6 citations · 7 across the 7 of their papers we have counts for
7 papers
Precision versus Shrinkage: A Comparative Analysis of Covariance Estimation Methods for Portfolio Allocation
Sumanjay Dutta, Shashi Jain
In this paper, we perform a comprehensive study of different covariance and precision matrix estimation methods in the context of minimum variance portfolio allocation. The set of…
A neural network based model for multi-dimensional nonlinear Hawkes processes
Sobin Joseph, Shashi Jain
This paper introduces the Neural Network for Nonlinear Hawkes processes (NNNH), a non-parametric method based on neural networks to fit nonlinear Hawkes processes. Our method is su…
Prediction of drag components on rough surfaces using effective models
Sahaj Jain, Y. Sudhakar
Owing to the multiscale nature and the consequent high computational cost associated with simulations of flows over rough surfaces, effective models are being developed as a practi…
A semi-static replication approach to efficient hedging and pricing of callable IR derivatives
Jori Hoencamp, Shashi Jain, Drona Kandhai
We present a semi-static hedging algorithm for callable interest rate derivatives under an affine, multi-factor term-structure model. With a traditional dynamic hedge, the replicat…
Method of lines for valuation and sensitivities of Bermudan options
Purba Banerjee, Vasudeva Murthy, Shashi Jain
In this paper, we present a computationally efficient technique based on the \emph{Method of Lines} (MOL) for the approximation of the Bermudan option values via the associated par…
Shallow Neural Hawkes: Non-parametric kernel estimation for Hawkes processes
Sobin Joseph, Lekhapriya Dheeraj Kashyap, Shashi Jain
Multi-dimensional Hawkes process (MHP) is a class of self and mutually exciting point processes that find wide range of applications -- from prediction of earthquakes to modelling…
Neural network for pricing and universal static hedging of contingent claims
Vikranth Lokeshwar, Vikram Bhardawaj, Shashi Jain
We present here a regress later based Monte Carlo approach that uses neural networks for pricing high-dimensional contingent claims. The choice of specific architecture of the neur…