Working Papers
Price Discovery under Regimes: An Empirical Investigation of CDS and Corporate Bond Spreads (with F. Melloni and M. Guidolin). R&R at Review of Quantitative Finance and Accounting. [SSRN].
Option-Implied Network Measures of Tail Contagion and Stock Return Predictability. [SSRN].
Sports Betting Legalization and Mutual Fund Managers Risk-Taking (with R. Brownen-Trinh and A. Orujov). [SSRN]. New version coming soon.
The Growth and Consequences of Index Investing (with A. F. Allard, M. Farkas, S. Rubio, and I. Tonks). [SSRN].
Ongoing Projects
Measures of Fragility for Tail Risk Models (with A. F. Allard, C. Chmielowska, and M. Guidolin). Working paper coming soon.
A fragile tail risk model is one that can only forecast tail risks accurately when backtested under a specific selection of the possible choices required by the backtest. In the literature, it is not yet clear how model fragility should be measured and quantified. We develop several novel measures of model fragility. Firstly, we propose indices– the success rate (SR), local fragility (LF), relative local fragility (RLF), and the relative area (RA)– that exploit comparative algorithms based on a statistical test that can either reject or not the null that a candidate model is no worse than an alternative according to some loss function. However, because of their limitations, we also propose measures that have an absolute nature and capture intrinsic features of forecasting models in terms of the stability of the loss function over the backtesting parameter space: ruggedness (RG), mean semi-elasticity (ME), and integrated semi-elastic radius (IE). The use and relevance of all of these fragility measures are shown with reference to an application of VaR and ES estimation on daily S&P 500 index returns.
Boosting AI with HI (Human Intelligence): An Application to Strategic Asset Allocation (with M. Guidolin, and R. Harris). Working paper coming soon.
Can Large Language Models (LLMs) such as ChatGPT serve as effective tools for strategic asset allocation? This project investigates whether LLM-based portfolio construction can match or outperform traditional quantitative approaches such as mean-variance optimization, minimum-variance portfolios, and the Black-Litterman model. We propose a hybrid framework that combines the qualitative screening capabilities of LLMs with rigorous quantitative optimization techniques. Using an extensive out-of-sample analysis across eight major global equity indices (DJIA, S&P 500, CAC 40, DAX 40, FTSE 100, FTSE MIB, IBEX 35, and TSX), we examine whether LLM-generated portfolios add economic value relative to traditional benchmarks. Preliminary results from over 4,000 API simulations per index suggest that while pure LLM portfolios generally underperform classical optimization methods, hybrid approaches that leverage LLMs for stock screening and human-guided quantitative models for weight determination show promise. The research will deliver practical guidelines for pension funds and asset managers on the effective integration of AI tools in strategic asset allocation decisions.
This project has been awarded funding by the Kroner Center for Financial Research (KCFR), University of California, San Diego.
Permanent Working Papers and Policy Reports