environmental Tax and Macroeconomic Dynamics under Vertical Production Structure

22. Mai 2025

Pengqing Zhang, Jiancai Pi

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Both upstream and downstream sectors can generate pollution, but such a vertical production structure has seldom been studied. This paper embeds upstream and downstream linkages into the environmental dynamic general equilibrium model, and explores both aggregate and sectoral effects of the environmental tax on macroeconomic dynamics. We find that: (i) the environmental tax designed for a 20 % reduction in emissions in the economy with upstream and downstream linkages is lower than that without the linkages; (ii) taxing only the upstream sector is more efficient for a moderate environmental tax rate than taxing both sectors, but to achieve a 20 % reduction goal, it will double abatement costs and reduce social welfare; (iii) there is an inverted U-shape implication of the environmental tax on output with respect to the degree of upstream and downstream linkages.
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Macroeconomic Imbalances and Financial Stress Among BRICS: Analysis of Frequency-Dependent and Asymmetric Causal Nexuses

16. April 2025

Mohammed Armah, Ebenezer Bugri Anarfo, Emmanuel Numapau Gyamfi, Godfred Amewu

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This study investigates the intricate and evolving causal relationship between macroeconomic imbalances and financial stress in BRICS nations. To analyse these dynamics across different time scales, thus enabling timely and policy intervention, we employ an asymmetric, noise-reducing-domain ICEEMDAN-based non-parametric model supplemented by a time-varying vector autoregressive model spanning from 1998 to 2021. Our findings reveal that financial stress and macroeconomic imbalance in BRICS exhibit both frequency-dependent and asymmetric linkages, shedding light on the mechanisms underlying financial contagion and market instability in BRICS nations. Specifically deteriorating financial conditions are linked to heightened macroeconomic instability, whereas periods of low stress correspond to improved macroeconomic condition. The results underscore the need to strengthen the macroeconomic frameworks with inflation serving as a critical nominal anchor and focal point of overarching policy guidelines. Proactive monitoring and responses to accumulation of financial imbalances are essential. The study recommends that policymakers should implement fiscal and monetary measures that prioritize sustainable economic growth while reducing reliance on debt and maintaining current account balance to mitigate financial vulnerabilities.
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A Time-Based Pricing Game Considering Echelon Utilization in the Battery Leasing Market

26. März 2025

Junhai Ma, Xue Ding, Li Zhao, Xiaoyan Wang

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At present, many electric vehicle (EV) companies consider the adoption of a battery leasing model as part of their sales strategy, addressing the issue of higher prices for energy vehicles (EVs) compared to internal combustion engine vehicles (ICEVs) of the same level. In order to enhance the development of the battery leasing model in the EV market, this research explores pricing strategies within the battery supply chain operating under the “battery leasing + echelon utilization” model. Both a Stackelberg and a dynamic system model are proposed with the consideration of echelon utilization. We investigate how the echelon utilization rate, battery quality, and recycling price influence pricing decisions for both battery manufacturers and EV companies. These insights offer guidance for these entities in formulating appropriate pricing strategies. Introducing a long-term repeated game model, we analyze system stability and chaos conditions, the finding from the dynamic model discloses how different behaviors impact the long-term profitability of all parties involved. Furthermore, we propose a chaos control method. Our findings provide valuable managerial insights for battery manufacturers and EV companies.
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Ripple Effects of the US-China Tension on Asian Emerging and Frontier Markets with Portfolio Implications

24. März 2025

Naveed Khan, Ozair Siddiqui, OlaOluwa S. Yaya, Xuan Vinh Vo

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In this paper, we investigate the ripple effects of the US-China tension on Asian emerging markets (India, Indonesia, South Korea, Malaysia, Philippines, Taiwan, and Thailand) and Asian frontier markets (Bangladesh, Sri Lanka, Pakistan, Bahrain, Kuwait, Vietnam, and Oman) for the period spanning from February 2013 to February 2024. The US-China Tension Index (UCTI) is a proxy variable for the US-China tension. Time-varying parameter vector autoregression, wavelet coherence, and hedging effectiveness techniques are employed for the empirical analysis. Findings show that the total connectedness between UCTI and Asian frontier markets is stronger than that of Asian emerging markets. Moreover, findings reveal that, in the case of Asian emerging markets, Indonesia, South Korea, Malaysia, Philippines, Taiwan, and Thailand are net transmitters of return spillovers, while India is a net receiver. In the case of Asian frontier markets, we find that Sri Lanka, Bahrain, Pakistan, Kuwait, and Oman are net transmitters. At the same time, Bangladesh and Vietnam are net receivers of return spillovers. In the frequency co-movement analysis, we report a positive correlation between UCTI and these markets at lower frequencies. In comparison, we report a negative correlation at the middle and higher frequencies. Furthermore, we report that hedging ratios highlight the significance of modifying portfolio weights in uncertain times when looking for investment opportunities in Asian emerging and frontier markets. Similarly, our findings highlight important implications for investors and portfolio managers to optimize their investments with risk-adjusted portfolios.
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Fama–French Five-Factor Modeling: New Evidence from a Nonparametric Method

11. März 2025

Zihao Hou, Viktor Manahov, Dimitrios Stafylas

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This study constructs a Fama–French five-factor model that considers the time-varying properties of the parameters and introduces a nonparametric method that estimates the factor loadings. We approach the topic from a micro perspective using high-frequency data to construct factors and models to evaluate the sensitivity of each factor on abnormal returns. The results show that the conditional alphas of portfolios are optimised, and the nonparametric model outperforms the traditional models. Our findings lead investors to consider the impact of parameter time variation when using multi-factor stock selection models to construct asset portfolios.
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Monetary Policy and Growth at Risk: The Role of Financial Conditions

11. Februar 2025

Licheng Zhang

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Previous studies mainly examine the monetary policy transmission to average growth outcomes, this paper instead studies the effects of monetary policy on the tail outcomes of GDP growth – Growth at Risk (GaR). We first use quantile regression to estimate the conditional distribution of GDP growth, and then estimate the impulse responses with smooth local projections. Our results show that GaR deteriorates in response to contractionary monetary policy shocks, suggesting an increase in macroeconomic tail risk. We investigate the role of financial conditions in the transmission of monetary policy to GaR. We find that contractionary monetary policy shocks lead to tighter financial conditions, and tighter financial conditions have significant negative effects on GaR. Moreover, we show that tighter financial conditions increase future recession risk in both the short and medium term.
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Factor Modeling for High-Dimensional Interval-Valued Data

4. Februar 2025

Yan Guo, Guchu Zou, Jianhong Wu

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The paper considers an approximate factor model for interval-valued panel data with both large numbers of cross-section units and time series observations. A ratio-type estimator is proposed for the number of interval-valued factors in the approximate factor model. A variant of the estimator is also suggested, which is robust to the case with dominant factors. Under certain conditions, the estimators can be proved to be consistent. Moreover, the estimators of interval-valued factors and the pooled loadings can be obtained by the principal component analysis method for point-valued data. Monte Carlo simulation studies show that the proposed estimators have the desired finite sample properties.
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Realized Probability Index is a Better Market Timing Indicator

20. Januar 2025

Haibin Xie, Boyao Wu, Yuying Sun, Shouyang Wang

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In practice, market timing is a well-known trading strategy among investors, and different indicators have been proposed for market timing methods. This paper compares three indicators used in market timing strategy: the return, the 0–1 binary index, and the realized probability index. It is shown that realized probability index is more informative and more efficient than the 0–1 binary index in terms of market timing, and tends to be more predictable than the return itself. This finding is interesting and important as it proves for the first time that the realized probability index is a more efficient market timing indicator and thus should be given more attention by both academic researchers and practitioners. An empirical study is performed on different stock indices, and the results confirm our finding.
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A Nonparametric Model for High-Frequency Energy Prices

24. Dezember 2024

Nikolay Gudkov, Katja Ignatieva

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This paper proposes an efficient approach for modelling a high frequency continuous time diffusion process for the dynamics of crude oil. While various applications of continuous time models are considered in the literature, the results on choosing the right model are mixed. We employ a very general non-parametric approach to capture the dynamics of the crude oil market proxied by United States Oil (USO) exchange traded fund. This approach is purely data driven and does not require specification of the drift or the diffusion coefficient function. The proposed nonparametric kernel-based estimation procedure relies on the local polynomial kernel regression, where the choice of a bandwidth parameter plays a significant role. We demonstrate that besides offering a convenient way of estimating the continuous-time models for energy prices, our estimation procedure performs well when dealing with predicting USO prices out-of-sample. The analysis is extended by incorporating possible jump diffusion, where the assumption of continuity of the stochastic process is relaxed and a jump component is added to the diffusion process. In addition, we extend our model by adding possible seasonalities in the underlying dynamics, which requires decomposing the price by means of the Maximum Overlap Discrete Wavelet Transform (MODWT) algorithm and applying nonparametric kernel-based estimation procedure to modelling of the deseasonalized prices.
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Dynamic Panel Threshold Spatial Durbin Model with an Application to the Relationship between Financial Development and Green Growth

16. Dezember 2024

Lili Wei, Chunli Zhang

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This paper studies computation, estimation, inference and specification testing in dynamic panel threshold spatial Durbin (DPTSD) model with multiple thresholds. We first develop a new Markov chain Monte Carlo (MCMC) based algorithm to jointly estimate the threshold parameters and simultaneously construct the confidence intervals for the parameters, after suggesting a within-group spatial two-stage least squares estimator. We then construct test statistics for threshold effect and the number of thresholds. Monte Carlo experiments indicate that the proposed estimator and tests have desired performance in finite samples. We finally apply the DPTSD model to investigate the relationship between financial development and green growth, and find that the empirical results based on the DPTSD model are quite different from these based on the dynamic panel threshold model.
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To Bag is to Prune

25. Oktober 2024

Philippe Goulet Coulombe

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It is notoriously difficult to build a bad Random Forest (RF). Concurrently, RF blatantly overfits in-sample without apparent consequences out-of-sample. Arguments like the bias-variance trade-off or double descent cannot rationalize this paradox. I propose a new explanation: bootstrap aggregation and model perturbation as implemented by RF automatically prune a latent “true” tree. More generally, I document that randomized ensembles of greedily optimized learners implicitly perform optimal early stopping out-of-sample . So, letting RF overfit the training data is a dominant tuning strategy against nature’s undisclosed choice of noise level. Additionally, novel ensembles of Boosting and MARS are also eligible. I empirically demonstrate the property, with simulated and real data, by reporting that these new completely overfitting ensembles perform similarly to their tuned counterparts – or better.
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Impact of Disaggregated External Debt on Economic Growth: Evidence from Asian Developing Economies

10. Oktober 2024

Muhammad Dawood, Zhao Rui Feng, Muhammad Ilyas

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This study investigate the impact of total and disaggregated external debt on economic growth, across 32 Asian developing economies (ADE) from 1995 to 2020. The study applied the generalized method of moments (GMM) and dynamic common correlated estimator (DCCE) with interaction terms of institutional quality and macroeconomic policy to address the non-linearity, across-country heterogeneity, cross-sectional dependence and endogneity. The empirical results indicate that total external debt and its types initially harm economic growth, except for commercial creditors’ debt, which has positive effects. Short-term, private, and multilateral debt show weaker negative associations compared to long-term, public, and bilateral debt. Incorporating interaction terms in non-linear models reveal a shift from negative to positive impacts, highlighting the importance of robust institutional quality and stable macroeconomic policy in mitigating adverse debt effects. Our findings underline the necessity of tailored policies accounting for specific impacts of different debt types on economic growth. Moreover, highlight the significance of institutional quality and macroeconomic policy standards to manage risks, optimize debt management practices, adjust fiscal policies, and foster sustainable economic growth. Future research should further investigate the broad impact of domestic debt in conjunction with external debt across different countries, time periods, and methodological approaches.
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Sectoral Dependence and Financial Contagion in the BRICS Grouping: An Application of the R-Vine Copulas

30. September 2024

Lumengo Bonga-Bonga, Johannes J. Hendriks

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This paper presents a novel approach utilising R-Vine copulas and tail dependence structures to distinguish between contagion and interdependence amid equity market interrelation. The approach is applied in the case of BRICS equity markets. Moreover, rather than analysing the equity markets in aggregate, our approach focuses on sectoral levels within BRICS equity markets to examine the nature of interrelation among them. Based on the tail dependence of sectoral equity market volatilities, empirical findings indicate minimal contagion events across various sectors of the BRICS equity markets. These results are corroborated through portfolio optimisation, demonstrating that markets identified as sources of contagion receive lower weights in the portfolio. This paper offers valuable insights for policymakers, investors, and asset managers by shedding light on the interrelationships among different sectors of the BRICS equity markets and the potential investment strategies that can be formulated based on co-movement types between these markets.
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Asymptotic Properties of ReLU FFN Sieve Estimators

5. September 2024

Frank J. Fabozzi, Hasan Fallahgoul, Vincentius Franstianto, Grégoire Loeper

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Recently, machine learning algorithms have increasing become popular tools for economic and financial forecasting. While there are several machine learning algorithms for doing so, a powerful and efficient algorithm for forecasting purposes is the multi-layer, multi-node neural network with rectified linear unit (ReLU) activation function – deep neural network (DNN). Studies have demonstrated the empirical applications of DNN but have devoted less research to investigate its statistical properties which is mainly due to its severe nonlinearity and heavy parametrization. By borrowing tools from a non-parametric regression framework, sieve estimator, we first show that there exists such a sieve estimator for a DNN. We next establish three asymptotic properties of the ReLU network: consistency, sieve-based convergence rate, and asymptotic normality, and then validate our theoretical results using Monte Carlo analysis.
Open Access
Heterogeneity, Jumps and Co-Movements in Transmission of Volatility Spillovers Among Cryptocurrencies

27. August 2024

Konstantinos Gkillas, Maria Tantoula, Manolis Tzagarakis

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We analyze properties identified in the price volatility of Bitcoin and some of the leading cryptocurrencies namely Litecoin, Ripple, and Ethereum. We employ Heterogeneous Autoregressive models (HAR) in both a univariate and multivariate level of analysis. First, the significance of heterogeneity and jumps is examined, considering the ability of several univariate HAR models, to predict realized volatility of cryptocurrencies. Second, we examine the relevance of realized volatility jumps and covariances in the transmission of volatility spillovers among cryptocurrencies. We perform a comparative spillover analysis of the multivariate HAR models in two versions, considering variances only and covariances as well. Our results indicate that covariances and jumps inclusion lead to an increase in spillovers. The time-varying spillover analysis indicates higher dependency between Bitcoin and the other cryptocurrencies mostly at short frequencies.
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Heterogeneous Volatility Information Content for the Realized GARCH Modeling and Forecasting Volatility

19. August 2024

Wen Xu, Pakorn Aschakulporn, Jin E. Zhang

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As the demand for accuracy in volatility modeling and forecasting increases, the literature tends to incorporate different volatility measures with heterogeneous information content to construct the hybrid volatility model. This study focuses on one of the popular hybrid volatility models: the Realized Generalized Autoregressive Heteroskedasticity (Realized GARCH) and embeds various volatility measures, including the CBOE VIX, VIX1D, Realized Volatility, and Daily Range to examine their heterogeneous impact on the conditional volatility estimation and forecasting. To evaluate the impact of the volatility measures, we first construct a volatility response function. This involves calculating the difference in one-step-ahead conditional volatility forecasts that incorporate information from both return and volatility measures against the forecasts based on return innovations only. Subsequently, the variance share is calculated to evaluate its role in explaining future variations in the Realized GARCH. Our results show that among these four volatility measures, VIX is the most informative volatility. Although VIX1D is overemphasized by the literature, its significance in volatility forecasting remains substantial, confirming that risk-neutral volatility measures are generally more informative than physical measures. Finally, we also find that incorporating multiple risk-neutral volatility measures does not improve forecasting performance compared to using a single measure due to overlapping information.
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Likelihood-Ratio-Based Confidence Intervals for Multiple Threshold Parameters

8. August 2024

Luiggi Donayre

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This paper proposes the inversion of likelihood ratio tests for the construction of confidence intervals for multiple threshold parameters. Using Monte Carlo simulations, conservative likelihood-ratio-based confidence intervals are shown to exhibit empirical coverage rates at least as high as nominal levels for all threshold parameters, while still being informative in the sense of only including relatively few observations in each confidence interval. These findings are robust to the magnitude of the threshold effect, the sample size and the presence of serial correlation. Applications to existing models with multiple thresholds for U.S. real GDP growth and for the wage Phillips curve demonstrate how the proposed approach is empirically relevant to make inferences about the uncertainty of threshold estimates.
Open Access
Which Global Cycle? A Stochastic Factor Selection Approach for Global Macro-Financial Cycles

8. August 2024

Tino Berger, Sebastian Hienzsch

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Instead of assuming a certain factor structure, we statistically test for the factor structure driving common global dynamics in macroeconomic and financial data by employing a stochastic factor selection approach. Using a sample of 16 developed countries from 1996Q1 to 2019Q4, we present strong empirical evidence of a global macro-financial cycle and an independent global financial cycle. Moreover, the global macro-financial cycle we estimate is essentially the global business cycle identified in the literature. It captures the common global macroeconomic dynamics and drives a significant share of the comovement in the financial sector. The remaining commonality in financial variables is driven by separate global financial cycles: the global credit cycle and the global capital flow cycle.
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Determination of the Number of Breaks in Heterogeneous Panel Data Models

8. August 2024

Lu Wang, Shuke Hu

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This paper considers a heterogeneous panel data model with an unknown number of breaks. We propose a so-called two-stage procedure to determine the number of breaks and detect the location of break points. The consistency of the estimated number of breaks and the estimated break points are established under fairly general conditions. Monte Carlo simulations and two empirical applications are provided to demonstrate the finite sample performance of the proposed method.
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Non-Linear Impact of Income Inequality on Mental Health: Evidence from Low and Middle-Income Countries

19. Juli 2024

Ankita Mishra, Abebe Hailemariam, Preety Srivastava, Greeni Maheshwari

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In this study, we examine the relationship between income inequality and mental health using a sample of low and middle-income countries over the period 1990–2019. Using a dynamic panel threshold model that allows for endogeneity in both the regressors and threshold variable, we find a non-linear relationship between income inequality and the prevalence of mental health disorders. Specifically, income inequality is associated with reduced prevalence of mental health disorders at low levels of income inequality but after it surpasses a threshold Gini coefficient (estimated between 39 and 49), it has an adverse effect on mental health. The impact is more pronounced in low income and lower middle-income countries. We also find evidence of heterogenous effects by age and gender. Our findings indicate the importance of modelling non-linearity in the income inequality-health relationship and highlight the importance of keeping income inequality within reasonable bounds.
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Score-Driven Location Plus Scale Models: Asymptotic Theory and An Application to Forecasting Dow Jones Volatility

10. Juni 2024

Szabolcs Blazsek, Alvaro Escribano, Adrian Licht

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Journal Impact Factor	0.9	2024, Journal Citation Reports (Clarivate, 2025)
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CiteScore	1.4	2024, Scopus (Elsevier B.V., 2025)
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Source Normalized Impact per Paper	0.503	2024, CWTS Journal Indicators (CWTS B.V., 2025; Data Source: Scopus)
Mathematical Citation Quotient	0.06

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