Modelling Value-at-Risk in investment banks: “Empirical evidence of JP Morgan, Merrill Lynch and Bank of America”

Abstract

The objective of paper is to assess the efficiency of financial model to capture increasing volatilities across asset class markets of the three investment banks. For which data will be collect to forecast the credit risk, and to know how well our standard tools forecast volatility, particularly during the turmoil that extend throughout the globe. Volatility prediction is a critical task in asset valuation and risk management for investors and financial intermediaries. The paper will focus on Value-at-Risk (VaR) which is a standard model that has been forecasted using both non-parametric and parametric approaches and then Backtesting procedure had been applied to achieve the both outcome. One is to detect the underlying credit risk which is associated with the market as well as portfolio risk, and other is to perceive model which provide more accurate forecasting.