This paper provides a selected review of the recent developments and applications of mixture-of-normal (MN) distribution models in financial econometrics. One noted feature of the MN model is its flexibility in accommodating various shapes of continuous distributions, and its ability in...

As an alternative to quasi-maximum likelihood, targeting estimation is a much applied estimation method for univariate and multivariate GARCH models. In terms of variance targeting estimation recent research has pointed out that at least finite fourth-order moments of the data generating process...

Introduction -- ARMA(p,q) Processes -- Model Selection in ARMA(p,q) processes -- Stationarity and Invertibility -- Non-stationarity and ARIMA(p,d,q) processes -- Seasonal ARMA(p,q) processe -- Unit root tests -- Structural Breaks -- ARCH, GARCH and Time-varying Variance -- Vector Autoregressions...

In this book, the authors reject the theorem-proof approach as much as possible, and emphasize the practical application of econometrics. They show with examples how to calculate and interpret the numerical results. This book begins with students estimating simple univariate models, in a step by...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...