Market Risk Measures
Dieser Vortrag ist nur für Mitglieder der Institution Experten verfügbar.
Du bist nicht eingeloggt. Du musst dich anmelden oder registrieren und Mitglied dieser Institution werden um Zugang zu erhalten. In unserer Hilfe findest du Informationen wie du Mitglied einer Institution werden kannst.
Über den Vortrag
Der Vortrag „Market Risk Measures“ von Edu Pristine ist Bestandteil des Kurses „Archiv - Market Risks“. Der Vortrag ist dabei in folgende Kapitel unterteilt:
- Historical Simulation
- Identifying VaR Limit
- Parametic Estimation Approach
- Computing VaR
- Expected Shortfall
- Coherent Risk Measures
- Estimating Quentiles
- Estimating Standard Errors
- QQ Plots
- Questions
Dozent des Vortrages Market Risk Measures
Edu Pristine
Trusted by Fortune 500 Companies and 10,000 Students from 40+ countries across the globe, EduPristine is one of the leading International Training providers for Finance Certifications like FRM®, CFA®, PRM®, Business Analytics, HR Analytics, Financial Modeling, Operational Risk Modeling etc. It was founded by industry professionals who have worked in the area of investment banking and private equity in organizations such as Goldman Sachs, Crisil - A Standard & Poors Company, Standard Chartered and Accenture.EduPristine has conducted corporate training for various leading corporations and colleges like JP Morgan, Bank of America, Ernst & Young, Accenture, HSBC, IIM C, NUS Singapore etc. EduPristine has conducted more than 500,000 man-hours of quality training in finance.
http://www.edupristine.com
Kundenrezensionen
5,0 von 5 Sternen
| 5 Sterne |
|
5 |
| 4 Sterne |
|
0 |
| 3 Sterne |
|
0 |
| 2 Sterne |
|
0 |
| 1 Stern |
|
0 |
Auszüge aus dem Begleitmaterial
... 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 ...
