27 citations · 119 across the 13 of their papers we have counts for
13 papers
Analysis of optimal portfolio on finite and small-time horizons for a stochastic volatility model with multiple correlated assets
Minglian Lin, Indranil SenGupta
In this paper, we consider the portfolio optimization problem in a financial market where the underlying stochastic volatility model is driven by n-dimensional Brownian motions. At…
Some asymptotics for short maturity Asian options
Humayra Shoshi, Indranil SenGupta
Most of the existing methods for pricing Asian options are less efficient in the limit of small maturities and small volatilities. In this paper, we use the large deviations theory…
Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning
Xianfei Hui, Baiqing Sun, Indranil SenGupta +2
This paper models stochastic process of price time series of CSI 300 index in Chinese financial market, analyzes volatility characteristics of intraday high-frequency price data. I…
A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks
Shubham Ekapure, Nuruddin Jiruwala, Sohan Patnaik +1
In this paper, we implement a combination of technical analysis and machine/deep learning-based analysis to build a trend classification model. The goal of the paper is to apprehen…
Fractional Barndorff-Nielsen and Shephard model: applications in variance and volatility swaps, and hedging
Nicholas Salmon, Indranil SenGupta
In this paper, we introduce and analyze the fractional Barndorff-Nielsen and Shephard (BN-S) stochastic volatility model. The proposed model is based upon two desirable properties…
Analysis of optimal portfolio on finite and small time horizons for a stochastic volatility market model
Minglian Lin, Indranil SenGupta
In this paper, we consider the portfolio optimization problem in a financial market under a general utility function. Empirical results suggest that if a significant market fluctua…
Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters
Xianfei Hui, Baiqing Sun, Hui Jiang +1
In this paper we implement a combination of data-science and fuzzy theory to improve the classical Barndorff-Nielsen and Shephard model, and implement this to analyze the S&P 500 i…
Multi-asset Generalised Variance Swaps in Barndorff-Nielsen and Shephard model
Subhojit Biswas, Diganta Mukherjee, Indranil SenGupta
This paper proposes swaps on two important new measures of generalized variance, namely the maximum eigenvalue and trace of the covariance matrix of the assets involved. We price t…
First exit-time analysis for an approximate Barndorff-Nielsen and Shephard model with stationary self-decomposable variance process
Shantanu Awasthi, Indranil SenGupta
In this paper, an approximate version of the Barndorff-Nielsen and Shephard model, driven by a Brownian motion and a Lévy subordinator, is formulated. The first-exit time of the lo…
Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model
Humayra Shoshi, Indranil SenGupta
In this paper, a refined Barndorff-Nielsen and Shephard (BN-S) model is implemented to find an optimal hedging strategy for commodity markets. The refinement of the BN-S model is o…
Sequential hypothesis testing in machine learning, and crude oil price jump size detection
Michael Roberts, Indranil SenGupta
In this paper we present a sequential hypothesis test for the detection of general jump size distrubution. Infinitesimal generators for the corresponding log-likelihood ratios are…
Infinitesimal generators for two-dimensional Lévy process-driven hypothesis testing
Michael Roberts, Indranil SenGupta
In this paper, we present the testing of four hypotheses on two streams of observations that are driven by Lévy processes. This is applicable for sequential decision making on the…
Refinements of Barndorff-Nielsen and Shephard model: an analysis of crude oil price with machine learning
Indranil SenGupta, William Nganje, Erik Hanson
A commonly used stochastic model for derivative and commodity market analysis is the Barndorff-Nielsen and Shephard (BN-S) model. Though this model is very efficient and analytical…