The high cost of capital for firms conducting medical research and development (R&D) has been partly attributed to the government risk facing investors in medical innovation. This risk slows down medical innovation because investors must be compensated for it. We propose new and simple financial instruments, Food and Drug Administration (FDA) hedges, to allow medical R&D investors to better share the pipeline risk associated with FDA approval with broader capital markets.
G13: Contingent Pricing; Futures Pricing; option pricing
The purpose of this paper is to develop a model framework for the analysis of interactions between banking sector risk, sovereign risk, corporate sector risk, real economic activity, and credit growth for 15 European countries and the United States. It is an integrated macroeconomic systemic risk model framework that draws on the advantages of forward-looking contingent claims analysis (CCA) risk indicators for the banking systems in each country, forward-looking CCA risk indicators for sovereigns, and a GVAR model to combine the banking, the sovereign, and the macro sphere.
The recent global financial crisis has forced a re-examination of risk transmission in the financial sector and how it affects financial stability. Current macroprudential policy and surveillance (MPS) efforts are aimed establishing a regulatory framework that helps mitigate the risk from systemic linkages with a view towards enhancing the resilience of the financial sector.
This paper offers an ambiguity-based interpretation of variance premium—the difference between risk-neutral and objective expectations of market return variance—as a compounding effect of both belief distortion and variance differential regarding the uncertain economic regimes. Our approach endogenously generates variance premium without imposing exogenous stochastic volatility or jumps in consumption process. Such a framework can reasonably match the mean variance premium as well as the mean equity premium, equity volatility, and the mean risk-free rate in the data.