GARCH and Monte Carlo Simulation by Edu Pristine

Author of lecture GARCH and Monte Carlo Simulation

Edu Pristine

Customer reviews

Excerpts from the accompanying material

... standard deviation of returns, "t" is time, and ? is the random variable + ...

... various possible scenarios of the future are simulated ...

... distribution so that the Monte Carlo simulation follows a normal distribution.Bootstrap method: The bootstrap approach draws random return data randomly from the sample of historical data. ...

... which prices will be computed © EduPristine For Quants-III (Confidential) 5 Calculate the value of the portfolio for sequence ...

... for the option at maturity © EduPristine For Quants-III (Confidential) 6 Discount each payoff value back to its present value using risk ...

... CIR model is useful only for simple bond portfolios. It is not suitably complex for leveraged and fixed income portfolios. 2. Brennan and Schwartz Model © EduPristine For Quants-III (Confidential) 7This is a two factor model of ...

... to sampling variation, should be greater with: A. More observations and a high confidence level (e.g., 99%) ...

... with the square root of the number of data points. A similar reasoning applies to (2). A greater confidence level involves fewer ...

... to simulate paths of the stock price using a Monte Carlo simulation. Let t denote the time interval used and let St denote the stock price at time interval t. So, according to your model, s t+1= s t(1 + 0.13 t + 0.25 ??t) where ? is a standard normal variable. To implement this simulation, you generate a path of the stock price by starting at t = 0, generating a sample for ?, updating the stock price according to the model ...

... Choice A describes a valid method for generating a sample from ...