## Statistics of Financial Markets II (VL)

## Course Description

Learn from Nobel prize winners, such as Engle (ARCH Models, 2003), Scholes, Merton, (Derivative Valuation, 1997) or Modigliani (Financial Markets Analysis, 1985) to understand statistics of financial markets!

The class is addressed to students with excellent knowledge of multivariate statistics and students with good skills in statistical software. This course is a starting point for students interested in quantitative finance and students with ambitions to work in the derivative, investment and risk-control departments. Former students of this course work for example at Deutsche Bank, Sal. Oppenheim, Citigroup, European Central Bank, BAFin, KPMG, Nadler Company and many international universities.

## Prerequisites

The courses Multivariate Statistical Analysis I is required.

## Course Learning Objectives

Value at Risk, backtesting, time-series models ARMA, unit-root tests, ARCH, GARCH models, Copulae dependence concept, Extreme Values, Neural Networks.

## Course Structure

The course Statistics of Financial Markets II focuses on quantitative methods in risk management such as Value at Risk (VaR) and backtesting. The implications of the current Basel II directive to the risk management of the financial institution are discussed. The students will be equipped with the knowledge of the standard time-series models ARMA, unit-root tests, ARCH and GARCH models that are essential for understanding the standard risk-management models e.g. Risk Metrics methodology. The advanced statistical methods based on the Copulae dependence concept, Extreme Values, Neural Networks as well nonparametric and adaptive methods are introduced and applied to the risk management problems.

Essential part of the course is the application and visualization of the taught methods in codes, written in different programming languages such as R, Matlab, SAS, Python, etc. One part of the examination is writing such codes, called Quantlets, and uploading them to the LvB Quantnet group on GitHub.

## Software

We will use the statistical software R and RStudio as IDE.

## Literature and Sources

Franke, J., Härdle, W., and Hafner, C. (2015) Statistics of Financial Markets: an Introduction. 4th ed., Springer Verlag, Heidelberg. ISBN: 978-3-642-54538-2 (555 p)

Härdle, W., Hautsch, N. and Overbeck, L. (2009) Applied Quantitative Finance. 2nd extended ed., Springer Verlag, Heidelberg. ISBN 978-3-540-69177-8 (448 p)

Hull (2005) Options, Futures, and Other Derivatives. 6th ed., Prentice Hall. ISBN 0-13-149908- 4 (816 p)

Härdle, W., Simar, L. (2015) Applied Multivariate Statistical Analysis. 4th extended ed., Springer Verlag, Heidelberg. ISBN 978-3-662-45170-0 (580 p)

Cizek, P., Härdle, W., Weron, R. (2011) Statistical Tools for Finance and Insurance. 2nd ed., Springer Verlag, Heidelberg. ISBN: 978-3-642-18061-3 (420 p)

** www.quantlet.de** (source codes)