On this set of exercises, we are going to explore some of the probability functions in R with practical applications. Basic probability knowledge is required. Note: We are going to use random number functions and random process functions in R such as runif, a problem with these functions is that every time you run them […]

# statistics

## Hacking statistics or: How I Learned to Stop Worrying About Calculus and Love Stats Exercises (Part-6)

Statistics are often taught in school by and for people who like Mathematics. As a consequence, in those class emphasis is put on leaning equations, solving calculus problems and creating mathematics models instead of building an intuition for probabilistic problems. But, if you read this, you know a bit of R programming and have access […]

## Volatility modelling in R exercises (Part-4)

This is the fourth part of the series on volatility modelling. For other parts of the series follow the tag volatility. In this exercise set we will explore GARCH-M and E-GARCH models. We will also use these models to generate rolling window forecasts, bootstrap forecasts and perform simulations. Answers to the exercises are available here. […]

## Volatility modelling in R exercises (Part-3)

This is the third part of the series on volatility modelling. For other parts of the series follow the tag volatility. In this exercise set we will use GARCH models to forecast volatility. Answers to the exercises are available here. Exercise 1 Load the rugarch and the FinTS packages. Next, load the m.ibmspln dataset from […]

## Hacking statistics or: How I Learned to Stop Worrying About Calculus and Love Stats Exercises (Part-2)

Statistics are often taught in school by and for people who like Mathematics. As a consequence, in those class emphasis is put on leaning equations, solving calculus problems and creating mathematics models instead of building an intuition for probabilistic problems. But, if you read this, you know a bit of R programming and have access […]

## Volatility modelling in R exercises (Part-2)

This is the second part of the series on volatility modelling. For other parts of the series follow the tag volatility. In this exercise set we will use the dmbp dataset from part-1 and extend our analysis to GARCH (Generalized Autoregressive Conditional Heteroscedasticity) models. Answers to the exercises are available here. Exercise 1 Load the […]

## Hacking statistics or: How I Learned to Stop Worrying About Calculus and Love Stats Exercises (Part-1)

Statistics are often taught in school by and for people who like Mathematics. As a consequence, in those class emphasis is put on leaning equations, solving calculus problems and creating mathematics models instead of building an intuition for probabilistic problems. But, if you read this, you know a bit of R programming and have access […]

## Volatility modelling in R exercises (Part-1)

Volatility modelling is typically used for high frequency financial data. Asset returns are typically uncorrelated while the variation of asset prices (volatility) tends to be correlated across time. In this exercise set we will use the rugarch package (package description: here) to implement the ARCH (Autoregressive Conditional Heteroskedasticity) model in R. Answers to the exercises […]

## Neural networks Exercises (Part-3)

Neural network have become a corner stone of machine learning in the last decade. Created in the late 1940s with the intention to create computer programs who mimics the way neurons process information, those kinds of algorithm have long been believe to be only an academic curiosity, deprived of practical use since they require a […]

## Ridge regression in R exercises

Bias vs Variance tradeoff is always encountered in applying supervised learning algorithms. Least squares regression provides a good fit for the training set but can suffer from high variance which lowers predictive ability. To counter this problem, we can regularize the beta coefficients by employing a penalization term. Ridge regression applies l2 penalty to the […]