Expectations, Risk Premia and Information Spanning in Dynamic Term Structure Model Estimation

Guimarães, Rodrigo Laiola

doi:10.2139/ssrn.2420379

Cited by 10 publications

(20 citation statements)

References 45 publications

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“…7 For the most direct comparison to the OIS-augmented model, I estimate the survey-augmented model using the algorithm of Guimarães (2014) which uses the same Joslin et al (2011) identification restrictions as the OIS-augmented model, as opposed to the Kim and Wright (2005) survey-augmented model that applies the Kim and Orphanides (2012) identification algorithm, first proposed in Kim and Orphanides (2005). Lloyd (2017c) shows that the Kim and Wright (2005) model performs worse than the OIS-augmented decomposition.…”

Section: Section 7 Concludesmentioning

confidence: 99%

“…16 14 See Appendix A for a detailed specification of data sources. Guimarães (2014) uses survey forecasts from the Survey of Professional Forecasters in his estimation of the US term structure of interest rates.…”

Section: Ois Rates and Survey Expectationsmentioning

confidence: 99%

“…Thus, survey forecasts are unlikely to provide sufficient information to accurately identify the daily frequency evolution of interest rate expectations. Moreover, the survey forecasts used by Kim and Orphanides (2012) and Guimarães (2014) correspond to the expectations of professional forecasters and not necessarily those of financial market participants.…”

Section: Introductionmentioning

confidence: 96%

“…They document that, between 1990 and 2003, the survey-augmented model produces sensible estimates of interest rate expectations. Guimarães (2014) shows that, relative to an unaugmented GADTSM, the survey-augmented model provides estimates of interest rate expectations that better correspond with survey expectations of future interest rates and delivers gains in the precision of interest rate expectation estimates. However, I show that estimated interest rate expectations from the OIS-augmented model are superior to the survey-augmented model for the 2002-2016 period.…”

Section: Introductionmentioning

confidence: 97%

“…However, a popular class of these models -Gaussian affine dynamic term structure models (GADTSMs) -suffers from an identification problem that results in estimates of interest rate expectations that are spuriously stable (e.g. Bauer, Rudebusch, and Wu, 2012;Kim and Orphanides, 2012;Guimarães, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Estimating nominal interest rate expectations: Overnight indexed swaps and the term structure

Lloyd

2020

Journal of Banking & Finance

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Financial market participants and policymakers closely monitor the evolution of interest rate expectations. At any given time, the term structure of interest rates contains information regarding these expectations. No-arbitrage dynamic term structure models have regularly been used to estimate interest rate expectations and term premia, but daily frequency estimates of these models fail to accurately capture the evolution of interest rate expectations implied by surveys and financial market instruments. I propose the augmentation of no-arbitrage Gaussian affine dynamic term structure models (GADTSMs) with overnight indexed swap (OIS) rates in order to better estimate the evolution of interest rate expectations and term premia across the whole term structure. I augment the model with 3 to 24-month OIS rates, which provide accurate information about interest rate expectations. The OIS-augmented model that I propose, estimated between January 2002 and December 2016 for the US, generates estimates of the expected path of short-term interest rates, up to the 10-year horizon, that closely correspond to those implied by federal funds futures rates and survey expectations at a range of horizons, and accurately depict their daily frequency evolution. Against these metrics, the interest rate expectation estimates from OIS-augmented models are superior to estimates from existing GADTSMs.

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Section: Section 7 Concludesmentioning

confidence: 99%

Section: Ois Rates and Survey Expectationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimating nominal interest rate expectations: Overnight indexed swaps and the term structure

Lloyd

2020

Journal of Banking & Finance

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Evaluating the robustness of UK term structure decompositions using linear regression methods

Malik

Meldrum

2016

Journal of Banking & Finance

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Can we Use High‐Frequency Data to Better Understand the Effects of Monetary Policy and its Communication? Yes and No!*

Hambur,

Haque

2024

Economic Record

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We examine the effects of three facets of monetary policy in Australia using high‐frequency yield changes around the Reserve Bank of Australia's announcements: current policy, signalling/forward guidance and changes in premia. Shocks to current policy have similar effects to those identified using conventional approaches, but the effects of signalling and premia shocks are imprecisely estimated. Still, the approach provides the following evidence: forward guidance shocks raised future rate expectations in the mid‐2010s as the Reserve Bank of Australia highlighted housing risks; COVID‐era policy mainly affected term premia, unlike pre‐COVID policy; shocks to the expected path of rates are predictable, suggesting markets misunderstand the RBA's reaction to data.

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Expectations, Risk Premia and Information Spanning in Dynamic Term Structure Model Estimation

Cited by 10 publications

References 45 publications

Estimating nominal interest rate expectations: Overnight indexed swaps and the term structure

Estimating nominal interest rate expectations: Overnight indexed swaps and the term structure

Evaluating the robustness of UK term structure decompositions using linear regression methods

Can we Use High‐Frequency Data to Better Understand the Effects of Monetary Policy and its Communication? Yes and No!*

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