Project Description
In this project, we will explore the volatility structure of US Government Bond Yields. Essentially all financial assets exhibit a phenomenon called volatility clustering where low and high volatility regimes follow each other. The alternating volatility regimes are a focus for risk management and investment decisions. We will use the well-known GARCH ([Generalized AutoRegressive Conditional Heteroskedasticity](https://en.wikipedia.org/wiki/Autoregressive_conditional_heteroskedasticity)) method to explore the statistical properties of financial time series data. This project assumes background knowledge on time series analysis, GARCH modeling, plotting and uses packages `xts` and `rugarch`. The historical yield data are published by the US Federal Reserve Data Releases and imported from Quandl, [https://www.quandl.com/data/FED/SVENY](https://www.quandl.com/data/FED/SVENY),
Project Tasks
- 1Volatility changes over time
- 2Plotting the evolution of bond yields
- 3Make the difference
- 4The US yields are no exceptions, but maturity matters
- 5Let's dive into some statistics
- 6GARCH in action
- 7Fitting the 20-year maturity
- 8What about the distributions? (Part 1)
- 9What about the distributions? (Part 2)
- 10A final quiz
Technologies
R

József Soltész
Manager at KPMG
József Soltész, CFA, FRM is a banking expert specialized in market and liquidity risks. He loves to find the underlying story hidden in the data and emphasizes sharing the knowledge. In his day-to-day work, he analyzes the risk management activities and risk models of banks using SQL, VBA, and R.