John Creighto
- 487
- 2
Options prices are often based upon the volatility of a stock. I'm left to wonder how we might estimate volatility. Let:
r is the yearly expected rate of return
\sigma_r is the uncertainty in the yearly expected rate of return.
\sigma_y the daily volatility.
Then we might model the current price as follows:
y(n)=\sum_{i=1}^{\infty} w_i (r+\sigma_{r,i})^{(i-n)/365}(y(n-i)+\sigma_{y,i})
where:
w_i is how much weight we use each past value to determine the future value
and
1=\sum_{i=1}^{\infty} w_i
Once the w_i's are chosen then r, \sigma_r and \sigma_y are chosen so that they minimize:
E \left[\left( y(n)-\hat{y}(n) \right)^2\right]
where:
\hat{y}(n)=\sum_{i=1}^{\infty} w_i E\left[(r+\sigma_{r,i})^{(i-n)/365}\right]y(n-i)
r is the yearly expected rate of return
\sigma_r is the uncertainty in the yearly expected rate of return.
\sigma_y the daily volatility.
Then we might model the current price as follows:
y(n)=\sum_{i=1}^{\infty} w_i (r+\sigma_{r,i})^{(i-n)/365}(y(n-i)+\sigma_{y,i})
where:
w_i is how much weight we use each past value to determine the future value
and
1=\sum_{i=1}^{\infty} w_i
Once the w_i's are chosen then r, \sigma_r and \sigma_y are chosen so that they minimize:
E \left[\left( y(n)-\hat{y}(n) \right)^2\right]
where:
\hat{y}(n)=\sum_{i=1}^{\infty} w_i E\left[(r+\sigma_{r,i})^{(i-n)/365}\right]y(n-i)
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