Research Overview

My research program develops a coherent econometric framework that links multilevel factor structures, fractional integration, long-memory dynamics, and stress-scenario propagation across macroeconomic, financial, energy, and climate-related systems.

Across applications (from growth-in-stress densities to electricity markets, exchange-rate volatility, and long economic histories), most of my research papers uncover persistent common components, heterogeneous cross-sectional structures, and non-negligible tail risks that shape macroeconomic adjustment and policy design.

You can find related publications in the Publications and Working Papers sections.

Click each section to explore.

Scenario-Based Predictive Densities: I develop econometric frameworks that generate full predictive densities under both baseline and stressed configurations of the latent factors driving macro-financial systems. These methods are applicable to growth, inflation, volatility, energy markets, and other macroeconomic variables.

  • Construct severe yet plausible scenarios from the joint distribution of high-dimensional factor models
  • Produce full predictive densities instead of point forecasts or isolated quantiles
  • Generalize Growth-in-Stress (GiS) to other macro-financial variables
  • Quantify how shocks—e.g., COVID-19—alter the tails of predictive distributions

Technical contribution: a multidimensional scenario-consistent methodology that extends traditional risk tools such as GaR by exploiting multilevel dynamic factor structures and tail-sensitive predictive environments.

Related papers:

ML-DFM for Financial Econometrics (High-Frequency Volatility and Credit Default Swaps)

  • High-frequency exchange-rate volatility: global-in-time plus intermittent hourly factors, with COVID-19 inducing a permanent shift in the global factor and intermittent factors reverting to their mean within about a year
  • Credit Default Swaps: CDS comovement driven by global, local, and semi-pervasive factors

Dynamic Multi-Level Factor Models with Stochastic Trends

  • Allows long memory + short memory + stochastic trends
  • Prewhitening via cross-sectional and local averages
  • Factor estimators are asymptotically normal
  • Nord Pool application shows fractional cointegration

Factor Dynamics with Time-Varying Loadings

  • Wavelet-based GLS estimation for time-varying loadings
  • Handles stationary + nonstationary factors
  • Explains cross-hour dependence in Nord Pool electricity markets

This links time-varying loadings with long-memory innovations.

Fractionally Integrated Panel Models

Proposes FI panels with multilevel dependence: top-level and block-level factors.

Periodic FI Models for Power Markets

Electricity prices exhibit fractional cointegration and strong diurnal periodicity.

Technical contribution: a unified asymptotic framework combining long memory and multilevel factor structures.

Related papers:

This line of work studies how large shocks and structural breaks—financial crashes, pandemics, energy regime shifts—reshape long-run growth trajectories, combining long economic histories with change-point and structural-break econometrics.

EPC–GDP Causality under Structural Breaks

Shows heterogeneous causal relationships across the Americas, depending on crisis episodes.

Related papers: