Long-Run Risk and the Persistence of Consumption Shocks

Ortu, Fulvio; Tamoni, Andrea; Tebaldi, Claudio

doi:10.1093/rfs/hht038

Cited by 105 publications

(54 citation statements)

References 52 publications

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“…Finally, we note that the importance of low-frequency risk is consistent with the empirical findings in Berkowitz (2001), Cogley (2001), Calvet & Fisher (2007), Yu (2012), Ortu et al (2013), Dew- and Bandi et al (2018), who document improved model-and/or utility-specification fit, lower pricing errors, tighter risk price estimation and better asset allocation from using low-frequency variation. However, we add to their findings by identifying important components of the state vector nonparametrically using a large cross-section of stocks and by quantifying their associated risk premia.…”

Section: Introductionsupporting

confidence: 87%

“…Yu (2012) studies the co-spectrum between aggregate, or market, returns and consumption growth for the habit model ofCampbell & Cochrane (1999) and the LRR model, finding that the habit model cannot reconcile increased correlation between returns and consumption growth at lower frequencies, whereas the LRR model can. In addition to studying different asset pricing models above, the differences between the present framework andYu (2012) is similar to those decsribed for the frameworks ofCalvet & Fisher (2007),Ortu et al (2013) and in Section 3.3.…”

supporting

confidence: 56%

“…Finally, Calvet & Fisher (2007), Ortu et al (2013), and provide dynamic decompositions of asset returns, consumption growth, and price-dividend ratios based on different time domain methods. In particular, whereas Calvet & Fisher (2007) stipulate that dividends and returns are priced using a Markov-Switching stochastic volatility model, Ortu et al (2013), analyze the dynamics of consumption and CCAPM betas using extended Wold decompositions, thereby providing improved inference for the asset pricing implications of different components of the state vector (consumption, in their setting). Similarly, Bandi et al (2018) decompose the CAPM beta in the frequency domain and demonstrate that by measuring market betas at lower frequencies, this substantially reduces pricing errors for the twenty-five Fama-French portfolios.…”

Section: Relation To the Literaturementioning

confidence: 99%

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Frequency Dependent Risk

Neuhierl

Varneskov

2018

SSRN Journal

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This paper provides a new nonparametric framework for studying the dynamics of the state vector and its associated risk prices. Specifically, in a general setting where the stochastic discount factor (SDF) decomposes into permanent and transitory components, we analyze their contribution to the unconditional asset return premium using frequency domain techniques. We show analytically that the co-spectrum between returns and the SDF only displays frequency dependencies through its transitory component, that is, through the state vector. Moreover, we demonstrate that state vector dynamics and its risk prices can be uncovered by studying (transformations of) the covariance between (portfolios of) asset returns. We introduce two new frequency risk measures and apply our framework to study its pricing in the full cross-section of US stocks, utilizing the market, value, size and momentum factors as baseline portfolios to construct the measures. Our analysis uncovers the existence of, at least, two significantly priced low-frequency risk factors, one of which commands a large positive risk premium of 6% per year. Moreover, we document, at least, one high-frequency component in the state vector that is significantly priced. Importantly, we show that these frequency dependent risk factors are unspanned by a battery of appraised risk factors and characteristics. Our analysis demonstrates that multiple state vector components with varying persistence and risk prices are needed to be consistent with the cross-section. Throughout, we contrast our findings with the implications of the long-run risk model, the dynamic disaster model as well as a regime-switching CCAPM, providing new analytical results for such models. ) similarly decompose betas at different time scales with, however, the focus of testing frequency-specific versions of the CAPM rather than study how dynamic decompositions aid the pricing of returns at the aggregate level (that is, not the individual components of the returns). 7 We will, however, provide several examples of dynamic asset pricing models that are embedded in our framework. 8 This holds approximately in a consumption-based asset pricing setting where the representative agent has power utility and all dividends are consumed, e.g., Munk (2013, Chapters 8 and Theorem 10.4).

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Section: Introductionsupporting

confidence: 87%

supporting

confidence: 56%

Section: Relation To the Literaturementioning

confidence: 99%

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Frequency Dependent Risk

Neuhierl

Varneskov

2018

SSRN Journal

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“…The key reason is that agents operate on different investment horizons -these are associated with various types of investors, trading tools, and strategies that correspond to different trading frequencies (Gençay et al, 2010;Conlon et al, 2016). Shorter or longer frequencies are the result of the frequency-dependent formation of investors' preferences, as shown in the modeling strategies of Bandi and Tamoni (2017); Cogley (2001); Ortu et al (2013). In our analysis, we consider the long-, medium-, and short-term frequency responses to shocks and analyze financial connectedness at a desired frequency band.…”

Section: Introductionmentioning

confidence: 99%

Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets

Baruník¹,

Kočenda²

2019

The Energy Journal

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We analyze total, asymmetric and frequency connectedness between oil and forex markets using high-frequency, intra-day data over the period 2007 -2017. By employing variance decompositions and their spectral representation in combination with realized semivariances to account for asymmetric and frequency connectedness, we obtain interesting results. We show that divergence in monetary policy regimes affects forex volatility spillovers but that adding oil to a forex portfolio decreases the total connectedness of the mixed portfolio. Asymmetries in connectedness are relatively small. While negative shocks dominate forex volatility connectedness, positive shocks prevail when oil and forex markets are assessed jointly. Frequency connectedness is largely driven by uncertainty shocks and to a lesser extent by liquidity shocks, which impact long-term connectedness the most and lead to its dramatic increase during periods of distress.

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“…11 To separate the log price-dividend ratio into the business-cycle and trend components, we employ the persistencebased decomposition suggested in Ortu, Tamoni, and Tebaldi (2013). Importantly, the business cycle component extracted using this decomposition is adapted to time t information and is, therefore, non anticipative.…”

Section: [Insert Figure 2 About Here]mentioning

confidence: 99%

News Shocks and Asset Prices

Malkhozov

Tamoni

2013

SSRN Journal

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We study the importance of anticipated shocks (news) for understanding the comovement between macroeconomic quantities and asset prices. We find that fourquarter anticipated investment shocks are an important source of fluctuations for macroeconomic variables: they account for about half of the variance in hours and investment. However, it is the four-quarter anticipated productivity shock that is driving a large fraction of consumption and most of the price-dividend ratio fluctuations. These productivity news are key for the model to reproduce the empirical tendency for stockmarket valuations and excess returns to lead the business cycle. Importantly, a model that does not use asset price information in the estimation would downplay the role of productivity news; in this case, the model implies that return moves (almost) completely contemporaneously with the economic activity, counterfactually with the data. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means without the prior permission in writing of the publisher nor be issued to the public or circulated in any form other than that in which it is published.Requests for permission to reproduce any article or part of the Working Paper should be sent to the editor at the above address. AbstractWe study the importance of anticipated shocks (news) for understanding the comovement between macroeconomic quantities and asset prices. We find that four-quarter anticipated investment shocks are an important source of fluctuations for macroeconomic variables: they account for about half of the variance in hours and investment. However, it is the four-quarter anticipated productivity shock that is driving a large fraction of consumption and most of the price-dividend ratio fluctuations. These productivity news are key for the model to reproduce the empirical tendency for stock-market valuations and excess returns to lead the business cycle. Importantly, a model that does not use asset price information in the estimation would downplay the role of productivity news; in this case, the model implies that return moves (almost) completely contemporaneously with the economic activity, counterfactually with the data.JEL classification: C22, E32, E44, G12, G17

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Long-Run Risk and the Persistence of Consumption Shocks

Cited by 105 publications

References 52 publications

Frequency Dependent Risk

Frequency Dependent Risk

Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets

News Shocks and Asset Prices

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