Empirical Finance (E_FIN_EF)

The codes written during the matlab tutorial with a step by step explanation of what we are programming.

This summary provides the basis for the ARMA models. It contains an explanation of the autocorrelation, White Noise, Partial Autocorrelation, Moving Average Model, Stationarity of the time series, weakly stationary, covariance stationary, model selection criteria, and how to interpret the graphs. This summary helps you go through material without watching again the lengthy web-lectures. I practically wrote down everything what he said. It helps also if you did not watch the weblecture, because ...

Here is the summary from the models AR(1), MA(1), ARMA(2,1) step by step, explained with colours. If something is not understandable, please write it in the comments below. This summary helps you go through material without watching again the lengthy web-lectures. I practically wrote down everything what he said. It helps also if you did not watch the weblecture, because you can find here everything what he talked about.

This is the summary of forecasting with GARCH and Value at Risk. The summary contains an explaination of the derivation of the GARCH model, evaluation of volatility forecast, value at risk, testing the VaR, how to judge if the VaR is correct, an example of the model, and the criticism. This summary helps you go through material without watching again the lengthy web-lectures. I practically wrote down everything what he said. It helps also if you did not watch the weblecture, because you can fin...

Here is a summary of the above models. The explanation is taken from the class. This summary contains everything what we talked about in terms of interpretation, estimation, and diagnostic checks. This summary helps you go through material without watching again the lengthy web-lectures. I practically wrote down everything what he said. It helps also if you did not watch the weblecture, because you can find here everything what he talked about.

Here you can find a summary of the ARCH models. Basically, in this document you can find everything that the prof. said in the class. It contains explanation of different types of volatility, the basic ARCH model, conditional variance, transformation of the model into ARMA model, volatility clustering, testing for ARCH effects, diagnostic of the model. This summary helps you go through material without watching again the lengthy web-lectures. I practically wrote down everything what he said. It ...

Here are the notes from the ARMA Model Lab session. The document includes all the steps with the explanation attached. There are 8 steps. 1 - Looking to the data, 2 - Looking at the autocorrelation plot, 3 - Estimate ARMA models, 4 - Construct residuals and check if there is autocorrelation, 5 - Construct the fit of the model with the test for homoscedasticity and the log of the VIX, 6 - Forecasting, 7 - Checking the forecast model with explanation of unbiasedness, accuracy, and efficiency, 8 -...

Summary of the 2nd lecture, week 1. It includes explanation of the logit model with an empirical example, estimation of the model, and how to build a ROC curve step by step as provided by the teacher in class. Also the interpretation of the coefficients is provided. The self-study questions and the multiple questions with the solutions are from the end of the book. The exam questions are from the exams provided by the lecturer.

Here are the exercises from Chapter 12 together with the solutions.