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Rather than "data mining", I'd try to have a more structured approach to the selection of variables / factors: the key drivers in ag prices are, as always in economics, supply and demand. Supply is, as you say, determined by planted acreage and yields, which in turn depend on weather and other factors (for example fertilization - can't run heavy on Nitrogen ...


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To provide a straight forward answer: It is not a good model. It never was, it never will be. Until we all do not come up with a better model that provides better modeling accuracy while it is equally intuitive and makes similarly simplifying assumptions the BS model with its geometric brownian motion component is here to stay. It actually does not matter ...


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I think this is a good idea; instead of if I'd ask: to what degree though. Futures prices reflect a lot of factors, so naturally you will only see some correlation which will change over time and even over different expiries (e.g. due to different liquidity in the back months). My advice therefore: Make sure you understand how the markets work because you ...


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Basically, Black-Scholes is an "industry standard" formula. It is widely used by practitioners and usually augmented with extra specifications or intuition. It has a closed form solution, which is rare in option pricing models. It is also relative to simple to understand. Otherwise, you usually need to rely on Monte Carlo simulation or some other way. And ...


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Brownian motion - because it is simple, and results in intuitive closed form solutions, and it's not a terrible description of asset prices, especially when employed in high-frequency event time. Geometric - because the returns compound, and equities cannot go below zero due to the fact that they are limited liability corporations There are many, many ...


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Choose the most robust (or insensitive) strategy. You are right that the best strategy might be overfit. So look at your parameter space and focus on the area where profitability, for example, changes least when you change the parameter value. Here is a 1D example: The most profitable strategy is that single point that unfortunately leaves no room for ...


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If at first you don't have a model at all, then geometric Brownian motion is not bad. As others before me said: log-returns are normally distributed in this model. This is debatable and there are times and markets where this is not true. There is more than enough research about this. But why is a model based on Brownian motion not that bad? The reason is ...


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The normal distribution is very powerful distribution: By the central limit theorem, the mean of any large sample always converges to the normal distribution Considering the most simplistic Binomial Tree model, where price goes only up or down each period, it can be shown that the distribution of returns of this tree converges to Normal for infinetesimal ...


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So Black-Scholes came around and it made pricing options mathematically viable in a (seemingly) objective sense. The assumptions underlying the model are flawed, but they work reasonably well in a lot of market environments. The problem lies in when normal market conditions start to change character. When things start to get wacky, like when a giant selloff ...



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