Common Factors Affecting Bond Returns

Litterman, Robert B.; Scheinkman, José A.

doi:10.3905/jfi.1991.692347

Cited by 1,452 publications

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“…33 Litterman and Scheinkman (1991) report that the third factor, modeling changes in the curvature of the term structure, is important in explaining price changes. Table 1 Descriptive statistics for cap/floor prices This table presents descriptive statistics of the data set used in this paper.…”

Section: Resultsmentioning

confidence: 99%

Pricing and hedging interest rate options: Evidence from cap–floor markets

Gupta

Subrahmanyam

2005

Journal of Banking & Finance

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for valuable comments on earlier drafts. We acknowledge with thanks the insightful comments provided by two anonymous referees on a previous draft of the paper, which led to a substantial improvement in our model specifications. We are also thankful to the participants in the seminars at Baruch College, Case Western Reserve University, McGill University, New York University, Rutgers University, University of South Carolina, University of Strathclyde, University of Toronto, and conference participants at the Derivatives Securities Conference at Boston University, the Western Finance Association meetings, the European Financial Management Association meetings, the European Finance Association meetings and the Financial Management Association meetings for comments and suggestions. The usual disclaimer applies. Pricing and Hedging Interest Rate Options:Evidence from Cap-Floor Markets AbstractWe examine the pricing and hedging performance of interest rate option pricing models using daily data on U.S. dollar cap and floor prices across both strike rates and maturities. Our results show that fitting the skew of the underlying interest rate probability distribution provides accurate pricing results within a one-factor framework. However, for hedging performance, introducing a second stochastic factor is more important than fitting the skew of the underlying distribution. This constitutes evidence against claims in the literature that correctly specified and calibrated one-factor models could replace multi-factor models for consistent pricing and hedging of interest rate contingent claims.

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Section: Resultsmentioning

confidence: 99%

Pricing and hedging interest rate options: Evidence from cap–floor markets

Gupta

Subrahmanyam

2005

Journal of Banking & Finance

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“…23 The accurate fit of our models is due to the fact that the three yield factors well capture the variation in the yield curve (Litterman and Scheinkman (1991)). The spanned models achieve a slightly better fit because the macro variables also affect model-implied yields and can capture some additional yield variation.…”

Section: Estimated Spanned and Unspanned Mtsmsmentioning

confidence: 95%

Resolving the Spanning Puzzle in Macro-Finance Term Structure Models

Bauer

Rudebusch

2015

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Previous macro-finance term structure models (MTSMs) imply that macroeconomic state variables are spanned by (i.e., perfectly correlated with) model-implied bond yields. However, this theoretical implication appears inconsistent with regressions showing that much macroeconomic variation is unspanned and that the unspanned variation helps forecast excess bond returns and future macroeconomic fluctuations. We resolve this contradiction-or "spanning puzzle"-by reconciling spanned MTSMs with the regression evidence, thus salvaging the previous macro-finance literature. Furthermore, we statistically reject "unspanned" MTSMs, which are an alternative resolution of the spanning puzzle, and show that their knife-edge restrictions are economically unimportant for determining term premia. Terms of use: Documents in EconStor mayJEL-Code: E430, E440, E520.

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“…The terms "short rate" and "inflation" are just convenient names for the unobserved factors. Another example is Litterman and Scheinkman (1991), who call their factors "level," "slope" and "curvature". Similarly, Dai and Singleton (2000) use the words "level," "slope" and "butterfly" to describe their factors.…”

Section: Introductionmentioning

confidence: 99%

A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables

Ang

Piazzesi

2003

Journal of Monetary Economics

1,277

325

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We describe the joint dynamics of bond yields and macroeconomic variables in a Vector Autoregression, where identifying restrictions are based on the absence of arbitrage. Using a term structure model with inflation and economic growth factors, together with latent variables, we investigate how macro variables affect bond prices and the dynamics of the yield curve. We find that the forecasting performance of a VAR improves when no-arbitrage restrictions are imposed and that models with macro factors forecast better than models with only unobservable factors.Variance decompositions show that macro factors explain up to 85% of the variation in bond yields. Macro factors primarily explain movements at the short end and middle of the yield curve while unobservable factors still account for most of the movement at the long end of the yield curve.

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Common Factors Affecting Bond Returns

Cited by 1,452 publications

References 0 publications

Pricing and hedging interest rate options: Evidence from cap–floor markets

Pricing and hedging interest rate options: Evidence from cap–floor markets

Resolving the Spanning Puzzle in Macro-Finance Term Structure Models

A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables

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