Why Does the Yield Curve Predict Output and Inflation?

Estrella, Arturo

doi:10.1111/j.1468-0297.2005.01017.x

Cited by 232 publications

(204 citation statements)

References 46 publications

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“…The former suggests that the economy is more likely to be in state 1 in quarter t when short-term interest rates were high in quarter t −1, and the latter suggests that the economy is more likely to be in state 1 in quarter t when the yield curve was steep in quarter t −1 . Shortterm interest rates and the slope of the yield curve have each been shown to be important predictors of future inflation (e.g., Fama, 1975;Estrella and Hardouvelis, 1991;Estrella, 2005 ). Thus, the results for these two indicators are consistent with the results reported earlier for INFL t-1 and FED t-1 in that they suggest that the economy is more likely to be in state 1 when market participants expect a higher inflationary environment.…”

Section: St At Esupporting

confidence: 82%

“…From prior research, we identify six financial market indicators that may be useful for predicting whether the economy is in state 1 in quarter t ( Estrella and Hardouvelis, 1991;Gertler et al, 1991;Kashyap et al, 1994;Campbell and Vuolteenaho, 2004;Estrella, 2005;Hahn and Lee, 2006;Petkova, 2006 ). These indicators are: (1) TERM_SPREAD t-1 , the average difference between the 10-year and short-term US government bond yields throughout quarter t −1, (2) T_BILL t-1 , the average short-term interest rate throughout quarter t −1, (3) DEFAULT_SPREAD t-1 , the average difference between the BAA and AAA-rated corporate bond yields throughout quarter t −1, (4) NFCI t-1 , the average financial market conditions throughout quarter t −1, as measured by The Chicago Federal Reserve's National Financial Conditions Index, (5) VALUE_SPREAD t-1 , the average natural log of the ratio of the book-to-market ratios of small value firms to small growth firms, (6) VOLATILITY t-1 , market return volatility during quarter t −1, estimated as the annualized standard deviation of daily returns on the CRSP value-weighted market index in quarter t −1.…”

Section: St At Ementioning

confidence: 99%

See 1 more Smart Citation

Aggregate earnings and stock market returns: The good, the bad, and the state-dependent

Zolotoy¹,

Frederickson²,

Lyon

2017

Journal of Banking & Finance

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a b s t r a c tPrior research documents a negative aggregate earnings-returns relation. In contrast, we posit that the sign of the relation varies, depending upon the macroeconomic and financial market conditions that exist in the earnings announcement quarter. We argue that the existing macroeconomic and financial market conditions influence market participants' frame of reference, which in turn affects whether they interpret aggregate earnings surprises to be informative about the expected inflation component of the discount rate, the market risk premium component of the discount rate, or aggregate future cash flows. Consistent with this, we find that the sign of the aggregate earnings-returns relation changes numerous times across our sample period. We also find that market participants interpret aggregate earnings to be informative about changes in expected inflation (market risk premium) when the sign of the aggregate earningsreturns relation is negative (positive). Finally, we identify macroeconomic and financial market conditions under which the aggregate earnings-returns relation is more (less) likely to be negative (positive).

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Section: St At Esupporting

confidence: 82%

Section: St At Ementioning

confidence: 99%

Aggregate earnings and stock market returns: The good, the bad, and the state-dependent

Zolotoy¹,

Frederickson²,

Lyon

2017

Journal of Banking & Finance

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“…The yield curve is often viewed as a useful predictor of real economic activity (Estrella and Hardouvelis, 1993;Estrella, 2007;Fornari and Lemke, 2010). A flattening of the yield curve, either through contractionary monetary policy or through expectations of lower inflation, typically points to a deterioration of economic activity in the future.…”

Section: Addressing Model Uncertaintymentioning

confidence: 99%

Leading indicators of crisis incidence: Evidence from developed countries

Babecký

Havránek

Matějů

et al. 2013

Journal of International Money and Finance

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MaRs is to develop core conceptual frameworks, models and/or tools supporting macro-prudential supervision in the EU. The research is carried out in three work streams: 1) Macro-financial models linking financial stability and the performance of the economy; 2) Early warning systems and systemic risk indicators; 3) Assessing contagion risks. MaRs is chaired by Philipp Hartmann (ECB). Paolo Angelini (Banca d'Italia), Laurent Clerc (Banque de France), Carsten Detken (ECB), Cornelia Holthausen (ECB) and Katerina Šmídková (Czech National Bank) are workstream coordinators. Xavier Freixas (Universitat Pompeu Fabra) and Hans Degryse (Katholieke Universiteit Leuven and Tilburg University) act as external consultant. Angela Maddaloni (ECB) and Kalin Nikolov (ECB) share responsibility for the MaRs Secretariat. The refereeing process of this paper has been coordinated by a team composed of Cornelia Holthausen, Kalin Nikolov and Bernd Schwaab (all ECB). The paper is released in order to make the research of MaRs generally available, in preliminary form, to encourage comments and suggestions prior to final publication. The views expressed in the paper are the ones of the author(s) and do not necessarily reflect those of the ECB or of the ESCB. Acknowledgements 1 AbstractWe search for early warning indicators that could indicate important risks in developed economies. We therefore examine which indicators are most useful in explaining costly macroeconomic developments following the occurrence of economic crises in EU and OECD countries between 1970 and 2010. To define our dependent variable, we bring together a (continuous) measure of crisis incidence, which combines the output and employment loss and the fiscal deficit into an index of real costs, with a (discrete) database of crisis occurrence. In contrast to recent studies, we explicitly take into account model uncertainty in two steps. First, for each potential leading indicator, we select the relevant prediction horizon by using panel vector autoregression. Second, we identify the most useful leading indicators with Bayesian model averaging. Our results suggest that domestic housing prices, share prices, and credit growth, and some global variables, such as private credit, are risk factors worth monitoring in developed economies. When the global turmoil demonstrated that developed economies can be also among those hit significantly by a crisis, it was not obvious whether the previously identified early warning indicators should be employed again. We try to take up this challenge and contribute to the early warning literature by focusing solely on developed economies and by offering several methodological improvements for explaining crisis incidence.In the first step, we refine the measure of the real costs of crises to the economy by combining a continuous index of real costs with a binary index of crisis occurrence. The continuous index reflects the output and employment loss along with the fiscal deficit, while the binary index captures the occurrence of various (bank...

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“…27 For instance, the information content of the output gap (which suffers from the additional complication that it is unobservable and needs to be estimated) is likely to depend on the relative importance of shocks to aggregate demand and supply. 28 Similarly, the information content of the slope of the term structure of interest rates may depend on the nature of the monetary policy regime, as demonstrated by Estrella (2003).…”

mentioning

confidence: 99%