American Research Journal of Business and Management

Michael Jacobs, Jr.1 

It is well known in the term structure literature that the normal and log-normal distribution models are not consistent across high and low interest rate regimes, which creates challenges for building models to measure and manage interest rate risk. In this paper we outline a tractable approach to solving this problem based upon the theory of Black (1995), which utilizes an “inverse-call transformation” methodology to derive “shadow rates” as underlying drivers of observed yields, that have been shown in the literature to be more appropriate than the standard models for the purposes forecasting and risk management with respect to interest rate sensitive portfolios. We extend the literature by calibrating optimal shadow rates, modeling them in a multivariate dynamic conditional correlation (DCC) framework and applying the results to an interest rate risk management exercise, thereby providing a useful risk management tool for both banks and their prudential supervisors. We conclude that our mixture of normal and log-normal distributions model, utilizing optimally calibrated shadow rates as drivers, produces the most reasonable set of simulated 1 year rate distributions from the fitted DCC model as compared to the normal or log-normal model. 

Dr. V.Suganya

Services are the main ingredient in purchase or utilisation of goods. More so, organisations involved in services have to gather up their resources in providing with the best possible output / service to the society. Electricity services in India are a publicly owned institution, which have been spreading its wings form the urban down to the rural areas. Though it has been known for its credibility, it has certain bottlenecks, which hinder its effective functioning. The current paper discusses these bottlenecks and also provides a way out. The paper is conceptual in nature. 

Alexander Franco, Ph.D.