Risk-Neutral Option Pricing with Mean-Reverting Price Dynamics
Summary
The question asks how to price a European call when the underlying follows mean-reverting arithmetic dynamics with constant absolute volatility. The answer frames option valuation through a change to a risk-neutral process: in a complete market with continuous hedging, the physical drift can be replaced for pricing purposes, while the diffusion specification determines the resulting option model. With constant absolute volatility, the relevant setup is arithmetic Brownian motion; the answer does not provide the call formula itself.
It contrasts this with proportional volatility, which leads to the Black–Scholes pricing form, and emphasizes that estimating volatility needs care when returns are autocorrelated by mean reversion. The usual square-root-of-time scaling relies on independent returns and may not apply. The discussion assumes a complete market and continuous hedging, and points to prior research rather than working through a calibration or empirical test. Its risk-neutral drift notation is presented briefly, so readers should verify the precise process convention before applying it.
Key ideas
- In a complete market with continuous hedging, option prices can be evaluated using risk-neutral dynamics.
- With constant absolute volatility, the underlying follows an arithmetic Brownian motion for pricing purposes.
- A proportional-volatility specification instead leads to the Black–Scholes option framework.
- Mean reversion can make standard volatility scaling from independent returns inappropriate.
- The answer gives a pricing framework but does not derive the call valuation formula.
Tags
Full text
# European Call price for an asset with mean reverting (Vasicek model) dynamics
# European Call price for an asset with mean reverting (Vasicek model) dynamics
Let's look at a stock with a mean reverting price dynamics: $$dS_t = a(S-S_0)dt + \sigma dW_t$$
If we let $\sigma=0.25$ and $a=-0.5$ then the variance of this process is: $$Var(S_t) = 0.199\sim0.2$$ see the Wiki article about for this kind of proces: https://en.wikipedia.org/wiki/Vasicek_model
How do I derive the Arbitrage free pricing function for a Call option with strike K and underlying being the stock with MR as described above.
## Answer by RRL (score 5, accepted)
https://quant.stackexchange.com/a/38972
The whole point of no-arbitrage pricing in a complete market is that a general underlying model of the form
$$d S_t = \mu(S_t,t)\, dt + \sigma(S_t,t) \, dW_t$$
can be replaced with the risk-neutral process.
$$d S_t = (r - \sigma^2/2)\, dt + \sigma(S_t,t) \, dW_t$$
for the purpose of finding the theoretical fair option price. This, of course, follows from the possibility of continuous hedging and, mathematically, through a change of measure.
You introduce two twists in that the drift imposes mean reversion and you set $\sigma(S_t,t) = \sigma = \text{constant}$. Had you chosen $\sigma(S_t,t) = \sigma S_t$, this would revert to the Black-Scholes model as far as the option price is concerned. The form of the drift is irrelevant.
Assuming $\sigma(S_t,t) = \sigma$ will then give the closed-form option price for arithmetic Brownian motion.
There is, however, one issue that needs to be addressed -- the estimation of $\sigma$. Without mean reversion, and autocorrelation of returns, the volatility can be estimated using price data observed at discrete time intervals and independence would imply $\sqrt{t}$ scaling. The parameter $\sigma$ used in the option pricing formula would, for example, be obtained by estimating volatility $\hat{\sigma}$ over intervals of length $\delta t$ and assigning $\sigma = \hat{\sigma}/\sqrt{\delta t}$.
This would not be the case if the real price dynamics were mean reverting.
See the paper by Lo and Wang.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.