arXiv subjects

Suparna Biswas

Publications and source records attributed to Suparna Biswas.

8 recordsLinked to original sources

arXiv2025

Portfolio Optimization for Index Tracking with Constraints on Downside Risk and Carbon Footprint

Historically, financial risk management has mostly addressed risk factors that arise from the financial environment. Climate risks present a novel and significant challenge for companies and financial markets. Investors aiming for avoidance of firms with high carbon footprints require suitable risk measures and portfolio management strategies. This paper presents the construction of decarbonized indices for tracking the S \& P-500 index of the U.S. stock market, as well as the Indian index NIFTY-50, employing two distinct methodologies and study their performances. These decarbonized indices optimize the portfolio weights by minimizing the mean-VaR and mean-ES and seek to reduce the risk of significant financial losses while still pursuing decarbonization goals. Investors can thereby find a balance between financial performance and environmental responsibilities. Ensuring transparency in the development of these indices will encourage the excluded and under-weighted asset companies to lower their carbon footprints through appropriate action plans. For long-term passive investors, these indices may present a more favourable option than green stocks.

q-fin.RM↗

arXiv2025

Estimation of Spectral Risk Measure for Left Truncated and Right Censored Data

Left truncated and right censored data are encountered frequently in insurance loss data due to deductibles and policy limits. Risk estimation is an important task in insurance as it is a necessary step for determining premiums under various policy terms. Spectral risk measures are inherently coherent and have the benefit of connecting the risk measure to the user's risk aversion. In this paper we study the estimation of spectral risk measure based on left truncated and right censored data. We propose a non parametric estimator of spectral risk measure using the product limit estimator and establish the asymptotic normality for our proposed estimator. We also develop an Edgeworth expansion of our proposed estimator. The bootstrap is employed to approximate the distribution of our proposed estimator and shown to be second order accurate. Monte Carlo studies are conducted to compare the proposed spectral risk measure estimator with the existing parametric and nonparametric estimators for left truncated and right censored data. Our observation reveal that the proposed estimator outperforms all the estimators for small values of $k$ (coefficient of absolute risk aversion) and for small sample sizes for i.i.d. case. In the dependent case, it demonstrates superior performance for small $k$ across all sample sizes. Finally, we estimate the exponential spectral risk measure for two data sets viz; the Norwegian fire claims and the French marine losses.

stat.ME↗

arXiv2024

Novel Epileptic Seizure Detection Techniques and their Empirical Analysis

An Electroencephalogram (EEG) is a non-invasive exam that records the brain's electrical activity. This is used to help diagnose conditions such as different brain problems. EEG signals are taken for epilepsy detection, and with Discrete Wavelet Transform (DWT) and machine learning classifier, they perform epilepsy detection. In Epilepsy seizure detection, machine learning classifiers and statistical features are mainly used. The hidden information in the EEG signal helps detect diseases affecting the brain. Sometimes it is complicated to identify the minimum changes in the EEG in the time and frequency domain's purpose. The DWT can give a suitable decomposition of the signals in different frequency bands and feature extraction. We use the tri-dimensionality reduction algorithm, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). Finally, features are selected by using a fusion rule and at the last step, three different classifiers, Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest-Neighbor (KNN) have been used individually for the classification. The proposed framework is tested on the Bonn dataset. The simulation results provide 100% accuracy for the LDA and NB combination outperforming accuracy with other classifiers combinations, including 89.17% for LDA and SVM, 80.42% for LDA and KNN, 89.92% for PCA and NB, 85.58% PCA and SVM, 80.42% PCA and KNN, 82.33% for ICA and NB, 90.42% for ICA and SVM, 90% for ICA and KNN. Also, the LDA and NB combination shows the sensitivity, specificity, accuracy, Precision, and Recall of 100%, 100%, 100%, 100%, and 100%. The results prove the effectiveness of this model.

cs.LG↗

arXiv2023

Kernel Based Estimation of Spectral Risk Measures

Spectral risk measures (SRMs) belong to the family of coherent risk measures. A natural estimator for the class of SRMs has the form of L-statistics. Various authors have studied and derived the asymptotic properties of the empirical estimator of SRM. We propose a kernel based estimator of SRM. We investigate the large sample properties of general L-statistics based on i.i.d and dependent observations and apply them to our estimator. We prove that it is strongly consistent and asymptotically normal. We compare the finite sample performance of our proposed kernel estimator with that of several existing estimators for different SRMs using Monte Carlo simulation. We observe that our proposed kernel estimator outperforms all the estimators. Based on our simulation study we have estimated the exponential SRM of four future indices-that is Nikkei 225, Dax, FTSE 100, and Hang Seng. We also discuss the use of SRM in setting initial margin requirements of clearinghouses. Finally we perform a backtesting exercise of SRM.

q-fin.ST↗

arXiv2021

An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers

This paper presents an epilepsy detection method based on discrete wavelet transform (DWT) and Machine learning classifiers. Here DWT has been used for feature extraction as it provides a better decomposition of the signals in different frequency bands. At first, DWT has been applied to the EEG signal to extract the detail and approximate coefficients or different sub-bands. After the extraction of the coefficients, principal component analysis (PCA) has been applied on different sub-bands and then a feature level fusion technique is used to extract the important features in low dimensional feature space. Three classifiers namely: Support Vector Machine (SVM) classifier, K-Nearest-Neighbor (KNN) classifier, and Naive Bayes (NB) Classifiers have been used in the proposed work for classifying the EEG signals. The proposed method is tested on Bonn databases and provides a maximum of 100% recognition accuracy for KNN, SVM, NB classifiers.

eess.SP↗

arXiv2020

Anisotropic strange star with Tolman-Kuchowicz metric under $f(R,T)$ gravity

In the current article, we study anisotropic spherically symmetric strange star under the background of $f(R,T)$ gravity using the metric potentials of Tolman-Kuchowicz type~\cite{Tolman1939,Kuchowicz1968} as $λ(r)=\ln(1+ar^2+br^4)$ and $ν(r)=Br^2+2\ln C$ which are free from singularity, satisfy stability criteria and also well behaved. We calculate the value of constants $a$, $b$, $B$ and $C$ using matching conditions and the observed values of the masses and radii of known samples. To describe the strange quark matter (SQM) distribution, here we have used the phenomenological MIT bag model equation of state (EOS) where the density profile ($ρ$) is related to the radial pressure ($p_r$) as $p_r(r)=\frac{1}{3}(ρ-4B_g)$. Here quark pressure is responsible for generation of bag constant $B_g$. Motivation behind this study lies in finding out a non-singular physically acceptable solution having various properties of strange stars. The model shows consistency with various energy conditions, TOV equation, Herrera's cracking condition and also with Harrison-Zel$'$dovich-Novikov's static stability criteria. Numerical values of EOS parameter and the adiabatic index also enhance the acceptability of our model.

gr-qc↗

arXiv2019

Relativistic strange stars in Tolman-Kuchowicz spacetime

In this article we propose a relativistic model of a static spherically symmetric anisotropic strange star with the help of Tolman-Kuchowicz (TK) metric potentials [Tolman, Phys. Rev. {\bf55}, 364 (1939) and Kuchowicz, Acta Phys. Pol. {\bf33}, 541 (1968)]. The form of the potentials are $λ(r)=\ln(1+ar^2+br^4)$ and $ν(r)=Br^2+2\ln C$ where $a$, $b$, $B$ and $C$ are constants which we have to evaluate using boundary conditions. We also consider the simplest form of the phenomenological MIT bag equation of state (EOS) to represent the strange quark matter (SQM) distribution inside the stellar system. Here, the radial pressure $p_r$ relates with the density profile $ρ$ as follows, $p_r(r)=\frac{1}{3}[ρ(r)-4B_g]$, where $B_g$ is the Bag constant. To check the physical acceptability and stability of the stellar system based on the obtained solutions, we have performed various physical tests. It is shown that the model satisfies all the stability criteria, including nonsingular nature of the density and pressure, implies stable nature. Here, the Bag constant for different strange star candidates are found to be $(68-70)$~MeV/{fm}$^3$ which satisfies all the acceptability criteria and remains in the experimental range.

gr-qc↗

arXiv2018

Strange stars in Krori-Barua space-time under f(R; T) gravity

In the present work, we study about highly dense compact stars which are made of quarks, specially strange quarks, adopting the Krori-Barua (KB)~\cite{Krori1975} metric in the framework of $f(R,T)$ gravity. The equation of state (EOS) of a strange star can be represented by the MIT bag model as $p_r(r)=\frac{1}{3}[ρ(r)-4B_g]$ where $B_g$ is the bag constant, arises due to the quark pressure. Main motive behind our study is to find out singularity free and physically acceptable solutions for different features of strange stars. Here we also investigate the effect of alternative gravity in the formation of strange stars. We find that our model is consistent with various energy conditions and also satisfies Herrera's cracking condition, TOV equation, static stability criteria of Harrison-Zel$'$dovich-Novikov etc. The value of the adiabatic indices as well as the EOS parameters re-establish the acceptability of our model. Here in detail we have studied specifically three different strange star candidates, viz. $PSRJ~1614~2230, Vela~X-1$ and $Cen~X-3$. As a whole, present model fulfils all the criteria for stability. Another fascinating point we have discussed is the value of the bag constant which lies in the range $(40-45)$~MeV/fm$^{3}$. This is quite smaller than the predicted range, i.e., $(55-75)$~MeV/fm$^{3}$ ~\cite{Farhi1984,Alcock1986}. The presence of the constant ($χ$), arises due to the coupling between matter and geometry, is responsible behind this reduction in $B_g$ value. For $χ=0$, we get the higher value for $B_g$ as the above mentioned predicted range.

gr-qc↗