Working Papers

Looking ahead, my research is increasingly focused on the intricate interplay between macroeconomic dynamics and financial market stability. I am particularly interested in how systemic risks propagate across interconnected global markets, and I am developing new Bayesian frameworks to better identify these spillover effects in high-dimensional settings. By leveraging advanced Monte Carlo methodologies, my goal is to bridge the gap between theoretical risk modeling and practical, macro-scale risk management. This includes addressing critical challenges such as tail-risk contagion and long-term asset-liability resilience. Ultimately, I aim to provide the robust econometric tools necessary to navigate the complexities of our current global financial landscape, ensuring that risk assessment remains both precise and policy-relevant.

Structural Macroeconomics with Daily Data: A Matrix-Free Mixed-Frequency VAR Approach （Link）

Financial markets reveal monetary-policy news at a daily or even intraday frequency, while output and prices are observed only monthly or quarterly. This paper argues that the mismatch is not only a measurement problem but an identification problem: aggregating financial variables to the macroeconomic frequency can blur immediate financial responses with delayed real adjustment and change the inferred transmission of monetary policy. I develop a daily Bayesian mixed-frequency VAR in which low-frequency macroeconomic aggregates are treated as latent daily processes observed through aggregation equations. To make the daily system feasible, the long lag polynomial is represented by a low-dimensional basis expansion, and the missing daily macroeconomic paths are drawn with a matrix-free sampler that avoids forming or factorising the large precision matrix. In an application to U.S. monetary policy combining quarterly output, monthly prices, and daily financial variables, the daily system delivers immediate financial responses and gradual declines in output and prices after a contractionary shock, without the price puzzle found in low-frequency recursive VARs. The results show that temporal aggregation can obscure structural monetary transmission, and that estimating the system on the daily clock changes the economic conclusions.

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.

U.S. Economy and Global Stock Markets: Insights from a Distributional Approach. Joint work with Ping Wu. (Link)

Financial markets are interconnected, with micro-currents propagating across global markets and shaping economic trends. This paper moves beyond traditional stock market indices to examine cross-sectional return distributions-15 in our empirical application, each representing a distinct global market. To facilitate this analysis, we develop a matrix functional VAR method with interpretable factors extracted from cross-sectional return distributions. Our approach extends the existing framework from modeling a single function to multiple functions, allowing for a richer representation of cross-sectional dependencies. By jointly modeling these distributions with U.S. macroeconomic indicators, we uncover the predictive power of financial market in forecasting macro-economic dynamics. Our findings reveal that U.S. contractionary monetary policy not only lowers global stock returns, as traditionally understood, but also dampens cross-sectional return kurtosis, highlighting an overlooked policy transmission. This framework enables conditional forecasting, equipping policymakers with a flexible tool to assess macro-financial linkages under different economic scenarios.