This paper introduces a no-arbitrage framework to assess how macroeconomic factors help explain the risk-premium agents require to bear the risk of fluctuations in stock market volatility. We develop a model in which return volatility and volatility risk-premia are stochastic and derive...

This paper introduces a no-arbitrage framework to assess how macroeconomic factors help explain the risk-premium agents require to bear the risk of fluctuations in stock market volatility. We develop a model in which stock volatility and volatility risk-premia are stochastic and derive...

How does stock market volatility relate to the business cycle? We develop, and estimate, a no-arbitrage model, and find that (i) the level and fluctuations of stock volatility are largely explained by business cycle factors and (ii) some unobserved factor contributes to nearly 20% to the overall...

This paper introduces a no-arbitrage framework to assess how macroeconomic factors help explain the risk-premium agents require to bear the risk of fluctuations in stock market volatil- ity. We develop a model in which return volatility and volatility risk-premia are stochastic and derive...

Does capital markets uncertainty affect the business cycle? We find that financial volatility predicts 30% of post-war economic activity in the United States, and that during the Great Moderation, aggregate stock market volatility explains, alone, up to 55% of real growth. In out-of-sample...

This paper studies asset markets in which ambiguity averse investors face Knightian uncertainty about expected payoffs. The same investors, however, might wish to resolve their uncertainty, although not risk, by just purchasing information. In these markets, uninformed and, hence, ambiguity...

Which pricing kernel restrictions are needed to make low dimensional Markov models consistent with given sets of predictions on aggregate stock-market fluctuations ? This paper develops theoretical test conditions addressing this and related reverse engineering issues arising within a fairly...

 This paper introduces a new parameter estimator of dynamic models in which the state is a multidimensional, continuous-time, partially observed Markov process. The estimator minimizes appropriate distances between nonparametric joint (and/or conditional) densities of sample data and...