I am trying to price a call option on a mutual fund.
Given the lack of market implied data, I am going to estimate the fund´s expected volatility using as a reference its historical volatility (calculated over a period comparable to the option maturity).
However, I am not sure how to estimate the fund´s expected return. Some alternatives are:
- Use an appropriate risk free rate and deduct the annual fees of the underlying fund
- Use the historical return as expected return and neglect the annual fees
(1) will be consistent with the traditional risk-neutral framework while (2) can be backed by the lack of market implied information, the potential limitations in hedging the call option, and the fact that historical returns may exhibit some statistical persistence in mutual funds.
Which alternative do you think is more appropriate? Given the fund´s historical returns and the current risk-free rates (1) and (2) produce very different option values.
EDIT: Per comments I understand that there might be no generally accepted answer for this question. Therefore I will use both risk-neutral and real-world distributions.
Regarding the real-world distribution, once I have estimated $\mu$ and $\lambda =\frac{\mu-r_f}{\sigma}$ my idea will be :
- Estimate the real-world terminal distribution using $\mu$ instead of $r_f$. For instance using: $\frac{dS}{S} = \mu dt + \sigma dW_t$
- Calculate the expected payoff under the real-world terminal distribution
- Discount this payoff using an appropriate discount rate based on $\mu$ and $\lambda$.
Do you think this approach is enough or are there other details that I should take into account.
Finally, which will be an appropriate discount rate for the real-world payoffs? I am inclined to use a simple CAPM approach $e^{-r_dt}$ with $r_d = r_f + \beta(E(r_m) - r_f)$, but this do not make any explicit use of $\lambda$, so I am uncertain here.