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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 Tasks1,500 XP

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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

  1. 1
    Volatility changes over time
  2. 2
    Plotting the evolution of bond yields
  3. 3
    Make the difference
  4. 4
    The US yields are no exceptions, but maturity matters
  5. 5
    Let's dive into some statistics
  6. 6
    GARCH in action
  7. 7
    Fitting the 20-year maturity
  8. 8
    What about the distributions? (Part 1)
  9. 9
    What about the distributions? (Part 2)
  10. 10
    A final quiz

Technologies

R R

Topics

Data VisualizationApplied Finance
József Soltész Headshot

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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