Full Information Maximum Likelihood Estimation
Posted: Thu May 05, 2011 9:03 pm
Hi
I am trying to estimate the following equation with the help of Full Information Maximum Likelihood:
Rit= α0 + γm*βim + γl*βil + βim*Rmt + βil*Lt + εit
Where,
Rit = Monthly return of portfolio i(i=1,2,3,4,5,6 and 7) in month t
Rmt = Monthly return of ASX All ordinaries in month t
Lt= unexpected changes in the level derived from ARIMA (p, d, q) process
γm = the market risk contribution to expected return on common stock or risk premium co-efficient
γl = the level factor contribution to expected return on common stock or risk premium co-efficient
βim = Factor risk sensitivity, known as ‘factor loadings’ for the market return
βil = Factor risk sensitivity, known as ‘factor loadings’ for the unexpected change in the level factor
Here both γm(γl) and βim(βil) are not observable. I need to get the both factor sensitivity co-efficient(βim and βlm and risk premium co-efficient(γm and γl ) through Full Information Maximum Likelihood Estimation.
It would be great any one assists in solving this problem.
Regards,
Akhtar
I am trying to estimate the following equation with the help of Full Information Maximum Likelihood:
Rit= α0 + γm*βim + γl*βil + βim*Rmt + βil*Lt + εit
Where,
Rit = Monthly return of portfolio i(i=1,2,3,4,5,6 and 7) in month t
Rmt = Monthly return of ASX All ordinaries in month t
Lt= unexpected changes in the level derived from ARIMA (p, d, q) process
γm = the market risk contribution to expected return on common stock or risk premium co-efficient
γl = the level factor contribution to expected return on common stock or risk premium co-efficient
βim = Factor risk sensitivity, known as ‘factor loadings’ for the market return
βil = Factor risk sensitivity, known as ‘factor loadings’ for the unexpected change in the level factor
Here both γm(γl) and βim(βil) are not observable. I need to get the both factor sensitivity co-efficient(βim and βlm and risk premium co-efficient(γm and γl ) through Full Information Maximum Likelihood Estimation.
It would be great any one assists in solving this problem.
Regards,
Akhtar