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  1. jairamjana

    Options Strategy Resource

    This is a resource I made by myself for a quick summary on basic Options Strategy.. I made this 3 years back or so.. So it's a bit ancient for me.. https://www.dropbox.com/s/q56ryasdnfulv27/option_pay_off.pdf?dl=0 Hope its useful. Jairam
  2. jairamjana

    Copula Free Resource

    I used a filesharing site .. Thanks.. I added the new link.. I couldn't directly upload to the forum as file size was so large.. And yes hopefully I will learn Copulas both intuitively and mathematically one day.. I was told that dependence on copulas for modelling default risk (I have not...
  3. jairamjana

    Copula Free Resource

    Gummystuff http://www.financialwisdomforum.org/gummy-stuff/ has given a comprehensive walkthrough on Copula for the layman. I have formatted the same in PDF.. This is free for all content so thank this man gummystuff if you like it..:) PDF Link ----> http://www.filedropper.com/copulas Hope...
  4. jairamjana

    Conditional Expectation of MA(1)

    Thank you so much for that clarity @ami44.. So linearity is what is important.. So there you go @brian.field it works both ways...
  5. jairamjana

    Conditional Expectation of MA(1)

    Just for further input... I followed Wikipedia.. https://en.wikipedia.org/wiki/Conditional_variance ...
  6. jairamjana

    Conditional Expectation of MA(1)

    Var(aX+bY) is an unconditional form right.. So that it will become a^2Var(x) + b^2Var(Y) .. I am just saying I am unsure if it holds for conditional VAR.. Await further feedback from others...
  7. jairamjana

    Conditional Expectation of MA(1)

    I will give my opinion... That way of splitting the components only works if it is a expectation operator.. Conditional Var has to first be converted into a expectation operator form... I may not be sure of your way.. @David Harper CFA FRM.. If does require clarification.. See page 5...
  8. jairamjana

    Conditional Expectation of MA(1)

    P.S:i edited the equation just now.. Forgot the power 2 ...
  9. jairamjana

    Conditional Expectation of MA(1)

    That doesn't work var(Yt |omega_t-1) = E[(Yt - E(Yt | omega_t-1)^2 | omega_t-1 ]= E[(Yt- theta*(e_t-1))^2| omega_t-1] = E[(e_t)^2 | omega_t-1] = sigma^2
  10. jairamjana

    Conditional Expectation of MA(1)

    Whether variance is e_t-1 or e_t it's a constant sigma^2.. Yes I made a typo for the et white noise part it's 0 and sigma ^2
  11. jairamjana

    Conditional Expectation of MA(1)

    Just remember that it's same for AR(1).. We always assume e(t) is iid WN(1,sigma^2).. Wold decomposition theory should be reread multiple times..and also general linear process
  12. jairamjana

    Conditional Expectation of MA(1)

    The mean of the disturbance term e(t) conditional on information set omega(t-1) ={e(t-1),e(t-2),e(t-3)......} will be the unconditional mean of e(t).. So E[e(t) | omega(t-1)] will be equal to E[e(t)]... The disturbance term being white noise is uncorrelated with past disturbance and hence e(t) =...
  13. jairamjana

    Hybrid Approach Weights Formula

    Thank you so much for your time @David Harper CFA FRM ... I learnt a lot atleast about WHS method.. Just a thought.. Have you heard of Oracles Crystal Ball ERM ??
  14. jairamjana

    GARCH(1,1) vs EWMA for Forecasting Volatility

    I have heard great things about her books on Market Risk.. I am just a student so its a bit on the higher side to buy the set.. When I actually enter this field I will look to buy her book.. I hear she also has given spreadsheet case studies for her references...I am sure I will find it all...
  15. jairamjana

    GARCH(1,1) vs EWMA for Forecasting Volatility

    So I link this video which explains GARCH(1,1) as a measure to forecast future volatility. Now we know EWMA is a special case of GARCH which sums alpha and beta equal to 1 and therefore ignores any impact on long run variance, implying that variance is not mean reverting.. Again when we...
  16. jairamjana

    Hypothesis Testing

    I would say that H0 should contain an equality sign or variants of the equality sign only for convenience sake... For e.g in a typical least squares regression, take the case of the explanatory variable x(i) which has coefficients Beta(1) .. We say that when Beta(1) = 0 , then the so called...
  17. jairamjana

    Hybrid Approach Weights Formula

    @David Harper CFA FRM Thank you for showing me a alternate approach.. Of course I am embarrased I took a log 10 by mistake.. My intention was LN and that made the answer vary a lot.. And also I understand Linda Allen approach we average the worst returns rank by rank and probably that enhanced...
  18. jairamjana

    Var (X) = E[(X - mean)2] = E(X2) - [E(X)]2 where mean = E(X)

    Well Var X = E[(X- u)^2] = E[(X- E(X))^2] = E[(X^2 - 2 * X * E(X) + E(X)^2)] = E(X^2) - 2 * E(X) * E(X) + E(E(X)^2)] = E(X^2) - 2 * E(X) ^2 + E(X)^2] = E(X^2) - E(X) ^2 It's just derivation using Expectations Operator.. u is always E(X) in a probability distribution with random variable...
  19. jairamjana

    Partial autocorrelation

    Well I made it so that you should only touch Column A ... Btw I named Column A as VariableA for convenience sake.. I don't know if the excel file also stores the keys in formula name manager so any third person can make use of it... So see to it that the name box displayed while highlighting...
  20. jairamjana

    Partial autocorrelation

    @brian.field no problem.. it so happens I was looking for a way to do the Yule Walker in matrix form and how to incorporate in excel.. And I saw your post and also the pdf link.. That helped me a lot and with a little bit of tweaking I could do it in a much more refined way.. Actually there...
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