We model the conditional mean and volatility of stock returns as a latent vector autoregressive (VAR) process to study the contemporaneous and intertemporal relationship between expected returns and risk in a flexible statistical framework and without relying on exogenous predictors. We find a strong and robust negative correlation between the innovations to the conditional moments that leads t...

One important contribution has been made by Benoit Mandelbrot, the famous father of fractals who proposed a multi-fractal model of asset returns (MMAR), a theory which inherits all the hallmarks of Mandelbrot’s earlier work that has emerged since the 1970s. As a new formalization of stochastic models for the volatility dynamics of asset prices, it preserves the hierarchical multiplicative struc...

This paper motivates and introduces a two-stage method for estimating diffusion processes based on discretely sampled observations. In the first stage we make use of the feasible central limit theory for realized volatility, as recently developed in Barndorff-Nielsen and Shephard (2002), to provide a regression model for estimating the parameters in the diffusion function. In the second stage t...

Motivated by the need of a positive-semidefinite estimator of multivariate realized covariance matrices, we model noisy and asynchronous ultra-high-frequency asset prices in a state-space framework with missing data. We then estimate the covariance matrix of the latent states through a Kalman smoother and Expectation Maximization (KEM) algorithm. Iterating between the two EM steps, we obtain a ...

In this paper we consider estimation and inference of common breaks in panel data models via adaptive group fused lasso. We consider two approaches — penalized least squares (PLS) for firstdifferenced models without endogenous regressors, and penalized GMM (PGMM) for first-differenced models with endogeneity. We show that with probability tending to one both methods can correctly determine the ...

We model the conditional mean and volatility of stock returns as a latent vector autoregressive (VAR) process to study the contemporaneous and intertemporal relationship between expected returns and risk in a flexible statistical framework and without relying on exogenous predictors. We find a strong and robust negative correlation between the innovations to the conditional moments that leads t...

We consider a random utility model of strategic network formation, where we derive a tractable approximation to the distribution of network links using many-player asymptotics. Our framework assumes that agents have heterogeneous tastes over links, and allows for anonymous and non-anonymous interaction effects among links. The observed network is assumed to be pairwise stable, and we impose no ...

This paper considers the problem of parameter estimation in a model for a continuous response variable y when an important ordinal explanatory variable x is missing for a large proportion of the sample. Nonmissingness of x, or sample selection, is correlated with the response variable and/or with the unobserved values the ordinal explanatory variable takes when missing. We suggest solving the e...

This paper examines the impact of oil price volatility on macroeconomic performance in fourteen non-oil exporting Sub-Saharan Africa (SSA) economies using panel ARDL model fort he 1980-2015. It also looks at channels through which transmit to three major sectors their economies. The estimate indicates how persistent prevailed economy by measuring short run and long effects. result shows that ec...

We propose a dynamic model for financial market volatility with an heterogeneous structure for three components: continuous volatiilty, leverage and jumps. We find that each of the three components plays a significant role in volatility forecasting and neglecting one of them is detrimental to the forecasting performance. Importantly, we find remarkable forecasting power for the negative past re...