We investigate the effects of margining, a widely-used mechanism to attach collateral to derivatives contracts, on derivatives' trading volume, default risk, and on the welfare in the banking sector. First, we develop a stylized banking sector equilibrium model to derive a set of testable...

We present a simple agent-based model to study how the proximate triggering factor of a crash or a rally might relate to its fundamental mechanism, and vice versa. Our agents form opinions and invest, based on three sources of information, (i) public information, i.e. news; (ii) information from...

We study a number of large international military conflicts since World War II where we establish a news analysis as a proxy for the estimated likelihood that the conflict will result in a war. We find that in cases when there is a pre-war phase, an increase in the war likelihood tends to...

This paper deals with efficient estimation in exchangeable nonlinear dynamic panel models with common unobservable factor. The specification accounts for both micro- and macro-dynamics, induced by the lagged individual observation and the common stochastic factor, respectively. For large...

The recursive prediction and filtering formulas of the Kalman filter are difficult to implement in nonlinear state space models. For Gaussian linear state space models, or for models with qualitative state variables, the recursive formulas of the filter require the updating of a finite number of...

This study investigates reference-dependent choice with a stochastic, state-dependent reference point. The optimal reference-dependent solution equals the optimal consumption solution (no loss aversion) if the reference point is selected fully endogenously. Given that loss aversion is...

We develop new higher-order asymptotic techniques for the Gaussian maximum likelihood estimator of the parameters in a spatial panel data model, with fixed effects, time-varying covariates, and spatially correlated errors. We introduce a new saddlepoint density and tail area approximation to...

This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

This paper proposes a robust semiparametric bootstrap method to estimate predictive distributions of GARCH-type models. The method is based on a robust estimation of parametric GARCH models and a robustified resampling scheme for GARCH residuals that controls bootstrap instability due to...

This chapter surveys recent econometric methodologies for inference in large dimensional conditional factor models in finance. Changes in the business cycle and asset characteristics induce time variation in factor loadings and risk premia to be accounted for. The growing trend in the use of...