This paper documents that option-implied tail risk in the U.S. financial sector predicts real economic activity. The predictability is found to be incremental to the information content in a stock price-based measure of financial sector tail risk. This finding holds both in- and out-of-sample...

We present a numerically efficient approach for machine-learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs and convex trading constraints. This approach can then be used to implement a stochastic implied volatility...

We propose a new model for the variance between multiple time series, the Regime Switching Dynamic Correlation. We decompose the covariances into correlations and standard deviations and the correlation matrix follow a regime switching model; it is constant within a regime but different across...

Using different loss functions in estimation and forecast evaluation of econometric models can cause suboptimal parameter estimates and inaccurate assessment of predictive ability. Though there are not general guidelines on how to choose the loss function, the modeling of Value-at-Risk is a rare...

This paper evaluates the performance of several popular technical trading rules applied to the Australian share market. The optimal trading rule parameter values over the in-sample period of 4/1/82 to 31/12/89 are found using a genetic algorithm. These optimal rules are then evaluated in terms...

We discuss martingales, detrending data, and the efficient market hypothesis for stochastic processes x(t) with arbitrary diffusion coefficients D(x,t). Beginning with x-independent drift coefficients R(t) we show that Martingale stochastic processes generate uncorrelated, generally...

Market participants often invest in order to acquire information that pertains to the market itself (e.g. order flow) rather than to fundamentals. This enables them to infer more information from past trades. I show that agents trading on such information, typically high-frequency traders,...

Stock market fundamentals would not seem to meaningfully predict returns over a shorter-term horizon - instead, I shift focus to severe downside risk (i.e., crashes). I use the cointegrating relationship between the log S&P Composite Index and log earnings over 1871 to 2015, combined with...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

This article implements the minimum variance frontier for the stochastic discount factor, according to both Hansen and Jagannathan (1991) and Cochrane and Hansen (1992), for the Brazilian stock market. Two approaches are considered in terms of equity returns and equity premium, respectively, the...