Oil Prices and Long-Run Risk

Ready, Robert

doi:10.2139/ssrn.1720502

Cited by 12 publications

(6 citation statements)

References 51 publications

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“…In that sense, the paper is best viewed as a thorough attempt to study how oil risks are linked to oil and nonoil securities rather than as a prespecified factor model like that of Chen, Roll, and Ross (). Our approach and findings complement recent work that explicitly models the role of oil in the pricing of securities (see Baker and Routledge () and Ready (, )).…”

supporting

confidence: 58%

“…In Baker and Routledge (), the dynamics are a function of risk‐sharing among agents while in Ready () they result from supply fluctuations. Our decomposition better resembles the supply interpretation in Ready (), where long‐run, short‐run, and volatility fluctuations in supply can be priced differently in equilibrium. Ready () provides evidence complementary to ours for the importance of oil supply shocks in the cross‐section of returns.…”

mentioning

confidence: 99%

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Estimating Oil Risk Factors Using Information from Equity and Derivatives Markets

Chiang¹,

Hughen²,

Sagi

2015

The Journal of Finance

106

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We introduce a novel approach to estimating latent oil risk factors and establish their significance in pricing nonoil securities. Our model, which features four factors with simple economic interpretations, is estimated using both derivative prices and oil-related equity returns. The fit is excellent in and out of sample. The extracted oil factors carry significant risk premia, and are significantly related to macroeconomic variables as well as portfolio returns sorted on characteristics and industry. The average nonoil portfolio exhibits a sensitivity to the oil factors amounting to a sixth (in magnitude) of that of the oil industry itself.FEW, IF ANY, COMMODITIES have been the focus of more attention for their perceived economic significance than oil. While there is strong evidence relating oil prices to the business cycle, the nature of the relationship is nonlinear, timevarying, and difficult to attribute to any single source such as political uncertainty, cartel decisions, or global economic conditions (see Hamilton (2003) and Barsky and Kilian (2004)). Despite its prominence in the business media and economics literature, and despite the well-documented role of business cycles in asset pricing, academic research has largely failed to find consistent evidence that oil is an important determinant of cross-sectional asset prices.1 This paper introduces a new model and method to estimate latent oil risk factors using

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supporting

confidence: 58%

mentioning

confidence: 99%

Estimating Oil Risk Factors Using Information from Equity and Derivatives Markets

Chiang¹,

Hughen²,

Sagi

2015

The Journal of Finance

106

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“…For a longterm investment, the sign of the roll return is commonly the dominant determinant of the total index return. 5 Ghoddusi [2012] and Ready [2012] provide long-run risks asset pricing models for commodities. The roll return is positive (negative) in the case where the term structure of commodity futures prices is downward (upward) sloping; these two types of slope of the term structure of futures prices are termed backwardation and contango, respectively.…”

Section: Endnotesmentioning

confidence: 99%

Advances in the Commodity Futures Literature: A Review

Skiadopoulos

2013

JOD

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“…While most of this literature is based on additive utility specifications, Dunn and Singleton (1986), using term structure data, find evidence against a specification of expected non-separable utility over durables and non-durables. Further, while most of the literature, including our paper, relies on homothetic preferences, Pakos (2011), Pakos (2005) and Ready (2010) argue for non-homothetic preferences to capture the interaction between the two goods in the data.…”

Section: Introductionmentioning

confidence: 96%

“…We use a standard specification of preferences which features a homothetic utility function and constant preference weights to the consumption goods Pakos (2005). andReady (2010) consider the extension of the model to non-homethetic preferences and show its implications for the equilibrium asset prices.…”

mentioning

confidence: 99%

Durable Goods, Inflation Risk, and Equilibrium Asset Prices

2015

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High inflation predicts a decline in future real consumption and equity cashflows, which is significantly stronger for durable than for non-durable goods. This suggests that durables is an important channel through which inflation affects long-term economic growth and asset prices. We derive and estimate an equilibrium two-good nominal economy with recursive utility over durable and nondurable consumption and persistent variations in real expected growth rates and expected inflation. Our model can account for the key features of macro data, nominal bond yields and equity prices in durable and nondurable sectors, such as an upward-sloping nominal yield curve, and higher risk premia, volatility and correlations of durable equity returns with expected inflation and bond returns, relative to nondurable equities. The two-good structure, recursive utility and a non-neutral effect of inflation on future growth play the key role to explain these data features.

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Oil Prices and Long-Run Risk

Cited by 12 publications

References 51 publications

Estimating Oil Risk Factors Using Information from Equity and Derivatives Markets

Estimating Oil Risk Factors Using Information from Equity and Derivatives Markets

Advances in the Commodity Futures Literature: A Review

Durable Goods, Inflation Risk, and Equilibrium Asset Prices

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