Quantifying Volatility in VaR Model
Über den Vortrag
Der Vortrag „Quantifying Volatility in VaR Model“ von Edu Pristine ist Bestandteil des Kurses „Archiv - Valuation and Risk Models“. Der Vortrag ist dabei in folgende Kapitel unterteilt:
- Quantifying Volatility in VaR Model: Fat Tails
- Regime Switching
- Altenative Measure
- Cyclical Volatility of Financial Markets
- Estimation of Volatility
Dozent des Vortrages Quantifying Volatility in VaR Model
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
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Auszüge aus dem Begleitmaterial
... period of time. Market conditions may cause the mean and variances to change over the period of time, which leads to fat-tailed distributions. The fat-tailed unconditional distribution ...
... accurate measure of risk would be to measure the risk or volatility in these high and low risk areas separately, and then base our ...
... is informative. More information in recent past than ...
... day n as estimated at the end of day n-1. Variance estimate for next day is usually calculated as: Variance = average squared deviation from average return over last ‘n’ days ...
... of time. Market conditions may cause the mean and variances to change over the period of time, which leads to fat-tailed distributions. ...
... accurate measure of risk would be to measure the risk or volatility in these high and low risk areas separately, and then base our forecast ...
... changes is informative. More information in recent past than ...
... day n as estimated at the end of day n-1 variance estimate for next day is usually calculated as: • Variance = average squared deviation from average return ov er last ‘n’ days ...
