theta t
operator, which represents the passage of time t. Here's how a stochastic process might be represented: S = 1
loop inf
theta @dt
S = S * exp( (r - 0.5 * sigma^2) * @dt
end
We also introduced stochastic expressions like expected value E(V!) given the current state of all simulated values where V! is a reference to the future value of V. 1: model EuropeanPut
2: % This model returns a simulated European put option price
3: import S “Stock prices”
4: import CUR “Discount factor”
5: import K “Strike price for the European put option”
6: import T “Time to maturity in years”
7: export P “European put option price”
8:
9: P = E(V_CUR!)
10: % T years pass
11: theta T
12: % at maturity T, the option payoff is discounted to time 0
13: V_CUR = max(K - S, 0) * CUR
14:
15: end
This system enables the evaluation of the models using Monte Carlo Simulations and the computation of expected values E(x!) using regression techniques.
I have conducted similar experiments in the past and concluded that promping does not reduce hallucinations.