*FRM Learning Objectives* 1) Explain the lognormal property of stock prices, the distribution of rates of return, and the calculation of expected return. 2) Distinguish the key properties and identify the common occurrences of the following distributions: binomial distribution, normal distribution. 3) Describe how volatility is captured in the binomial model. 4) Describe how the value calculated using a binomial model converges as time periods are added.
Thank you for the fantastic video. One small question, in the Binomial model or even earlier what is the purpose of using Square root of t rather than just t. Why t is being split for two paths in a binomial model. Even suggestion to read reference material is welcome.
Hello Prasad, the square root of delta t (or T) comes because if you were to assume that returns over successive time intervals (or timesteps) are independent and come from the same distribution, the variance "scales with time" which means that variance of 1-year return will be 52 times variance of weekly returns. Since standard deviation is square root of variance, the above rule implies that standard deviation scales with "square root of time".
*FRM Learning Objectives*
1) Explain the lognormal property of stock prices, the distribution of rates of return, and the calculation of expected return.
2) Distinguish the key properties and identify the common occurrences of the following distributions: binomial distribution, normal distribution.
3) Describe how volatility is captured in the binomial model.
4) Describe how the value calculated using a binomial model converges as time periods are added.
Thank you for the fantastic video. One small question, in the Binomial model or even earlier what is the purpose of using Square root of t rather than just t. Why t is being split for two paths in a binomial model.
Even suggestion to read reference material is welcome.
Hello Prasad, the square root of delta t (or T) comes because if you were to assume that returns over successive time intervals (or timesteps) are independent and come from the same distribution, the variance "scales with time" which means that variance of 1-year return will be 52 times variance of weekly returns. Since standard deviation is square root of variance, the above rule implies that standard deviation scales with "square root of time".
amazing. thank you
This is amazing thank you
Glad that you liked the video.