From the 1 of 5 linked papers with an AI index.
5 papers
Spillover-Informed Network Architecture for Global Volatility Forecasting
Neha Gupta, Nishit Soni, Aditya Maheshwari
Spillover of volatility shocks across borders during turbulent periods makes accurate equity market volatility forecasts especially critical for risk management, derivatives pricin…
NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Process
Neha Gupta, Aditya Maheshwari
The paper introduces NeuroMem-FHP, a deep‑learning framework that uses LSTM and Transformer models to estimate the parameters of fractional Hawkes processes directly from event tim…
NeuroMemFPP: A recurrent neural approach for memory-aware parameter estimation in fractional Poisson process
Neha Gupta, Aditya Maheshwari
In this paper, we propose a recurrent neural network (RNN)-based framework for estimating the parameters of the fractional Poisson process (FPP), which models event arrivals with m…
Noncentral moderate deviations for time-changed multivariate Lévy processes with linear combinations of inverse stable subordinators
Neha Gupta, Claudio Macci
The term noncentral moderate deviations is used in the literature to mean a class of large deviation principles that, in some sense, fills the gap between the convergence in probab…
Geometrical subordinated Poisson processes and its extensions
Neha Gupta, Aditya Maheshwari, Dheeraj Goyal
In this paper, we study a generalized version of the Poisson-type process by time-changing it with the geometric counting process. Our work generalizes the work done by Meoli (2023…