Fitting Risk-Neutral Distributions to Option Prices, Including Negative Rates

# Parametric estimation of risk-neutral density/implied distribution


# Parametric estimation of risk-neutral density/implied distribution












since a long time I'm struggling with a particular question regarding the parametric estimation of the risk-neutral density (or implied probability) from option prices.

I want to pursue the parametric approach of minimizing the squared deviations from theoretical and observed prices as it is described e.g. in Bahra (1997, p. 22 and the following) (http://www.bankofengland.co.uk/archive/Documents/historicpubs/workingpapers/1997/wp66.pdf).

As we know and as stated in equation (10) and (11) of Bahra (1997), the theoretical call and put prices are given by the discounted expected pay-offs, i.e.

\begin{equation} c(X, \tau) = e^{-r\tau}\int_{S_T = K}^{\infty} q(S_{T})(S_{T} - X)dS_{T} \\ p(X, \tau) = e^{-r\tau}\int_{S_T = K}^{\infty} q(S_{T})(X - S_{T} )dS_{T}, \end{equation}

with $X$ as the strike price, $\tau$ the time to maturity, $r$ the risk-free interest rate, $S_{T}$ the underlying asset price at maturity and $q(S_{T})$ the risk-neutral density with some specific functional form (log-normal, mixture of log-normals etc.).

The parameters $\theta$ of the risk-neutral density are obviously unknown, and that's why we minimize the squared distance between the theoretical and the real-world call/put prices, $\hat{c}_{i},\hat{p}_{i}$ w.r.t. $\theta$, i.e.

\begin{equation} min_{\theta} ( \sum_{i=1}^{n} (c(X, \tau) - \hat{c}_{i})^2 + \sum_{i}^{m}(p(X, \tau) - \hat{p}_{i})^2 ) \end{equation}

This is a simplified version of eq. (17) in Bahra.

Say we assume $q(S_{T})$ to be lognormal with expectation $\alpha$ and variance $\beta$. Personally, I would now use some sort of optimizer to obtain these parameters. However, in the literature of estimating the risk-neutral density, one usually further assumes that $\alpha = ln S_{t} + (\mu - 0.5\sigma^2)\tau$ and $\beta = \sigma\sqrt\tau$, where $\mu, \sigma$ are parameters of the Black-Scholes model.

My question now is whether we actually need to make this assumption or if we can simply optimize over two parameters that are free of any assumption?

I have this concern, because in my specific case, my underlying of the option is not an asset, but interest rates, i.e. an index (this type of options is called caps and floors). Otherwise, the approach is the same, but I'm wondering whether it would make sense to assume $\alpha = ln S_{t} + (\mu - 0.5\sigma^2)\tau$ and $\beta = \sigma\sqrt\tau$ (for instance, interest rates, i.e. $S_{t}$, could be negative, which would cause a problem)

I would appreciate any remarks.

## Answer by Quantuple (score 1, accepted)

https://quant.stackexchange.com/a/32894

- If you assume that, under $\Bbb{Q}$, you have the following parameteric distribution for asset prices $$ S_T \sim logN(\alpha,\beta) $$ you can indeed directly optimise for $\alpha$ and $\beta$. However, since your objective function involves call/put prices and not the density itself, you should express these prices as functions of $\alpha$ and $\beta$(*). Given the lognormal assumption, you fall-back onto the famous Black-Scholes modelling framework, hence the established link between $(\alpha,\beta)$ and $(\mu=r-q, \sigma)$.

- The lognormal assumption does not make sense if you are to model negative interest rates indeed. You can postulate any distribution whose support is $\Bbb{R}$ instead of $\Bbb{R}^+$ e.g. $$ S_T \sim N(\alpha, \beta) $$ and optimise for $\alpha$,$\beta$. Again, the only thing left to be done is to find the expression call/put prices under the postulated modelling assumptions (here normal distribution, hence Bachelier model).

(*) Actually, you could simply express the prices using the integral forms you mention in your original post, but this is less computationally efficient (requires a numerical integration) than if you use a closed-form formula, obviously.