activity
20242026
collaborators

9 papers

econ.EM2026

Distributional Granger Causality: Identification, Sequential Inference, and Adaptive Testing

Ayush Jha

Predictive dependence in time series need not be confined to the conditional mean. Outside the Gaussian setting, causal content may arise through conditional scale, tail behavior,…

econ.GN2026

Credit Capacity and the Propagation of Funding Shocks: Evidence from U.S. and Brazilian Financial Intermediaries

Ayush Jha, Ali Jaffri, Frank Fabozzi

Why do similar funding shocks generate sharply different credit outcomes across countries? We develop and estimate a dynamic structural model in which intermediary credit capacity…

q-fin.PR2026

Option Pricing under Stochastic Volatility and Jumps:A PIDE Framework with Empirical Evidence

Abigail Anokyewaa Mensah, Ayush Jha, Hongwei Mei +3

We develop a partial integro-differential equation (PIDE) framework for option pricing under joint stochastic volatility and jump dynamics, and evaluate its empirical content using…

econ.GN2025

Behavioral Probability Weighting and Portfolio Optimization under Semi-Heavy Tails

Ayush Jha, Abootaleb Shirvani, Ali M. Jaffri +2

This paper develops a unified framework that integrates behavioral distortions into rational portfolio optimization by extracting implied probability weighting functions (PWFs) fro…

econ.GN2025

Winners vs. Losers: Momentum-based Strategies with Intertemporal Choice for ESG Portfolios

Ayush Jha, Abootaleb Shirvani, Ali Jaffri +2

This paper introduces a state-dependent momentum framework that integrates ESG regime switching with tail-risk-aware reward-risk metrics. Using a dynamic programming approach and s…

econ.EM2025

Multivariate Affine GARCH with Heavy Tails: A Unified Framework for Portfolio Optimization and Option Valuation

Ayush Jha, Abootaleb Shirvani, Ali Jaffri +2

This paper develops and estimates a multivariate affine GARCH(1,1) model with Normal Inverse Gaussian innovations that captures time-varying volatility, heavy tails, and dynamic co…