Members of the Development and Economic History Research Group combine archival data, lab-in-the-field experiments, randomized controlled trials, text analysis, survey and secondary data along with theoretical tools to study issues in development and economic history. Faculty and students work in the field in South Asia, China and Africa as well as doing archival work in libraries across Europe and Asia.
Almost all faculty are members of CAGE in the economics department and some are also members of Warwick Interdisciplinary Centre for International Development (WICID). There is a regular weekly external seminar, two weekly internal workshops, and high quality PhD students. We also organise international conferences on campus, or in Venice.
Development and Economic History Research Group Workshop/Seminar
For faculty and PhD students at Warwick and other top-level academic institutions across the world. For a detailed scheduled of speakers please follow the link below.
Organisers: Yannick Dupraz
Academics associated with the Development and Economic History Research Group are:
CWIP Lunchtime Workshop - Eric Renault
Title of talk is Identification Robust Inference for Risk Prices in Structural Stochastic Volatility Models.
Co-authors : Xu Cheng and Paul Sangrey (University of Pennsylvania)
Abstract: In structural stochastic volatility asset pricing models, changes in volatility affect risk premia through two channels: (1) the investor’s willingness to bear high volatility in order to get high expected returns as measured by the market return risk price, and (2) the investor’s direct aversion to changes in future volatility as measured by the volatility risk price. Disentangling these channels is difficult and poses a subtle identification problem that invalidates standard inference. We adopt the discrete-time exponentially affine model of Han, Khrapov, and Renault (2018), which links the identification of the volatility risk price to the leverage effect. In particular, we develop a minimum distance criterion that links the market return risk price, the volatility risk price, and the leverage effect to well-behaved reduced-form parameters that govern the return and volatility’s joint distribution. The link functions are almost flat if the leverage effect is close to zero, making estimating the volatility risk price difficult. We translate the conditional quasi-likelihood ratio test that Andrews and Mikusheva (2016) develop in a nonlinear GMM framework to a minimum distance framework. The resulting conditional quasi-likelihood ratio test is uniformly valid. We invert this test to derive robust confidence sets that provide correct coverage for the risk prices regardless of the leverage effect’s magnitude.