• Corpus ID: 248495933

On Unspanned Latent Risks in Dynamic Term Structure Models

  title={On Unspanned Latent Risks in Dynamic Term Structure Models},
  author={Tomasz Dubiel-Teleszynski and Konstantinos Kalogeropoulos and Nikolaos Karouzakis},
We explore the importance of information hidden from the yield curve and assess how valuable the unspanned risks are to a real-time Bayesian investor seeking to forecast excess bond returns and maximise her utility. We propose a novel class of arbitrage-free unspanned Dynamic Term Structure Models (DTSM), that embed a stochastic market price of risk specification. We develop a suitable Sequential Monte Carlo (SMC) inferential and prediction scheme that guarantees joint identification of… 

Figures and Tables from this paper



Restrictions on Risk Prices in Dynamic Term Structure Models

  • M. Bauer
  • Economics
    SSRN Electronic Journal
  • 2015
Restrictions on the risk-pricing in dynamic term structure models (DTSMs) tighten the link between cross-sectional and time-series variation of interest rates, and make absence of arbitrage useful

Information in (and not in) the Term Structure

Standard approaches to building and estimating dynamic term structure models rely on the assumption that yields can serve as the factors. However, the assumption is neither theoretically necessary

Risk Premiums in Dynamic Term Structure Models with Unspanned Macro Risks

type="main"> This paper quantifies how variation in economic activity and inflation in the United States influences the market prices of level, slope, and curvature risks in Treasury markets. We

Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?

Goyal and Welch (2007) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. In this article,

Sequential Learning and Economic Benefits from Dynamic Term Structure Models

we sequential models on different sets of explore puzzling between meaningful out-of-sample bond

Recovery with Applications to Forecasting Equity Disaster Probability and Testing the Spanning Hypothesis in the Treasury Market

The Yield Spread and Bond Return Predictability in Expansions and Recessions

This paper uncovers that expected excess bond returns display a positive correlation with the slope of the yield curve (i.e., yield spread) in expansions but a negative correlation in recessions.

Bond Risk Premiums with Machine Learning

We show that machine learning methods, in particular, extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on

Can we Exploit Predictability in Bond Markets?

This paper investigates the optimal bond portfolio choice of an investor in a model that captures both the failure of the expectation hypothesis and recent findings that variables not in the term

Expected Returns in Treasury Bonds

We study risk premium in U.S. Treasury bonds. We decompose Treasury yields into inflation expectations and maturity-specific interest-rate cycles, which we define as variation in yields orthogonal to