Working Papers

Inflation Target at Risk: A Time-Varying Parameter Distributional Regression. Joint work with Yunyun Wang and Tatsushi Oka.(Link)

Inflation exhibits state-dependent, skewed, and fat-tailed dynamics that make risk a central concern for monetary policy. Accordingly, inflation risks are distributional and cannot be fully captured by mean-based models. We propose a flexible time-varying parameter distributional regression model that estimates the full conditional distribution of inflation, allowing macroeconomic drivers to have nonlinear and asymmetric effects across the distribution. Applied to U.S. inflation, the model captures major shifts in tail-risk probabilities. Analysis of risk drivers shows that deflationary pressures arise primarily from demand-side weakness and inflation persistence, whereas upside risks are driven mainly by supply-side shocks, particularly energy price inflation. Examining the impact of key drivers further reveals that the unemployment-inflation relationship weakens in the distributional tails. Energy price shocks, by contrast, have little effect on deflation risk but exhibit strongly time-varying and asymmetric effects on high-inflation risk.

A Parsimonious and Interpretable Factor model of Implied Volatility Surface. Joint work with Yanhong Wan and Wei Wei. (Link)

The implied volatility surface (IVS) is a key gauge of market uncertainty with implications for derivative pricing, risk management, and macro-financial analysis. This paper introduces a parsimonious factor-based model that decomposes the IVS into five components: level, tail asymmetry, term structure, curvature, and deviation. By incorporating the asymptotic properties of the IVS, the proposed framework offers a structurally grounded representation with enhanced interpretability. Empirical application to S&P 500 European options demonstrates that our model (i) captures the global geometry of the IVS; (ii) outperforms traditional polynomial-based benchmarks in both surface fitting and forecasting; and (iii) provides superior predictive power for both realized variance and macroeconomic conditions. The factors are particularly informative during episodes of heightened uncertainty, including the 2008 Global Financial Crisis and the 2020 COVID-19 shock.

Fast Posterior Sampling in Tightly Identifed SVARs Using ‘Soft’ Sign Restrictions. Joint work with Matthew Read. (Link)

We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on ‘soft’ sign restrictions. This step draws from a target density that smoothly penalises parameter values violating the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws from the desired distribution conditional on the ‘hard’ sign restrictions. Relative to standard accept-reject sampling, the method substantially improves computational efficiency when identification is ‘tight’. It can also greatly reduce the computational burden of implementing prior-robust Bayesian methods. We illustrate the broad applicability of the approach in a model of the global oil market identified using a rich set of sign, elasticity and narrative restrictions.