Sabyasachi Guharay

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In this article we develop a novel method for accurately estimating the Value-at-Risk (VaR) in the context of modern Operational Risk Management (ORM). We develop a method called Data Partition of Frequency and Severity (DPFS) which more accurately computes the VaR in a modern ORM context. The DPFS involves using clustering analysis to partition the(More)
In this article we show an application of data fusion techniques to the field of quantitative risk management. Specifically, we study a synthetic dataset which represents a typical mid-level financial institution's operational risk loss as defined by the Basel Committee on Banking Supervision (BCBS) report. We compute the economic capital needed for a(More)
This article uses Bayesian simulation algorithms in a checkerboard matrix framework in order to study whether competition can be statistically detected among living species. We study an exhaustive set of binary co-occurrence matrices for habitation data. We categorize the living species into five distinct groups: (1) Mammals; (2) Plants; (3) Birds; (4)(More)
An analog correlator has been constructed using a delay unit which is realized by simulation of the sixth-order Pade approximated result of exp(-jomegatau) where j= radical-1, omega the angular frequency of the signals of concern, and tau the delay in seconds. Several sinusoidal signals and a noise spectrum have been autocorrelated with this correlator;(More)
Statistical analysis of financial time series is studied. We use wavelet analysis to study signal to noise ratios along with auto-correlation function to study correlation length for time series data of daily stock prices for specific sectors of the market. We study the ”high beta” stocks versus the ”low beta” stocks. We sample ten companies from both of(More)
This is the second in a series of papers about the dynamics of the forced van der Pol oscillator [J. Guckenheimer, K. Hoffman, and W. Weckesser, SIAM J. Appl. Dyn. Syst., 2 (2003), pp. 1–35]. The first paper described the reduced system, a two dimensional flow with jumps that reflect fast trajectory segments in this vector field with two time scales. This(More)
Predictive analytics and data fusion techniques are being regularly used for analysis in Quantitative Risk Management (QRM). The primary risk metric of interest, Value-at-Risk (VaR), has always been difficult to robustly estimate for different data types. The classical Monte Carlo simulation (MCS) approach (denoted henceforth as classical approach) assumes(More)
Value-at-Risk (VaR) is a well-accepted risk metric in modern quantitative risk management (QRM). The classical Monte Carlo simulation (MCS) approach, denoted henceforth as the classical approach, assumes the independence of loss severity and loss frequency. In practice, this assumption does not always hold true. Through mathematical analyses, we show that(More)
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