Market Risk Measures
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About the Lecture
The lecture Market Risk Measures by Edu Pristine is from the course Archiv - Market Risks. It contains the following chapters:
- Historical Simulation
- Identifying VaR Limit
- Parametic Estimation Approach
- Computing VaR
- Expected Shortfall
- Coherent Risk Measures
- Estimating Quentiles
- Estimating Standard Errors
- QQ Plots
- Questions
Author of lecture Market Risk Measures
Edu Pristine
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Excerpts from the accompanying material
... far the easiest method for estimating VaR. All historical observations are simply ordered from the smallest to the ...
... this means that 3% (i.e. 300) of the ordered observations will fall in the tail of the distribution. Therefore the 301st ordered ...
... a given confidence level denotes the point that separates the tail losses from the remaining distribution. VaR cutoff will be in the left tail of the returns distribution VaR. ... In practice, since the population std. dev and mean are not known, the researcher will use the sample mean and std. dev. ...
... a beginning period value of $100. The arithmetic returns follow a normal distribution with a mean of 12% and a std. dev. of 25%. Calculate VaR at the 99.5% confidence level Solution: VaR (0.5%) = ( -12% + 2.576*25%)*100 = $52.4 VaR (lognormal distribution) A diversified portfolio exhibits a normal distributed geometric ...
... investor the amount or magnitude of the actual loss. The expected shortfall (ES) provides an estimate of the tail loss by averaging the VaRs for increasing ...
... risk measure than either a VaR or ES. When modeling for ES, the weighing function is set to [1/(1- confidence level)] ...
... divided into nine equal probability mass slices at 10%, 20%, …. 90%. The 10% quantile relates to -1.2816, the 20% quantile relates to -0.8416, and the 90% quantile relates to 1.2816. ...
... A bin width is just the width of the intervals, sometimes called "bins", in a histogram. Computing the std. error is done by realizing that the square root of the variance of the quantile ...
... the distribution of our observations. A quantile-quantile (QQ) plot is a straightforward way to visualize if the empirical data fits the hypothesized theoretical distribution. The process graphs the quantiles at regular confidence intervals for the empirical distribution ...
... C) Expected shortfall satisfies all the properties of coherent risk measurement while VaR satisfies only for normal distributions. D) None of the above. B) Coherent risk measures ...
... The correct answer is: The risk surface for expected shortfall is concave because of the property ...
