We compare the estimation of the value at risk by peek over threshold method with that by traditional variance-covariance method.
We use this formula to generate that estimations of the value at risk based on the variance-covariance method and compare them with the estimation based on the peak over threshold method.
This paper aims at generating different scenarios of the value at risk estimation during financial crashes.
40 million and loss occurs in stock of un-ginned cotton the value at risk will include the value of cotton bales as well as the value of un-ginned cotton while the total sum insured will include the two policies of 40 million covering both the stocks but not the policy worth 10 million which covers stock of cotton bales because no loss occurs in the stock of cotton bales and this policy would stand as non-contributing.
The stocks/property which is excluded from the ambit of Insurance policy(s) coverage because of breach of warranty/condition for which the insurers assume no liability will not be included in the value at risk.
Similarly the quantum of stocks shown in the Bank stocks report will also be ignored while assessing the value at risk or amount of loss because the stocks report submitted to the Bank by the mortgagors cannot reflect the quantum of stocks at one particular time e.
Profit margins for different lines of business, therefore, should reflect the different amounts of capital they need, as measured by value at risk.
Lastly, value at risk can enable firms to rigorously examine alternative decisions in ways that would otherwise be difficult or impossible.
Likewise, value at risk may help a firm select an appropriate degree of financial leverage.
outline the Monte Carlo simulation approach to calculating value at risk (VAR)
Historical simulation is one of the three most common approaches used to calculate value at risk.
For employees who need an understanding of how to use and calculate Value at Risk
as well as the statistical tools required for calculating Value at Risk