6 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
In this paper, we propose deep learning based NeuroMem-FHP framework for estimating the parameters of the fractional Hawkes process (FHP), a self-exciting point process that captur…
NEXUS: A compact neural architecture for high-resolution spatiotemporal air quality forecasting in Delhi National Capital Region
Rampunit Kumar, Aditya Maheshwari
Urban air pollution in megacities poses critical public health challenges, particularly in Delhi National Capital Region (NCR) where severe degradation affects millions. We present…
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…
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…
Hawkes process with tempered Mittag-Leffler kernel
Neha Gupta, Aditya Maheshwari
In this paper, we propose an extension of the Hawkes process by incorporating a kernel based on the tempered Mittag-Leffler distribution. This is the generalization of the work pre…