The secret shortcut to valuation when regular analysis fails: Using Monte Carlo and a ARIMA models with MA AR(1) or AR(2) simulation
Are you interested in accurately valuing complex options and other derivatives … as well as fixed income instruments?
And doing it faster than your competition?
It’s not very straightforward to get an accurate valuation, you know. That’s because you’ll never have enough data to get a perfect picture of a given instrument or portfolio. You have to “approximate” the data or your computations will never finish!
Unless you know how to use the Monte Carlo method, of course. It’s an ingenious shortcut not many traders understand. Here’s how it works:
1. First you define a domain of possible inputs. Then …
2. Generate inputs randomly from a probability distribution over the domain. And …
3. Perform a deterministic computation on the inputs.
4. Finally, you aggregate the results.
Done correctly, this method is surprisingly accurate. Not just for individual instruments, but entire portfolios. You can even simulate an entire stock market to practice trading stocks without risk!
In fact, for situations with more than 3 or 4 degrees of freedom, formulae such as Black Scholes (i.e. analytic solutions) don’t even exist, while other numerical methods such as the Binomial options pricing model and finite difference methods face several difficulties and are impractical.
In these cases, Monte Carlo methods converge to the solution more quickly, require less memory and are easier to program.
The programming gets even easier with my latest offering to Premium members. You see, I’m just finishing up a walkthrough video that explains what you need to know about using Monte Carlo simulations.
It’s absolutely essential if you want to minimize your learning curve for this very powerful tool. Especially when I’m including all the relevant source code in R for you. It doesn’t get any easier than this!
Get get access to this tutorial now:
Other videos will cover Stochastic Volatility … Markov Chain … Autoregressive (AR(1)) and GARCH forecasting … Bootstrapping … Dynamic Linear Modeling … and more!
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