Last time we have proposed the agent-based cobweb model. In certain case price time series of this model exhibit different volatility. So this time we will take a look at price change statistics in this model. As is common for our financial markets posts we will consider probability density functions and spectral density.

Most people hate math and (I would like to claim that) they hate statistics even more, because statistics are often harder to understand than everyday geometry, compound interest or any other everyday math problem. Yet understanding statistics is vitally important for many everyday decisions. While we can't fix the larger problem in just one post, we can suggest an interesting talk by Jennifer Rogers given at the Royal Institution about statistical lies and how not to fall for them.

Long time ago theoretical background of the cobweb model has somewhat troubled me. So I wanted to explore my doubts. At the time I didn't have any idea how to do this properly, but recently I think I have figured it out.

In this post I will construct quite simple agent-based model of the price formation in the free market. This time the approach works, at least in part, and from these we can uncover hidden assumption made in the classical cobweb model.

Emergence and growth of streaming platforms reshapes the face of modern economy. This can potentially result in better tomorrow for all of us, or we could return to the middle ages. For a deeper discussion see the following video by Wisecrack.

Two years ago I have covered a classical price discovery model known as the cobweb model. While the premise of the model is rather simple, its internal logic seemed flawed to me. Yet at that time I didn't know how to properly explore its flaws.

A bit later on I started exploring order book models and one of the simplest ones, the efficient market marker model, caught my eye and reminded me that I want to explore some issues regarding the cobweb model.

This post (and accompanying app) sat in my "unpublished" folder for a long time, until I have rediscovered it lately and worked out few things which bugged me in this approach. So here, in this post, I will present you with an interactive app, which combines both of these models. I will also provide some discussion on why the combination of these models doesn't work as expected.