What are the main differences between discrete and continuous time models when modeling asset price dynamics?

Question

My intuition says that both approaches, discrete time models and continuous time models will be models (i.e. approximations) of reality. Therefore it should be possible to develop useful models in both domains.

Continuous time models have more mathematical elegance and can therefore probably bring more mathematical machinery to bear on the problem which presumably helps with deriving analytical solutions and asymptotic limits.

Discrete models more easily correspond to observed data and measurements and are easier to simulate on computers.

I have been told that it is possible to discretise continuous time models and vice-versa but care has to be taken when performing this transformation. Could you please highlight what the common pitfalls are, in particular when modelling asset prices? If there are other differences in the dynamics between the two approaches (for example possibly something like non-linearities, chaos, ... in one and not the other) then I would like to know about that as well.

Answer 1

I mainly speak as market practitioner when I say that I believe in the end all models that are applied to data and real life pricing issues are discretized. Think about it, even the BS hedge argument is in the end just a "theoretical continuous time overlay" of actual discrete time steps and re-hedges. Thus some of the limiting assumptions re BS. You do not have continuous prices, even if ticks come in millisecond frequency they are still discretely timed. Thus you cannot hedge continuously but only re-hedge when you receive new price discovery.

Continuous pricing models are elegant to work with from a mathematical standpoint. However, in the end whatever derivative or mortgage security you attempt to price you must resort to discretized version of pricing algorithms. Thats my take of it. I would not waste too much time on trying to figure out how to move from one version to the other. I rather recommend you think about the problem at hand and what you actually want to solve for. From my experience 90% of pricing complexities boil down to finding a replication of the to-be-price asset in question. Monte Carlo simulators have become my best friend because their applicability is so versatile.