Interest rate modeling and a time series model for functional data
- In finance, an interest rate derivative is a financial instrument where the underlying asset is an interest rate at which payments are made based on a notional amount. A common approach to price interest rate derivatives is through the use of interest rate models. However, a drawback with this approach is that calibration of interest rate models does not involve the interest rate being modeled. Hence, calibrated models may not be good representations of interest rates and may not produce reliable derivative prices. To deal with the issue, we propose a time series modeling approach to analyze interest rates, specifically, the zero-coupon yield curves. In this approach, yield curves are modeled as functional data and we introduce models that are based on the well-known autoregressive model in time series analysis. The objective of this approach is to understand the dependency of the yield curves on historical data and to predict future yield curves before they are observed. The proposed models are illustrated and compared with the time series of US Treasury zero-coupon yield curves. We explore how individual models perform during different times in an economic cycle. We also propose a way to predict future caplet prices by combining yield curve prediction using functional time series models and historical implied volatilities of caplets. The time series approach that we propose are shown to work well against existing models such as the Hull-White model.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Pong, Chung Kwan
|Stanford University, Department of Statistics
|Lai, T. L
|Lai, T. L
|Statement of responsibility
|Chung Kwan Pong.
|Submitted to the Department of Statistics.
|Thesis (Ph.D.)--Stanford University, 2010.
- © 2010 by Chung Kwan Pong
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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