Empirical Finance - Weekly Summaries + Exercises and 1 Lab Session

Here are the exercises from Chapter 3 and 4 together with their solutions.

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. This summary helps you go through...

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

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 ...

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 - Co...

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 clusteri...

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 practical...

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...

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 everythin...

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. T...

This summary provides the basis for unit roots. It contains an explanation of the unit root issues, transitory effects, permanent effect, random walk model (with drift), trend stationary process, how to solve the issues, de-trending, how to formally test for non-stationarity, Dikey-Fuller test, Au...

This summary provides a good explanation of the panel data. It goes into explaining the meaning of panel data, how to deal with panel data regressions, pooled regressions with examples from the class, fixed effects, time and firm fixed effects and random effects model. It explains the within and be...