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...

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

 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...

In a market with informationally connected traders, the dynamics of volume, price informativeness, price volatility, and liquidity are severely affected by the information linkages every trader experiences with his peers. We show that in the presence of information linkages among traders, volume...

While CBOE’s VIX index is widely acknowledged as a broad-based investor “fear gauge” for its strong inverse relationship with major equity indexes, one cannot necessarily expect it to translate to the level of future turbulence or investor risk-aversion in fixed-income markets. Indeed,...

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 class of parameter estimators for dynamic models, called simulated non-parametric estimators (SNEs). The SNE minimizes appropriate distances between non-parametric conditional (or joint) densities estimated from sample data and non-parametric conditional (or joint)...