Frequency Dependent Risk

Andreas Neuhierl, Rasmus T. Varneskov

Publikation: KonferencebidragPaperForskningpeer review

Resumé

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.
OriginalsprogEngelsk
Publikationsdato2020
Antal sider52
StatusUdgivet - 2020
BegivenhedThe 80th Annual Meeting of American Finance Association. AFA 2020 - San Diego, USA
Varighed: 3 jan. 20205 jan. 2020
Konferencens nummer: 80
https://afajof.org/annual-meeting/

Konference

KonferenceThe 80th Annual Meeting of American Finance Association. AFA 2020
Nummer80
LandUSA
BySan Diego
Periode03/01/202005/01/2020
Internetadresse

Emneord

  • Asset pricing
  • Factor models
  • Nonparametric measures
  • Spectral analysis

Citer dette

Neuhierl, A., & Varneskov, R. T. (2020). Frequency Dependent Risk. Afhandling præsenteret på The 80th Annual Meeting of American Finance Association. AFA 2020, San Diego, USA.
Neuhierl, Andreas ; Varneskov, Rasmus T. / Frequency Dependent Risk. Afhandling præsenteret på The 80th Annual Meeting of American Finance Association. AFA 2020, San Diego, USA.52 s.
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Neuhierl, A & Varneskov, RT 2020, 'Frequency Dependent Risk' Paper fremlagt ved The 80th Annual Meeting of American Finance Association. AFA 2020, San Diego, USA, 03/01/2020 - 05/01/2020, .

Frequency Dependent Risk. / Neuhierl, Andreas; Varneskov, Rasmus T.

2020. Afhandling præsenteret på The 80th Annual Meeting of American Finance Association. AFA 2020, San Diego, USA.

Publikation: KonferencebidragPaperForskningpeer review

TY - CONF

T1 - Frequency Dependent Risk

AU - Neuhierl, Andreas

AU - Varneskov, Rasmus T.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Asset pricing

KW - Factor models

KW - Nonparametric measures

KW - Spectral analysis

KW - Asset pricing

KW - Factor models

KW - Nonparametric measures

KW - Spectral analysis

M3 - Paper

ER -

Neuhierl A, Varneskov RT. Frequency Dependent Risk. 2020. Afhandling præsenteret på The 80th Annual Meeting of American Finance Association. AFA 2020, San Diego, USA.