The Kalman filter is a very powerful algorithm to optimally include uncertain information from a dynamically changing system to come up with the best educated guess about the current state of the system. Applications include (car) navigation and stock forecasting. If you want to understand how a Kalman filter works and build a toy example in R, read on!
Continue reading “Kalman Filter as a Form of Bayesian Updating”
Forecasting the future has always been one of man’s biggest desires and many approaches have been tried over the centuries. In this post we will look at a simple statistical method for time series analysis, called AR for Autoregressive Model. We will use this method to predict future sales data and will rebuild it to get a deeper understanding of how this method works, so read on!
Continue reading “Time Series Analysis: Forecasting Sales Data with Autoregressive (AR) Models”
Our intuition concerning randomness is, strangely enough, quite limited. While we expect it to behave in certain ways (which it doesn’t) it shows some regularities that have unexpected consequences. In a series of seemingly random posts, I will highlight some of those regularities as well as consequences. If you want to learn something about randomness’ strange behaviour and gain some intuition read on!
Continue reading “Learning Statistics: Randomness is a Strange Beast”
R is one of the best choices when it comes to quantitative finance. Here we will show you how to load financial data, plot charts and give you a step-by-step template to backtest trading strategies. So, read on…
Continue reading “Backtest Trading Strategies Like a Real Quant”
Tomorrow, on the First of May, many countries celebrate the so called International Workers’ Day (or Labour Day): time to talk about the unequal distribution of wealth again, so read on!
Continue reading “The Rich didn’t earn their Wealth, they just got Lucky”
Asset returns have certain statistical properties, also called stylized facts. Important ones are:
- Absence of autocorrelation: basically the direction of the return of one day doesn’t tell you anything useful about the direction of the next day.
- Fat tails: returns are not normal, i.e. there are many more extreme events than there would be if returns were normal.
- Volatility clustering: basically financial markets exhibit high-volatility and low-volatility regimes.
- Leverage effect: high-volatility regimes tend to coincide with falling prices and vice versa.