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  1. Nicole Seaman

    P2.T10.20.6. Climate change and financial risk

    Learning objectives: Discuss the history of climate change-related risks for the financial sector including the Paris Agreement (2015) and distinguish the significance of Article 2.1 c as it pertains to the financial system. Distinguish the causes of potential mispricing of climate change risk...
  2. Nicole Seaman

    P1.T2.20.13. Coskewness and cokurtosis

    Learning objectives: Use sample data to estimate quantiles, including the median. Estimate the mean of two variables and apply the CLT. Estimate the covariance and correlation between two random variables. Explain how coskewness and cokurtosis are related to skewness and kurtosis. Questions...
  3. Nicole Seaman

    P1.T2.20.12. BLUE estimators, Law of large numbers (LLN), and central limit theorem (CLT)

    Learning objectives: Explain what is meant by the statement that the mean estimator is BLUE. Describe the consistency of an estimator and explain the usefulness of this concept. Explain how the Law of Large Numbers (LLN) and Central Limit Theorem (CLT) apply to the sample mean. Estimate and...
  4. V

    Kurtosis with stochastic volatility

    I am struggling to prove that for a normally distributed loss RV introducing stochastic volatility (\sigma_1 with probability 0.5 and \sigma_2 with probability 0.5) would make kurtosis bigger than 3 (fat tails). Can someone help? Thank you
  5. Nicole Seaman

    YouTube T2-7 Kurtosis of a probability distribution

    Kurtosis is the standardized fourth central moment and is a measure of tail density; e.g., heavy or fat-tails. Heavy-tailedness also tends to correspond to high peakedness. Excess kurtosis (aka, leptokurtosis) is given by (kurtosis-3). We subtract three because the normal distribution has...
  6. Nicole Seaman

    P1.T2.712. Skew, kurtosis, coskew and cokurtosis (Miller, Chapter 3)

    Learning objectives: Describe the four central moments of a statistical variable or distribution: mean, variance, skewness, and kurtosis. Interpret the skewness and kurtosis of a statistical distribution, and interpret the concepts of coskewness and cokurtosis. Describe and interpret the best...
  7. W

    Skewness and kurtosis

    Hi David, In the 2011 quant part a video you define the numerator of skewness as E[(Y-mu)^3] and then as mu^3 (slide 14). The same goes for kurtosis. Is there an error or am I interpreting something incorrectly? Any explanation would be gretly appreciated. Thanks, Mike
  8. P

    Gujarati-Skewness/Kurtois Calc formula & clalrification

    Hi David, Could you please provide some guidance on the following: 1) Sample Skewness and Kurtosis Formulas I have seen 2 differnt versions,can you please clarify which is correct: Version#1: Sample Skewness Sum of Third moment about the mean / cube of Std Deviation * 1/ N...
  9. S

    Kurtosis and skewness in investment terms

    Hi, David. I have a question regarding kurtosis and skewness. In investment terms skewness is supposed to mean “bias toward positive or negative return”. Kurtosis captures the tendency of the price of this investment to jump either direction. In FRM, I’ve encountered EVT, and its objective...