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Finding an Equivalent Martingale Measure for a Time-Changed Brownian Stock Model

Article Quant Q&A · Author: lbf_1994

Summary

The document explores how to model a stock under the physical measure as an exponential Lévy process, apply an integrated CIR clock, and then seek an equivalent martingale measure. It works through a Brownian special case with two shocks, one correlated with the intensity process to represent leverage. The proposed change of measure uses Girsanov shifts chosen so the discounted price has the drift required for a martingale, and it derives altered CIR parameters for the intensity.

The author presents this as a derivation and asks whether it is valid, especially regarding the time-change Brownian representation and the filtration for the martingale property. Those questions remain unanswered in the document. It provides no proof of the needed integrability conditions or of the measure change being equivalent, and the displayed intensity dynamics appear to require checking. The derivation is therefore useful as a framework and set of issues to investigate, not as a validated pricing recipe.

Key ideas

  • An integrated CIR process can serve as a stochastic clock for an exponential Lévy stock model.
  • Correlation between the clock's Brownian driver and the stock's Brownian component can represent leverage.
  • The proposed Girsanov shifts are selected to make the discounted stock drift match its quadratic-variation correction.
  • The Brownian representation under the time change and the relevant filtration require careful justification.
  • A valid equivalent measure also depends on integrability conditions that the document does not establish.

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Full text
# Equivalent martingale measure in time changed Levy models


# Equivalent martingale measure in time changed Levy models












I am investigating time changed Levy models. As far as I have seen, these models are usually directly described under the risk neutral measure $\mathbb{Q}$. However, I'm interested in first modelling the dynamics under the physical measure $\mathbb{P}$ and then using some change of measure to find an EMM. I work in the following framework: First I model the stock price $S(t)$ under the physical measure $\mathbb{P}$ as

$$ S(t):=S(0)\exp(X(t)) $$ with an arbitrary Levy process $X(t)$. The time change I use is given by an integrated CIR process, i.e. $$ T_t = \int_0^t\lambda(s)\mathrm{d}s $$ with $$ \mathrm{d}\lambda(t)=\kappa(\theta-\lambda(t))\mathrm{d}t+\eta\sqrt{\lambda(t)}\mathrm{d}W^1(t),\quad \lambda(0)=\lambda_0. $$ In order to capture the leverage effect, we can allow the Brownian motion $W^1(t)$ driving the intensity $\lambda(t)$ to be correlated with the Brownian motion part of $X(t)$. The stock price process under the time change is then given by $$ \tilde S(t):=S(T_t). $$

To start, I consider the following simple case: $$ X(t)=\mu t+\sigma \rho W^1(t) + \sigma\sqrt{1-\rho^2}W^2(t) $$ where $W^1(t)$ and $W^2(t)$ are two independent Brownian motions. In this case $\rho$ controls the leverage. The time changed price process is then given by $$ \tilde S(t)=S(0)\exp\left(\mu\int_0^t\lambda(s)\mathrm{d}s+\sigma\rho W^1\left(\int_0^t\lambda(s)\mathrm{d}s\right)+\sigma\sqrt{1-\rho^2} W^2\left(\int_0^t\lambda(s)\mathrm{d}s\right)\right). $$ In order to exclude arbitrage the discounted stock price process $$ \exp(-rt)\tilde S(t) = S(0)\exp\left(-rt+\mu\int_0^t\lambda(s)\mathrm{d}s+\sigma\rho W^1\left(\int_0^t\lambda(s)\mathrm{d}s\right)+\sigma\sqrt{1-\rho^2} W^2\left(\int_0^t\lambda(s)\mathrm{d}s\right)\right). $$ must be a martingale. By the Dubins-Schwarz theorem it holds $$ W^i\left(\int_0^t \lambda(s)\mathrm{d}s\right)=\int_0^t\sqrt{\lambda(s)}\mathrm{d}W^i(s), \quad i=1,2. $$ Hence, I can write $$ \exp(-rt)\tilde S(t)=S(0)\exp\left(-rt+\mu\int_0^t\lambda(s)\mathrm{d}s+\sigma\rho\int_0^t\sqrt{\lambda(s)}\mathrm{d}W^1(s)+\sigma\sqrt{1-\rho^2}\int_0^t\sqrt{\lambda(s)}\mathrm{d}W^2(s)\right) $$ In order to find a change of measure, I write $$ \exp(-rt)\tilde S(t)=S(0)\exp\left(-rt+\mu\int_0^t\lambda(s)\mathrm{d}s-\sigma\rho\int_0^t\gamma(s)\sqrt{\lambda(s)}\mathrm{d}s-\sigma\sqrt{1-\rho^2}\int_0^t\delta(s)\sqrt{\lambda(s)}\mathrm{d}s\\ +\sigma\rho\int_0^t\sqrt{\lambda(s)}\mathrm{d}\left(W^1(s)+\int_0^s\gamma(r)\mathrm{d}r\right)\\ +\sigma\sqrt{1-\rho^2}\int_0^t\sqrt{\lambda(s)}\mathrm{d}\left(W^2(s)+\int_0^s\delta(r)\mathrm{d}r\right)\right) $$ It must now hold that $$ -rt+\mu\int_0^t\lambda(s)\mathrm{d}s-\sigma\rho\int_0^t\gamma(s)\sqrt{\lambda(s)}\mathrm{d}s-\sigma\sqrt{1-\rho^2}\int_0^t\delta(s)\sqrt{\lambda(s)}\mathrm{d}s \\ = -\frac{\sigma^2}{2}\int_0^t\lambda(s)\mathrm{d}s $$ Differentiating both sides gives $$ -r+\mu\lambda(t)+\sigma\rho\gamma(t)\sqrt{\lambda(t)}-\sigma\sqrt{1-\rho^2}\delta(t)\sqrt{\lambda(t)}=-\frac{\sigma^2}{2}\lambda(t). $$ We set $\gamma(t):=\mu\sqrt{\lambda(t)}$ with arbitrary $\mu\in\mathbb{R}$. Hence, it must hold $$ \delta(t) = -\frac{r}{\sigma\sqrt{1-\rho^2}}+\frac{(\mu-\sigma\rho+\sigma^2/2)\sqrt{\lambda(t)}}{\sigma\sqrt{1-\rho^2}} $$ The risk neutral measure can then be defined via $$ \frac{\mathrm{d}\mathbb{Q}}{\mathrm{d}\mathbb{P}}:=\mathcal{E}(-\gamma(t)\bullet W^1(t)-\delta(t) \bullet W^2(t)). $$ The dynamics of the intensity under $\mathbb{Q}$ are then given by $$ \mathrm{d}\lambda(t)=\kappa^*(\theta^*-\lambda(t))\mathrm{d}t+\sigma\sqrt{\lambda(t)}\mathrm{d}W^1(t) $$ with $\kappa^*=\kappa+\eta\mu$ and $\theta^*=\frac{\kappa\theta}{\kappa+\eta\mu}$. Now my questions:

- Is the whole argument sound?

- In the part where I used the Dubins-Schwarz theorem, can I really use the same Brownian on both sides of the equation? In the theorem it says that there is some Brownian motion such that it holds. But if it isn't the same brownian motion I have no clue on how to proceed.

- With respect to which filtration is the discounted stock price a martingale under $\mathbb{Q}$? I guess it must be $\mathcal{G}_t:=\sigma\left(W^i(T_s), T_s; 0\leq s\leq t\right)$ . How can I make this rigorous?

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.