Edoardo Otranto

This paper investigates the impact of Trade Policy Uncertainty (TPU) on stock–bond correlation dynamics in the United States. Using daily data on major U.S. stock indices and the 10-year Treasury bond from 2015 to 2025, we estimate correlation within a two-step GARCH-based framework, relying on multivariate specifications, including Constant Conditional Correlation (CCC), Smooth Transition Conditional Correlation (STCC), and Dynamic Conditional Correlation (DCC) models to test the hypothesis that political agency significantly alters market structures. We extend these frameworks by incorporating the TPU index and a presidential dummy as exogenous social and political indicators to capture the effects of trade uncertainty and government cycles. The findings show that constant correlation models are strongly rejected in favor of time-varying specifications capable of reflecting the fluid nature of investor expectations. Both STCC and DCC models confirm TPU’s central role in driving correlation dynamics, with significant differences across political regimes. DCC models augmented with TPU and political effects deliver the best in-sample fit and strongest forecasting performance, demonstrating the necessity of integrating political variables into econometric modeling to achieve a more robust understanding of market interdependencies.

This paper proposes a novel data-driven approach for identifying and modelling areas with similar temperature variations throufigureh clustering and Space-Time AutoRegressive (STAR) models. Using annual temperature data from 168 countries (1901-2022), we apply three clustering methods based on (i) warming rates, (ii) annual temperature variations, and (iii) persistence of variation signs, using Euclidean and Hamming distances. These clusters are then employed to construct alternative spatial weight matrices for STAR models. Empirical results show that distance-based STAR models outperform classical contiguity-based ones, both in-sample and out-of-sample, with the Hamming distance-based STAR model achieving the best predictive accuracy. The study demonstrates that using statistical similarity rather than geographical proximity improves the modelling of global temperature dynamics, suggesting broader applicability to other environmental and socioeconomic datasets.

We propose a modeling framework for time-varying covariance matrices based on the assumption that the logarithm of a realized covariance matrix follows a matrix-variate oNrmal distribution. By operating in the space of symmetric matrices, the approach guarantees positive definiteness without imposing parameter constraints beyond stationarity. The conditional mean of the logarithmic covariance matrix is specified through a BEKK-type structure that can be rewritten as a diagonal vector representation, yielding a parsimonious specification that mitigates the curse of dimensionality. Estimation is performed by maximum likelihood exploiting properties of matrix-variate Normal distributions and expressing the scale parameter matrix as a function of the location matrix. The covariance matrix is recovered via the matrix exponential. Since this transformation induces an upward bias, an approximate, time-specific bias correction based on a second-order Taylor expansion is proposed. The framework is flexible and applicable to a wide class of problems involving symmetric positive definite matrices.

This paper investigates the impact of Trade Policy Uncertainty (TPU) on stock-bond correlation dynamics in the United States. Using daily data on major U.S. stock indices and the 10-year Treasury bond from 2015 to 2025, we estimate correlation within a two-step GARCH-based framework, relying on multivariate specifications, including Constant Conditional Correlation (CCC), Smooth Transition Conditional Correlation (STCC), and Dynamic Conditional Correlation (DCC) models. We extend these frameworks by incorporating TPU index and a presidential dummy to capture effects of trade uncertainty and government cycles. The findings show that constant correlation models are strongly rejected in favor of time-varying specifications. Both STCC and DCC models confirm TPU's central role in driving correlation dynamics, with significant differences across political regimes. DCC models augmented with TPU and political effects deliver the best in-sample fit and strongest forecasting performance, as measured by statistical and economic loss functions.

Recent developments in financial time series focus on modeling volatility across multiple assets or indices in a multivariate framework, accounting for potential interactions such as spillover effects. Furthermore, the increasing integration of global financial markets provides a similar dynamics (referred to as comovement). In this context, we introduce a novel model for volatility vectors within the Multiplicative Error Model (MEM) class. This framework accommodates both spillover and co-movement effects through a distinct latent component. By adopting a specific parameterization, the model remains computationally feasible even for high-dimensional volatility vectors. To reduce the number of unknown coefficients, we propose a simple model-based clustering procedure. We illustrate the effectiveness of the proposed approach through an empirical application to 29 assets of the Dow Jones Industrial Average index, providing insight into volatility spillovers and shared market dynamics. Comparative analysis against alternative vector MEMs, including a fully parameterized version of the proposed model, demonstrates its superior or at least comparable performance across multiple evaluation criteria.

Purpose: This study quantifies volatility spillovers between WTI and INE, assessing their time-varying nature and INE’s sensitivity to external shocks. It evaluates INE’s integration with global markets and its role as a regional benchmark, offering insights into emerging energy markets’ resilience. Design/methodology/approach: This study examines the volatility spillover dynamics between WTI and Shanghai (INE) crude oil futures launched in 2018 to establish a regional benchmark and stabilize China’s currency. Using high-frequency data, a Multiplicative Error Model (MEM) quantifies spillovers, while a Markov-switching extension captures regime-dependent effects across the pre-pandemic, pandemic (2020–2021), and post-COVID phases. Robustness checks control for geopolitical risks (e.g. US–China tensions) and RMB exchange rate fluctuations. Findings: The results show unidirectional spillovers from WTI to INE, with no reverse effect, highlighting WTI’s dominance. Spillovers intensify during high-volatility regimes, peaking during the pandemic but declining post-COVID. INE demonstrates resilience to geopolitical and currency risks, reflecting its regional focus. The Markov-switching framework confirms regime-dependent spillovers, indicating a structural market shift. Originality/value: This study pioneers the use of MEM with Markov-switching to analyze WTI–INE spillovers, challenging static assumptions in prior research. It provides practical insights for Asian policymakers and investors, clarifying INE’s role as a regionally focused yet globally influenced contract. INE’s insulation from non-energy risks offers novel perspectives on the stability of emerging energy markets, informing risk management in poverty-dependent economies.

An open problem of Markov switching models is identifying the number of states, generally fixed a priori; it is impossible to apply classical tests due to the issue of the nuisance parameters present only under the alternative hypothesis. In this work, we show, by Monte Carlo simulations, that fuzzy clustering is able to reproduce the parametric state inference derived from the Hamilton filter and that the typical indices used in clustering to determine the number of groups can be used to identify the number of states in this framework. The procedure is very simple to apply, considering that it is performed independently of the data generating process and that the indicators we use are available in most statistical packages. Furthermore, the proposed approach appears to be sufficiently robust to perturbations in the data generating processes. A final application of real data completes the analysis.