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Package 3


Introductions to Mathematics at University:

Pandemics, Mobile Apps and the Maths:

Currently frequently in the news is the introduction of mobile phone apps for contact tracing while opening up the country. The goal is to decrease the average infection rate R0, ideally to get it under 1, which means that infections will decrease.

Mathematical knowledge, beauty, curiosities, puzzles and more:

Lying with Statistics:

Disraeli's famous quote "There are three kinds of lies: lies, damned lies, and statistics." and Darrel Huff's often quote book "How to lie with Statistics" with detailed instructions on how to do so do not give our field a good press. However, such lies are typically not produced by statisticians, but by people trying to push an agenda (or inadvertently). The statisticians' role is in supporting citizens in spotting such lies. Here is a list with the most basic ways to cheat, so you get experienced and catch the liars quickly:

Visualisation and simulations of stock price data and financial instruments

Have you ever plotted stock price data? This type of data is very easily to download from the web. It has very different characteristics from the ecological or infectious disease data we saw in the two previous packages. For example, measurement error and missing data are less relevant. Usually, no information on a battery of related phenomena come with the data. The exception are other stock's prices, and analysing associations between stocks and portfolio level investment decisions making is a research area in both conventional and behavioural finance. Here are some starting points for getting a very first taste of computational finance.

  • PhD student Curtis Miller's introduction to stock market data, on how to get it and visualise it in R. There is also a Python version of this. (In case you get hungry from all this, the same author also provides donuts.)
  • Here is some sample R code to simulate stock price data. It creates a number of realisations of the same kind of stock (i.e. based on the same stochastic model). If you run it repeatedly you get another bunch of realisations. Not surprisingly, they are all different.
  • The R package derivmkts contains a lot of more sophisticated R code that you may explore for simulating stock market data and for experimenting with financial derivatives. Scroll down on the GitHub page more an overview of the package. Check out the Readme files and the Vignette to learn what this package can be used for in more detail. The functionalities include simulations based on the binomial model as well as implementations of financial derivatives theory (e.g. Black and Scholes formula based hedging). This is a lot to take in, but if you are interested you may want to first acquire some knowledge about the financial concepts, terminology, models and strategies:

Remember, you can find resources on how to learn R in Package 1. If you know another languages, e.g. Python, you may use this as well.