Based on the sensitivity of stocks to changes in winning probabilities observed before the election, we show how the stock market’s assessment of the unobserved postelection winning probabilities can be backed out from stock prices.
In this paper, we develop a novel, intuitive and objective measure of time-varying parameter uncertainty (PU) based on a simple statistical test.
We examine the effect of populism on financial markets around national elections.
We show that parameter uncertainty based on the turbulence withing each cross-section of factor portfolios produces a significant out-of-sample forecast for six out of seven tested Fama-French risk factors.
Based on a multidisciplinary literature review, we discuss the assumptions implicit in the prevalent Black-Scholes model and argue for relaxed assumptions that better represent characteristics of uncertain IT projects.