arXiv subjects

Giacomo Morelli

Publications and source records attributed to Giacomo Morelli.

4 recordsLinked to original sources

arXiv2026

Transfer Learning (Il)liquidity

The estimation of the Risk Neutral Density (RND) implicit in option prices is challenging, especially in illiquid markets. We introduce the Deep Log-Sum-Exp Neural Network, an architecture that leverages Deep and Transfer learning to address RND estimation in the presence of irregular and illiquid strikes. We prove key statistical properties of the model and the consistency of the estimator. We illustrate the benefits of transfer learning to improve the estimation of the RND in severe illiquidity conditions through Monte Carlo simulations, and we test it empirically on SPX data, comparing it with popular estimation methods. Overall, our framework shows recovery of the RND in conditions of extreme illiquidity with as few as three option quotes.

q-fin.MF↗

arXiv2025

Dynamic Conditional SKEPTIC

We introduce the Dynamic Conditional SKEPTIC (DCS), a semiparametric approach for efficiently and robustly estimating time-varying correlations in multivariate models. We exploit nonparametric rank-based statistics, namely Spearman's rho and Kendall's tau, to estimate the unknown correlation matrix and discuss the stationarity, beta- and rho- mixing conditions of the model. We illustrate the methodology by estimating the time-varying conditional correlation matrix of the stocks included in the S&P100 and S&P500 during the period from 02/01/2013 to 23/01/2025. The results show that DCS improves diagnostic checks compared to the classical Dynamic Conditional Correlation (DCC) models, providing uncorrelated and normally distributed residuals. A risk management application shows that global minimum variance portfolios estimated using the DCS model exhibit lower turnover than those based on the DCC and DCC-NL models, while also achieving higher Sharpe ratios for portfolios constructed from S&P 100 constituents.

stat.AP↗

arXiv2025

Robust Semiparametric Graphical Models with Skew-Elliptical Distributions

We propose semiparametric estimators, called elliptical skew-(S)KEPTIC, for efficiently and robustly estimating non-Gaussian graphical models. Our approach extends the semiparametric elliptical framework to the meta skew-elliptical family, which accommodates skewness. Theoretically, we show that the elliptical skew-(S)KEPTIC estimators achieve robust convergence rates for both graph recovery and parameter estimation. Through numerical simulations, we illustrate the reliable graph recovery performance of the elliptical skew-(S)KEPTIC estimators. Finally, we apply the new method to the daily log-returns of the stocks in the S\&P 500 index and obtain a sparser graph than with Gaussian copula graphical models.

stat.ME↗

arXiv2025

Propensity score with factor loadings: the effect of the Paris Agreement

Factor models for longitudinal data, where policy adoption is unconfounded with respect to a low-dimensional set of latent factor loadings, have become increasingly popular for causal inference. Most existing approaches, however, rely on a causal finite-sample approach or computationally intensive methods, limiting their applicability and external validity. In this paper, we propose a novel causal inference method for panel data based on inverse propensity score weighting where the propensity score is a function of latent factor loadings within a framework of causal inference from super-population. The approach relaxes the traditional restrictive assumptions of causal panel methods, while offering advantages in terms of causal interpretability, policy relevance, and computational efficiency. Under standard assumptions, we outline a three-step estimation procedure for the ATT and derive its large-sample properties using Mestimation theory. We apply the method to assess the causal effect of the Paris Agreement, a policy aimed at fostering the transition to a low-carbon economy, on European stock returns. Our empirical results suggest a statistically significant and negative short-run effect on the stock returns of firms that issued green bonds.

econ.EM↗