Indian Institute of Management Kashipur

About: Indian Institute of Management Kashipur is a education organization based out in Kashipur, India. It is known for research contribution in the topics: Supply chain & Volatility (finance). The organization has 102 authors who have published 203 publications receiving 1357 citations. The organization is also known as: IIM Kashipur.

Molecular docking-based interaction studies on imidazo[1,2-a] pyridine ethers and squaramides as anti-tubercular agents

Abstract: Development of new anti-tubercular agents is required in the wake of resistance to the existing and newly approved drugs through novel-validated targets like ATP synthase, etc. The major limitation of poor correlation between docking scores and biological activity by SBDD was overcome by a novel approach of quantitatively correlating the interactions of different amino acid residues present in the target protein structure with the activity. This approach well predicted the ATP synthase inhibitory activity of imidazo[1,2-a] pyridine ethers and squaramides (r = 0.84) in terms of Glu65b interactions. Hence, the models were developed on combined (r = 0.78), and training (r = 0.82) sets of 52, and 27 molecules, respectively. The training set model well predicted the diverse dataset (r = 0.84), test set (r = 0.755), and, external dataset (rext = 0.76). This model predicted three compounds from a focused library generated by incorporating the essential features of the ATP synthase inhibition with the pIC50 values in the range of 0.0508-0.1494 µM. Molecular dynamics simulation studies ascertain the stability of the protein structure and the docked poses of the ligands. The developed model(s) may be useful in the identification and optimization of novel compounds against TB.

Nexus Between Travel & Leisure Stocks and Uncertainties: An Extreme Risk Spillover Analysis

Abstract: The paper examines the presence of risk spillover to the US travel & leisure industry from the extreme changes in the uncertainties. More specifically, using a time-varying copula based conditional Value-at-Risk (CoVaR) framework, we evaluate the dynamic impact of the uncertainties on the extreme risk of the US travel & leisure industry by taking into consideration the uncertainty in economic policy, equity market conditions, and crude oil prices. The findings indicate a significant dynamic inverse relationship between the returns of travel & leisure industry and changes in uncertainty variables. The results further indicate the stronger sensitivity of the travel & leisure industry toward the uncertainties in financial market and crude oil prices. We find significant evidence of extreme upside and downside risk spillover to the US travel & leisure industry from excessive downward and upward changes in uncertainties respectively. The findings also demonstrate the asymmetric effect of extreme movements in uncertainty factors on the tail risk of the US travel & leisure industry. The findings of the study have ramifications for risk managers, portfolio managers and investors.

Estimating and Predicting Value-at-Risk in the presence of structural breaks: A Study based on unbiased extreme value volatility estimator

Abstract: We provide a framework based on the unbiased extreme value volatility estimator to predict long and short position value-at-risk (VaR). The given framework incorporates the impact of asymmetry, structural breaks and fat tails in volatility. We generate forecasts of both long and short position VaR and evaluate the VaR forecasting performance of the proposed framework using various backtesting approaches for both long and short positions and compare the results with that of various alternative models. Our findings indicate that the proposed framework outperforms the alternative models in predicting the long and the short position VaR. Our findings also indicate that the VaR forecasts based on the proposed framework provides the least total loss score for various long and short positions VaR and this supports the superior properties of the proposed framework in forecasting VaR more accurately. The study contributes by providing a framework to predict more accurate VaR measure based on the unbiased extreme value volatility estimator.