Variance Covariance
Über den Vortrag
Der Vortrag „Variance Covariance“ von Edu Pristine ist Bestandteil des Kurses „Archiv - Quantitative Analysis“. Der Vortrag ist dabei in folgende Kapitel unterteilt:
- Variance & Standard deviation
- Covariance and correlation
- Question
- Some Properties of Variance
- Skewness
- Kurtosis
- Coskewness and Cokurtosis
- BLUE
- Question
- Question - FRM Exam 2009
Dozent des Vortrages Variance Covariance
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.
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Auszüge aus dem Begleitmaterial
... Square root of the Variance is more convenient to use, as it has the same units as the original variable X ...
... EduPristine For Quantitative Analysis-I (Confidential) Correlation coefficient always lies in the range of +1 to -1. A correlation of 1 means that the two variables always move in the same direction A correlation of -1 ...
... what is the Variance of X given Variance[Y] = 100, Variance [4X ...
... 9*Var[Y] + 2*4*(-3)*Var[X]^(1/2)* Var[Y]^(1/2)*correlation[X,Y] -Solve for Var[X] = ...
... that the distribution has a long left tail, which indicates a high probability of observing large negative values.If this represents the distribution of ...
... observations in the tail will have a large weight and hence create large kurtosis. Such a distribution is called leptokurtic, or fat tailed. ...
... for these two variables at different point of time most risk models ignore the effects of coskewness and cokurtosis. The main reason for this is that as the number of variables increases, the number of coskewness ...
... to the parameter you are trying to estimate. An unbiased parameter is also efficient if its sampling distribution has minimum variance. ...
... deviations from the mean. -2 = [(12-5.67) 2 + (5-5.67) 2 + (-7-5.67) 2 + (11-5.67) 2 + (2-5.67) 2 + ...
... 0.5. The variance of 2X + 3Y is: A.13 B.29 ...
... - Y) = Var(X) + Var(Y) -2*Cov(x,y) -Var(cX) = c^2 * Var(X) -Cov (ax,by) = abCov(x,y) ...
... 4 million in stock P. You are considering a strategy of shifting USD 1 million into stock Q and keeping USD 3 million in stock P. What percentage of risk, as measured ...
... a variable to exceed a specified extreme value ‘X’ which is greater than the mean assuming the distributions all. ...
... Therefore, the probability of exceeding a specified extreme value will be higher. -B. Incorrect. Since answer A. has a lower kurtosis, a distribution with a kurtosis of 8 will necessarily produce a larger probability in the tails. © EduPristine For Quantitative ...
