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GARCH Models Volatility Quiz

Faham sida naxdintii hore iyo kala duwanaanshuhu u kaxeeyaan saadaasha GARCH, oo kala saar isbeddelka shuruudda ah jihada soo noqoshada iyo khatarta la ogaaday.

Wadooyinka haga ee la xidhiidhaMoodooyinka Volatility Garch
Su'aal 1 ee 8

Which terms drive the next variance in a GARCH(1,1) model?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Which terms drive the next variance in a GARCH(1,1) model?
    • A constant, past squared innovation and past conditional variance
    • Only the current return sign
    • The next observed return and its future error
    • The number of features and clusters
  2. What does a large recent squared shock tend to do to conditional variance?
    • Set the expected return direction to positive
    • Raise the next variance forecast through the ARCH term
    • Force beta to zero
    • Make realized variance known exactly
  3. Can a GARCH variance forecast determine whether the next return is positive or negative?
    • Yes, beta is the return sign
    • Yes, higher variance means a positive return
    • No, it models conditional variance rather than return direction
    • Only if the variance forecast is zero
  4. What do standardized residuals help assess?
    • The exact future loss amount
    • Whether feature names are ordered
    • The number of seasonal periods
    • Whether innovations scaled by fitted volatility retain structure
  5. Why inspect squared standardized residuals?
    • To estimate the mean return direction
    • To look for remaining volatility clustering
    • To calculate a classification threshold
    • To test whether all shocks are positive
  6. Under standard GARCH(1,1) assumptions, what does alpha+beta near one suggest?
    • A known positive return
    • No effect of past variance
    • Highly persistent volatility shocks
    • A zero conditional variance
  7. Why might a heavy-tailed innovation distribution be considered?
    • It makes the mean equation unnecessary
    • It guarantees every tail event is predicted
    • It sets conditional variance to zero
    • Financial returns may produce more extreme observations than a normal model captures
  8. Which measurement caveat applies when validating a variance forecast against realized variance?
    • The proxy itself is noisy and imperfectly measured
    • A proxy is always the true conditional variance
    • Only the return mean can be evaluated
    • The forecast horizon no longer matters