Bond risk, bond return volatility, and the term structure of interest rates

Viceira, Luis M.

doi:10.1016/j.ijforecast.2011.02.018

Cited by 110 publications

(54 citation statements)

References 81 publications

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“…Moreover, the short rate is important for the realized bond CAPM beta and the bond C-CAPM beta, cf. Viceira (2012) and for the high and low quantiles of the stock-bond correlation, cf. Aslanidis and Christiansen (2010).…”

Section: Data Descriptionmentioning

confidence: 99%

“…The yield spread is also a useful predictor for the stock-bond relationship, cf. Viceira (2012) and Aslanidis and Christiansen (2010).…”

Section: Data Descriptionmentioning

confidence: 99%

“…Moreover, other studies in the literature also use the short rate in levels, cf. Baele et al (2010) and Viceira (2012). Methodologically, in linear models Campbell and Yogo (2006) show that standard statistical inference can be invalid when the innovations in a persistent regressor are highly correlated with innovations in the dependent variable.…”

Section: Single Transition Variablementioning

confidence: 99%

“…Viceira (2012) argues that the yield spread appears to proxy for business conditions, while the short rate appears to reflect inflation and economic uncertainty. In other words, the yield spread and the short rate capture the information in the GDP and inflation that we leave out in model (iii).…”

Section: Multiple Transition Variablesmentioning

confidence: 99%

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Smooth transition patterns in the realized stock–bond correlation

Aslanidis

Christiansen

2012

Journal of Empirical Finance

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This paper explores the time variation in the stock-bond correlation using high-frequency data. Gradual transitions between regimes of negative and positive stock-bond correlation are well accommodated by the smooth transition regression (STR) model. We find that the regimes are systematically related to movements in financial and to a minor extent macroeconomic transition variables. In particular, the most informative transition variables are the short rate, the yield spread, and the VIX volatility index. Importantly, both in-sample and out-of-sample evaluation criteria show that multiple transition variable STR specifications considerably outperform single transition variable STR models. Our results are robust to different forecast horizons.

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Section: Data Descriptionmentioning

confidence: 99%

“…The yield spread is also a useful predictor for the stock-bond relationship, cf. Viceira (2012) and Aslanidis and Christiansen (2010).…”

Section: Data Descriptionmentioning

confidence: 99%

Section: Single Transition Variablementioning

confidence: 99%

Section: Multiple Transition Variablesmentioning

confidence: 99%

See 2 more Smart Citations

Smooth transition patterns in the realized stock–bond correlation

Aslanidis

Christiansen

2012

Journal of Empirical Finance

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“…the cost estimate for fulfilling the contract, which may be based on different assumptions about the future. Examples are the price of spare parts and the inflation rate on the financial markets (Viceira 2012). Figure 1 shows an example of a cost estimate for fulfilling the contract requirements and the included uncertainty in the form of a forecast range (Tay and Wallis 2000).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in competitive bidding – a framework for product–service systems

Kreye

Newnes

Goh

2012

Production Planning & Control

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Owing to servitisation, manufacturing companies are increasingly required to compete through the provision of services around their products. The contracts for these services are often allocated through competitive bidding where the potential suppliers submit a price bid to the customer. The pricing decision is influenced by various uncertainties. This article proposes a conceptual framework depicting these influencing uncertainties on the bidding strategy. This framework is based on three empirical studies with industry investigating different viewpoints on the decision-making process. The intention is to support the pricing decision when competitively bidding for a service contract. The framework can be applied to specific competitive bidding situations to identify the influencing uncertainties, model them and depict their influences on the pricing decision.

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Predictable Return Distributions

Pedersen

2015

Journal of Forecasting

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Using quantile regression this paper explores the predictability of the stock and bond return distributions as a function of economic state variables. The use of quantile regression allows us to examine specific parts of the return distribution such as the tails and the center, and for a sufficiently fine grid of quantiles we can trace out the entire distribution. A univariate quantile regression model is used to examine the marginal stock and bond return distributions, while a multivariate model is used to capture their joint distribution. An empirical analysis on US data shows that economic state variables predict the stock and bond return distributions in quite different ways in terms of, for example, location shifts, volatility and skewness. Comparing the different economic state variables in terms of their out-of-sample forecasting performance, the empirical analysis also shows that the relative accuracy of the state variables varies across the return distribution. Density forecasts based on an assumed normal distribution with forecasted mean and variance is compared to forecasts based on quantile estimates and, in general, the latter yields the best performance. Copyright

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Bond risk, bond return volatility, and the term structure of interest rates

Cited by 110 publications

References 81 publications

Smooth transition patterns in the realized stock–bond correlation

Smooth transition patterns in the realized stock–bond correlation

Uncertainty in competitive bidding – a framework for product–service systems

Predictable Return Distributions

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