CAIR’s asset pricing research improves the understanding of the fundamental risk factors driving the pricing dynamics of major asset classes, including stocks, bonds and derivatives. Publications not only appear in world leading academic journals, but our research attracts high-profile practitioner interest, evidenced by dedicated commentaries in outlets such as Forbes and Institutional Investor.
- the role of real option models, financial uncertainty and macroeconomic risk factors in determining cross-sectional stock returns;
- the impact of risk, uncertainty and non-expected utility preferences on macroeconomic activity and financial markets;
- the analysis of funding flows, trading costs and liquidity risk on investment performance, and the development and application of cutting-edge econometric methods in estimating and comparing structural asset pricing models.
Our research on asset pricing
Real Options Models of the Firm, Capacity Overhang, and the Cross-Section of Stock Returns
Authors: Aretz, Kevin; Pope, Peter F.
Journal: Journal of Finance, Vol. 73, No. 3
In a paper with Peter Pope (London School of Economics), Kevin Aretz uses a stochastic frontier model to estimate a state variable suggested by the models to drive the anomalies: “capacity overhang,” the difference between a firm’s actual installed capacity and the optimal capacity. Aretz and Pope show that the estimate helps explain momentum and profitability, but not value and investment anomalies.
Do Stock Returns Really Decrease with Default Risk? New International Evidence
Authors: Aretz, Kevin; Florackis, Chris; Kostakis, Alexandros.
Journal: MANAGEMENT SCIENCE, Vol. 64, No. 8.
This research focuses on the “distress risk anomaly,” the empirical finding that US stocks with a high failure probability have lower mean future returns than safe US stocks with a low failure probability. Using hand-collected bankruptcy filing data for firms from 14 non-US countries, the research shows that the distress risk anomaly does not exist outside of the US. Outside of the US, mean future returns significantly increase with the failure probability.
Does Smooth Ambiguity Matter for Asset Pricing?
Authors: Liu, H., Gallant, A. R. & Jahan-Parvar, M.
Journal: REVIEW OF FINANCIAL STUDIES. 32, p. 3617–3666, 2019.
Using the Bayesian method introduced by Gallant and McCulloch (2009) to estimate consumption-based asset pricing models featuring smooth ambiguity preferences. The researchers rely on semi-nonparametric estimation of a flexible auxiliary model in their structural estimation. Based on the market and aggregate consumption data, the estimation provides statistical support for asset pricing models with smooth ambiguity. Statistical model comparison shows that models with ambiguity, learning, and time-varying volatility are preferred to the long-run risk model. In the paper asset pricing implications of the estimated models are also analysed.
A new predictor of U.S. real economic activity: The S&P 500 option implied risk aversion
Authors: Faccini, Renato ; Konstantinidi, Eirini; Skiadopoulos, George; Sarantopoulou-Chiourea, Sylvia.
Journal:: MANAGEMENT SCIENCE, 2018.
Can Options Market Activity Predict Economic Growth? In this paper the authors show that risk aversion by participants in the U.S. options market predicts future real economic activity once it is estimated from the market prices of index options that trade in highly liquid option markets. The study also showed this link in the South Korean options market.
Authors: Caporin, M., Kolokolov, A. & Renò, R.
Journal : JOURNAL OF FINANCIAL ECONOMICS. 126, 3, p. 563-591, 2017.
The simultaneous occurrence of jumps in several stocks can be associated with major financial news, triggers short-term predictability in stock returns, is correlated with sudden spikes of the variance risk premium, and determines a persistent increase (decrease) of stock variances and correlations when they come along with bad (good) news. These systemic events and their implications can be easily overlooked by traditional univariate jump statistics applied to stock indices. They are instead revealed in a clearly cut way by using a novel test procedure applied to individual assets, which is particularly effective on high-volume stocks.