Transfer and Reinforcement Learning
In my previous column on Machine Learning applications in finance I discussed credit and market risk. Today, I will address another exciting ML application: pricing and hedging financial derivatives.
You may recall that a derivatives contract (such as an option) derives its value from the value of an underlying asset, such as a stock, an index, a currency or a commodity. The value of an option is determined by its maturity and exercise price as well as the current price of the underlying, its volatility and the interest rate.
If all these parameters are known, then the option’s price (even for an exotic and complex option) is just a deterministic function of these parameters – albeit a very complicated and nonlinear one. In some rare situations, this function is known. For European puts and calls, it is the famous Black-Scholes formula. But for most options, it is unknown, and we try to approximate it by, for instance, finite difference methods or Monte Carlo simulations.