I am a Lecturer at the University of Exeter.
I am a Lecturer at the University of Exeter.
Research:
Topics: Econometrics, Applied Econometrics and Computational Methods
Google Scholar and Research Gate
Publications
Federal Reserve Chairs and Monetary Regimes
Oxford Bulletin of Economics and Statistics
With: Yunus Aksoy and Zacharias Psaradakis
Abstract: We analyze the evolution of endogenously determined causal monetary policy regimes and compare these with Federal Reserve chairs' tenures between 965--2019. Taylor rules can broadly explain Federal Reserve's policies under the chairmanships of Miller, Volcker and Greenspan, whereas monetary feedback rules characterize the Bernanke and Yellen tenures. Our estimated monetary regimes generally align well with most Romer & Romer narrative monetary policy shock dates, and we find incidental evidence for December 1968, August 1978 and December 1988 shocks potentially leading to causal policy shifts.
On Testing for Bubbles During Hyperinflations
Studies in Nonlinear Dynamics & Econometrics
With: Zacharias Psaradakis, Martin Sola, and Patricio Yunis
Abstract: 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 in the drift, bubble-driven explosiveness, and volatility changes that may be fundamentals-driven and/or bubble-driven. The testing methodology is illustrated by applying it to data from hyperinflations in Argentina, Brazil, Germany and Poland.
European Sovereign Bond and Stock Market Granger Causality Dynamics - Preparing
With: Pedro Gomes and Zeynep Ozde Kurter
Abstract: We investigate the lead-lag relationship using weekly sovereign (government) bond yield changes and stock returns for seven European countries and how it changed during the period 2008-2018. Such lead-lag analysis are evaluated through vector autoregressions (VARs) on weekly data and with a method, based on Markov-switching Granger causality, that determines the direction of the Granger causality endogenously. We find that there are significant changes in the direction of the Granger causality between stock returns and changes in sovereign bond yields that vary across the countries. Additionally, global and specific economic events are linked to changes in Granger causality. Our results indicate that stock returns take a leading role in changes of sovereign bond yields in Germany, France, Spain, Italy and the United Kingdom, while changes of sovereign bond yields significantly lead stock returns in Portugal and Greece, where these countries faced very high instability during the European sovereign debt crisis period. No significant lead-lag relationship is found between stock and sovereign bond markets in some sub-periods for all countries.
Granger Causality Regimes and Volatility Dynamics: A Unified Markov-Switching Framework - Draft Soon
Abstract: I propose a Markov-switching vector autoregressive (MS-VAR) framework for analysing time-varying Granger causality in the presence of conditional heteroskedasticity. Existing approaches in the tradition of Psaradakis et al. (2005) embed both mean dynamics and covariance structure within the same regime indicators, creating a fundamental identification problem: volatility clusters and genuine causality regime changes are observationally similar from the perspective of the Hamilton filter, so the former can be spuriously attributed to the latter. I resolve this by separating the two channels, allowing regime switching only in the conditional mean while modelling second-moment dynamics through a state-invariant GARCH-DCC structure. I formalise the misspecification failure as a Kullback-Leibler proximity inversion and prove, under standard regularity conditions, that the fixed-covariance filter systematically misattributes GARCH volatility episodes to predictive-regime switches with positive stationary probability. Monte Carlo evidence with T = 300 confirms that the fixed-covariance estimator produces substantially worse regime classification across all four predictive states, with mean classification error roughly twice as large as the proposed estimator; differences are highly significant across all regimes. An empirical application to the trade-weighted U.S. dollar index and the Trade Policy Uncertainty (TPU) index of Baker et al. (2016), using monthly data from January 1985 to April 2026, illustrates the practical importance of the approach: ARCH-LM tests strongly reject homoskedasticity in the VAR residuals, and the MS-VAR with GARCH-DCC uncovers four distinct predictive regimes whose timing maps cleanly onto the major episodes of U.S. trade policy history. TPU-driven dollar dynamics dominate during both Trump administration trade-war episodes of 2017-2020 and 2025-2026, Dollar-to-TPU causality characterises the Plaza Accord era and the China shock of the early 2000s, and a persistent no-causality regime prevails during intervals of broadly stable multilateral trade rules.
Testing Time-Varying Granger Causality: A Markov-Switching VAR with TVTP and GARCH–DCC - Draft Soon
Abstract: I propose a bivariate Markov-switching VAR with time-varying transition probabilities (TVTP) and a state-invariant DCC--GARCH structure to test regime changes in Granger causality between crude oil returns and U.S. partisan conflict. Standard fixed-covariance Markov-switching estimators conflate volatility clustering with genuine causality regime switches; separating the conditional mean, where regime switching operates, from second-moment dynamics, handled by the DCC--GARCH block, delivers reliable inference on when and in which direction predictive relationships are active. I derive a joint Wald test for the TVTP slope parameters and characterise its conservative size at the boundary of the null. The TVTP slopes allow a direct horse-race between two competing narratives: under the GFC hypothesis, financial stress, measured by VIX, drives regime transitions in the causal channels; under the Shale Revolution hypothesis, those transitions are supply-driven and orthogonal to financial volatility. The estimates favour the Shale hypothesis. The two TVTP slopes governing oil-causality chain persistence are jointly insignificant, smoothed state probabilities identify a single persistent structural break around 2008, after which the no-causality regime dominates, and the DCC correlation trends smoothly toward zero without VIX-timed jumps. The one nuance is that extreme financial stress transiently reactivates the Oil→ →PCI channel, but these episodes last approximately two months and do not constitute a durable regime.
The Liquidity Effect Shifts. with Charisios Grivas
DSGE models and predictability in the real economy, with Stephen Wright and Rory Macqueen.