# Jump diffusion simulation

I want to simulate a geometric Brownian motion and we assume that the volatility of the stock can take just two values $$\sigma_1=0.2$$ and $$\sigma_2=0.8$$. We also assume that the jumps up from lower volatility $$\sigma_1$$ to higher volatility $$\sigma_2$$ occur as a exponential process with rate $$\lambda_1=2$$. Likewise, the jumps down from volatility $$\sigma_2$$ to lower volatility $$\sigma_1$$ occur as an exponential process with rate $$\lambda_2=4$$. I know how to simulate a geometric Brownian motion but i can't understand how I simulate the volatility. I must compare a number from the exponential distribution with what on every step to decide if I will make a jump or not?

• I think you need to compare a uniformly distributed random variate $u\sim U(0,1)$ with $z_i \equiv e^{-\lambda_i \Delta t}$, where $\Delta t$ is the time step in your simulation. If $u < z_i$, stay, else go to the other state. Nov 25, 2020 at 8:34
• I tried but doesn't work. Nov 25, 2020 at 10:03
• Well then, why don't you post your code and what you've tried so far in this forum? Nov 25, 2020 at 12:26
• So this is the code. I dont think that the condition in the second if statement is correct. Nov 25, 2020 at 13:39

Besides a couple of ways you might try to improve your code (which I will not do here); your jump check is not working correctly:

In a time step $$\Delta t$$, the process will jump with probability $$\approx exp(-\lambda \Delta t)$$. Hence, you need to compare

if (unifrnd(0,1) > exp(-lambda * dt))
% jump occured
% flip state
else
% no jump occured
% do not flip state
endif


HTH?

NB: You might want to simulate a vector of uniforms, and then iterate over elements to get the state $$1$$ or $$2$$. From this, you may compose a vector of volatiltities per time step.

I assume you are using Matlab or Octave? If that's the case, vectorisation is king!

Just use the definition of conditional probability. With $$\gamma_t \in \{-1,1\}$$ an indicator returning 1 if $$\sigma = 0.8$$ and -1 otherwise, and $$x_{1:T}$$ the path of the Brownian motion over the time-period $$[1,T]$$ you have

$$p(x_{1:T}, \gamma_{1:T}) = p(x_{1:T} \vert \gamma_{1:T})p(\gamma_{1:T}).$$

In practice you just simulate, until time $$T$$, alternating event-times $$\tau_i$$ from $$\text{Exp}(\lambda_1)$$ and $$\text{Exp}(\lambda_2)$$ until $$\tau_i > T$$. Conditional on these times, you then just simulate your GBM conditional on $$\gamma_t$$. To make your life easier, choose your step-size such that you get very close to all the event-times $$\tau_i$$ when simulating the GBM.

• Can you explain it a bit more please? Nov 26, 2020 at 10:32