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  1. Nicole Seaman

    P1.T2.21.3. Returns, volatility and non-normal distributions

    Learning objectives: Calculate, distinguish and convert between simple and continuously compounded returns. Define and distinguish between volatility, variance rate, and implied volatility. Describe how the first two moments may be insufficient to describe non-normal distributions. Questions...
  2. G

    Fat Tail Query

    Can you please help me with the answer of this question with explanation ? The problem of fat tails when measuing volatility is least likely in - 1 In an unstable distribution in a conditional distribution in a regime switching model in an unconditional distribution
  3. P

    Question about Binomial Trees

    I have 2 queries with respect to some concepts in FRM Part 1, Valuation & Risk Model GARP study book 2020/ 2021. 1. In the practice question no. 14.15 in chapter 14 - Binomial Trees. The question states that options maturity is 6 months and requires 2 step Binomial Tree. Volatility is 20% per...
  4. Nicole Seaman

    P2.T9.20.3. Factors: value, growth, inflation, and volatility

    Learning objectives: Describe the process of value investing and explain reasons why a value premium may exist. Explain how different macroeconomic risk factors, including economic growth, inflation, and volatility affect risk premiums and asset returns. Questions: 20.3..1. According to Andrew...
  5. S


    There was an example in GARP stating "Suppose that an asset price is $60 and that its daily volatility is 2%. This means that a one-standard devaition move in the asset price over one day would be 60*0.02 or 1.20%. If we assume taht the change in the asset price is normally distributed we can be...
  6. P

    Portfolio Systematic Risk, Breaking it down into factor % contributions

    I have a portfolio (p) of N equities, with lets say weights vector (m) at the start of the calculation period. Each equity has it's own set of factors (like corresponding country, industry index, etc.), some of the equities has the same factors. I am trying to breakdown the systematic risk into...
  7. M

    calculating volatility

    David, I am a bit concerned of voatility. This is given and that is fine, but lets put us for a minute in the shoes of a real trader. Ca you check the example and say if you think I am right or wrong computing volatility in this case of a currency pair. Of course this is only an example, I am...
  8. Nicole Seaman

    CFA Level 1 CFA: Measures of dispersion including volatility

    A previous video in this CFA playlist looked at classic measures of central tendency. This is also called the first moment of the distribution or the distributions the location where is the distribution centered. When we say that I think most of us think of the average or the mean, but we saw...
  9. S

    Normal IV vs. Log-normal IV

    Hi All, Anyone has any idea/thoughts around how we can convert Log-normal Implied Volatility(Black Scholes) to Normal Implied Volatility?
  10. Nicole Seaman

    YouTube T4-07: Binomial option pricing model: up/down jumps based on volatility

    Instead of arbitrarily selecting the up (u) and down (d) jumps in the binomial, we can "match them to a volatility input assumption, σ. The correct values are given by u = exp[σ*sqrt(Δt)] and d = 1/u; notice that the exponent is just apply the Square Root Rule (SRR) of scaling the per annum...
  11. Nicole Seaman

    YouTube T4-01: Three approaches to value at risk (VaR) and volatility

    The three approaches are 1. Parametric; aka, analytical; 2. Historical simulation; and 3. Monte Carlo simulation (MCS). The parametric approach assumes a clean function, the other two work with messy data. Historical simulation is betrayed by a histogram, MCS is betrayed by a random number...
  12. Nicole Seaman

    YouTube T2-26: Maximum likelihood estimation of GARCH parameters

    GARCH(1,1) is the popular approach to estimating volatility, but its disadvantage (compared to STDDEV or EWMA) is that you need to fit three parameters. Maximum likelihood estimation, MLE, is an immensely useful statistical approach that can be used to find "best fit" parameters. In this video...
  13. Nicole Seaman

    YouTube T2-23: Volatility: GARCH 1,1

    The GARCH(1,1) volatility estimate shares a similarity to EWMA volatility: both assign greater (lesser) weight to recent (distant) returns. But the GARCH(1,1) has an additional feature: it models a long-run (aka, unconditional) variance toward which the volatility series is pulled. David's XLS...
  14. Nicole Seaman

    YouTube T2-22: Volatility: Exponentially weighted moving average, EWMA

    The exponentially weighted moving average (EWMA) cures the key weakness of the common historical standard deviation by assigning greater weight to more recent returns and lessor weights to more distant (in the past) returns. Its key parameter is lambda, λ, which specifies the ratio of...
  15. Nicole Seaman

    YouTube T2-21: Volatility: standard deviation

    The simple, common approach to estimating volatility is historical standard deviation. Here is a thread about the decision to include/exclude the mean return: https://trtl.bz/2kLRK7z David's XLS is here: https://trtl.bz/2kOmHb6
  16. G

    Convexity and Volatility

    Hi everyone, It's my first time posting but I've been reading the forums since I enrolled for the FRM part I more than a year ago and I wanted, before anything else, to thank you all, particularly David, for all the help I've gotten from such a knowledgeable and supportive community while...
  17. Nicole Seaman

    YouTube T1-3 How to translate volatility over time

    We typically scale volatility with the square root rule, but keep in mind the key assumption (i.i.d. returns). We APOLOGIZE that the bottom-right corner is obstructed by the web camera. It contains Expected return = +10.0% such that the Absolute VaR = -10%*10/250 + 2.326*20%*sqrt(10/250); i.e...
  18. Nicole Seaman

    P1.T4.811. Two-step binomial models (Hull Ch.13)

    Learning objectives: Calculate the value of an American and a European call or put option using a one-step and two-step binomial model. Describe how volatility is captured in the binomial model. Questions: 811.1 Consider a six-month at-the-money (ATM) European call option on a...
  19. R

    Tuckman Chapter 9 : Term Structure of Volatility

    In "The Art of Term Structure Models : Drift" Tuckman mentions regarding term structure of volatility that: "The term structure of volatility in Model 1 is constant at 113 basis points." He also mentions that the Model 2 and the Ho-Lee, both do not change the term structure of volatility...
  20. K

    What is the Heath Jarrow Morton (HJM) model in interest rates?

    Hello All, I am interested in knowing what is HJM model ? How are they computed ? What are they used for and how are volatility surface derived ? How are they useed for valuations? Want to know mainly in brief and simple/layman language. Thanks