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Modeling the Volatility of US Bond Yields

Discover how the US bond yields behave using descriptive statistics and advanced modeling.

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  • 10 tasks
  • 117 participants
  • 1,500 XP

Project Description

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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) 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. You can get familiar with GARCH modeling if you check out the course GARCH Models in R.

The historical yield data are published by the US Federal Reserve Data Releases and imported from Quandl, 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
Instructor Avatar
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.

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Technology

  • R LogoR
  • Topics

    Data VisualizationApplied Finance