We develop a network-based vector autoregressive approach to uncover the interactions amongfinancial assets by integrating multiple realized measures based on high-frequency data. Undera restricted parameter structure, our approach allows the capture of cross-sectional and time ependencies...

This paper proposes computational framework for empirical estimation of Financial Agent-Based Models (FABMs) that does not rely upon restrictive theoretical assumptions. We customise a recent methodology of the Non-Parametric Simulated Maximum Likelihood Estimator (NPSMLE) based on kernel...

This paper proposes a general computational framework for empirical estimation of financial agent-based models, for which criterion functions have unknown analytical form. For this purpose, we adapt a recently developed nonparametric simulated maximum likelihood estimation based on kernel...

We propose a coherent inference model that is obtained by distorting the prior density in Bayes' rule and replacing the likelihood with a so-called pseudo-likelihood. This model includes the existing non-Bayesian inference models as special cases and implies new models of base-rate neglect and...

We provide plausibly identified evidence for the role of investor disagreement in asset pricing. Our natural experiment exploits the staggered implementation of EDGAR, which induces a reduction in investor disagreement with no accompanying changes in company fundamentals, disclosure quality, or...

We make use of the extant testing methodology of Barndorff-Nielsen and Shephard (2006) and Aït-Sahalia and Jacod (2009a,b,c) to examine the importance of jumps, and in particular large and small jumps, using high frequency price returns on 25 stocks in the DOW 30 and S&P futures index. In...

The topic of volatility measurement and estimation is central to financial and more generally time series econometrics. In this paper, we begin by surveying models of volatility, both discrete and continuous, and then we summarize some selected empirical findings from the literature. In...

We introduce econometric methods to perform estimation and inference on the permanent and transitory components of the stochastic discount factor (SDF) in dynamic Markov environments. The approach is nonparametric in that it does not impose parametric restrictions on the law of motion of the...

We perform a large simulation study to examine the extent to which various generalized autoregressive conditional heteroskedasticity (GARCH) models capture extreme events in stock market returns. We estimate Hill's tail indexes for individual S&P 500 stock market returns ranging from 1995-2014...