Publications

List of Publications

Leon-Gonzalez, R., & Majoni, B. (2024). Exact Likelihood for Inverse Gamma Stochastic Volatility Models.  Journal of Time Series   Analysis  2024 Dec 2, doi.org/10.1111/jtsa.12795.

Leon-Gonzalez R, Majoni B. (2025) Approximate Factor Models with a Common Multiplicative Factor for Stochastic Volatility.    

    Studies in Nonlinear Dynamics & Econometrics. doi.org/10.1515/snde-2024-0103  

Majoni, B. (2021). VAT Withholding Tax and its Impact on VAT Compliance: Evidence from the Zimbabwe Revenue Authority. African Multidisciplinary Tax Journal, 2021(1), 228-243.  doi.org/10.47348/AMTJ/2021/i1a13   

Job Market Paper

Majoni, B. (2024) Generalized Common Inverse Gamma Stochastic Volatility Factor Models in Vector Autoregressions.

The most recent version of the paper can be accessed from here

Working Papers

Majoni, B. (2025).  Discrete Jumps with Time Varying Intensity in an Inverse Gamma Stochastic Volatility Model

Abstract: 

 This paper develops a novel Inverse Gamma Stochastic Volatility (IGSV) model that is driven by discrete jumps in volatility with time varying probabilities linked to observable macroeconomic news announcements. The model captures heavy tailed returns and allows for abrupt changes in volatility driven by economic events, a key feature in financial time series. We employ an efficient Bayesian estimation strategy through a Particle Marginal Metropolis-Hastings (PMMH) with an auxiliary particle filter. To address the well known mixing challenges associated with nonlinear, non-Gaussian state-space models, we implement adaptive proposal scalings that are parameter specific, resulting in substantial improvements in sampling efficiency. Monte Carlo simulation studies across multiple sample sizes and computational budgets, show that our method efficiently recovers true parameters. An empirical application on financial time series data then shows the model’s capability to capture heavy tails and volatility that is driven by macroeconomic news announcements.

Majoni, B. (2024). Integrating Deep Learning and Inverse Gamma Stochastic Volatility Models in Forecasting Climate-Induced Losses. Work in Progress.

Abstract: 

This study proposes a hybrid modeling framework that integrates deep learning techniques with inverse gamma stochastic volatility 

(SV) models to forecast disaster-related economic losses. The SV models explicitly capture time-varying volatility and uncertainty in 

loss-generating processes, while deep neural networks handle complex nonlinear pattern recognition and enhance predictive 

performance. Root Mean Squared Error (RMSE) and multivariate regression comparisons will be used to evaluate the model’s 

performance. The envisioned outcome is a forecasting system that is both accurate and interpretable, supporting improved climate 

adaptation strategies in vulnerable regions.

Work In Progress

1) Multi Server Hospital Phlebotomy Operations Research – with Takashi Tsuchiya (GRIPS) and Yoshifumi Uwamino (Keio University).

2) Asymmetric volatility and Climate Shocks: Modeling Non-Gaussian Dependencies.