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20172026
most citedBayesian Testing Of Granger Causality In Functional Time Series

2 citations · 2 across the 3 of their papers we have counts for

collaborators

11 papers

stat.ME2026

Detection of Structural Distortions in Functional Time Series

Debanjana Datta, Rituparna Sen, Nalini Ravishanker

In the era of modern data science, the rapid proliferation of high-dimensional and functional datasets has fostered increasing interest in the investigation of paradigm shifts and…

q-fin.ST2022

Limiting Spectral Distribution of High-dimensional Hayashi-Yoshida Estimator of Integrated Covariance Matrix

Arnab Chakrabarti, Rituparna Sen

In this paper, the estimation of the Integrated Covariance matrix from high-frequency data, for high dimensional stock price process, is considered. The Hayashi-Yoshida covolatilit…

stat.ME2021★ 2 cited

Bayesian Testing Of Granger Causality In Functional Time Series

Rituparna Sen, Anandamayee Majumdar, Shubhangi Sikaria

We develop a multivariate functional autoregressive model (MFAR), which captures the cross-correlation among multiple functional time series and thus improves forecast accuracy. We…

q-fin.PM2019

Bayesian Filtering for Multi-period Mean-Variance Portfolio Selection

Shubhangi Sikaria, Rituparna Sen, Neelesh S. Upadhye

For a long investment time horizon, it is preferable to rebalance the portfolio weights at intermediate times. This necessitates a multi-period market model in which portfolio opti…

q-fin.ST2019

Copula estimation for nonsynchronous financial data

Arnab Chakrabarti, Rituparna Sen

Copula is a powerful tool to model multivariate data. We propose the modelling of intraday financial returns of multiple assets through copula. The problem originates due to the as…

q-fin.ST2019

Stylized facts of the Indian Stock Market

Rituparna Sen, Manavthi S

Historical daily data for eleven years of the fifty constituent stocks of the NIFTY index traded on the National Stock Exchange have been analyzed to check for the stylized facts i…