implied-volatility

  1. Nicole Seaman

    P1.T4.24.6. GARCH models and implied volatility

    Learning Objectives: Apply the GARCH (1,1) model to estimate volatility. Explain and apply approaches to estimate long horizon volatility/VaR and describe the process of mean reversion according to a GARCH (1,1) model. Evaluate implied volatility as a predictor of future volatility and its...
  2. Nicole Seaman

    P2.T5.23.6. Implied volatility

    Learning objectives: Define a volatility smile and volatility skew. Explain the implications of put-call parity on the implied volatility of call and put options. Compare the shape of the volatility smile (or skew) to the shape of the implied distribution of the underlying asset price and to the...
  3. 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...
  4. Nicole Seaman

    P1.T2.702. Simple (equally weighted) historical volatility (Hull)

    Learning objectives: Define and distinguish between volatility, variance rate, and implied volatility. Describe the power law. Explain how various weighting schemes can be used in estimating volatility. Questions 702.1. Consider the following series of closing stock prices over the tend most...
  5. Suzanne Evans

    P2.T5.200. Implied volatility

    Questions: 200.1. When the current price of a non-dividend stock is $30.00, an in-the-money (ITM) European call option with one (1.0) year to expiration and a strike price of $25.00 exhibits an implied volatility of 42.0%, although the historical volatility of the stock is 36.0%. Which is...
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