Abstract

The discrete-time multifactor Vasiček model is a tractable Gaussian spot rate model. Typically, two- or three-factor versions allow one to capture the dependence structure between yields with different times to maturity in an appropriate way. In practice, re-calibration of the model to the prevailing market conditions leads to model parameters that change over time. Therefore, the model parameters should be understood as being time-dependent or even stochastic. Following the consistent re-calibration (CRC) approach, we construct models as concatenations of yield curve increments of Hull–White extended multifactor Vasiček models with different parameters. The CRC approach provides attractive tractable models that preserve the no-arbitrage premise. As a numerical example, we fit Swiss interest rates using CRC multifactor Vasiček models.

Highlights

  • The tractability of affine models, such as the Vasiček [1] and the Cox–Ingersoll–Ross [2] models, has made them appealing for term structure modeling

  • Affine term structure models are based on a factor process, which in turn describes the evolution of the spot rate and the bank account processes

  • Under equivalent martingale measure P∗, the yield curve dynamics (Y (k, ·))k∈N0 obtained by the consistent re-calibration (CRC) algorithm of Section 3.1 has the following Heath–Jarrow–Morton [3] (HJM) representation for m > k + 1: Y (k + 1, m)(m − (k + 1))∆ = Y (k, m)(m − k)∆ − Y (k, k + 1)∆

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Summary

Introduction

The tractability of affine models, such as the Vasiček [1] and the Cox–Ingersoll–Ross [2] models, has made them appealing for term structure modeling. No-arbitrage arguments provide the corresponding zero-coupon bond prices, yield curves and forward rates Prices in these models are calculated under an equivalent martingale measure for known static model parameters. Model parameters typically vary over time as financial market conditions change They may, for instance, be of a regime switching nature and need to be permanently re-calibrated to the actual financial market conditions. This re-calibration is done on a regular basis (as new information becomes available).

Setup and Notation
Discrete-Time Multifactor Vasiček Model
Hull–White Extended Discrete-Time Multifactor Vasiček Model
Calibration of the Hull–White Extended Model
Consistent Re-Calibration
Consistent Re-Calibration Algorithm
Heath–Jarrow–Morton Representation
Real World Dynamics and Market Price of Risk
Choice of Parameter Process
Level and Speed of Mean Reversion
State Space Modeling Approach
Transition System
Measurement System
Bayesian Inference in the Transition System
Likelihood Function
Rescaling the Time Grid
Longitudinal Realized Covariations of Yields
Cross-Sectional Estimation of β and Σ
Inference on Market Price of Risk
Description and Selection of Data
Model Selection
Discussion of Identification Assumptions
Determination of the Number of Factors
Determination of Vasiček Parameters
Selection of a Model for the Vasiček Parameters
Simulation and Back-Testing
Simulation
Back-Testing
Findings
Regulatory Framework
Conclusions

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