We study the investor beliefs, sentiment and disagreement, about stock market returns during the COVID-19 pandemic using a large number of messages of investors -- about 3.7 million messages -- on a social media investing platform,StockTwits. The rich and multimodal features of StockTwits data...

To facilitate crossing from the "black box" to "glass box" in the application of neural networks (NNs), we develop a variable significant test for the multi-layer perceptrons. To derive the test statistic and its asymptotic distribution, we provide the consistency of the multi-layer perceptrons...

This paper investigates the performance of different parametric models, stable and tempered stable distributions, for capturing the tail behaviour of the log-returns. We first define and discuss the properties of the stable and tempered stable random variables. We then show how to estimate their...

We develop a multivariate Lévy model and apply the bivariate model for the pricing of quanto options that captures three characteristics observed in real-world markets for stock prices and currencies: jumps, heavy tails and skewness. The model is developed by using a bottom-up approach from a...

We develop a multivariate Lévy model and apply the bivariate model for the pricing of quanto options that captures three characteristics observed in real-world markets for stock prices and currencies: jumps, heavy tails and skewness. The model is developed by using a bottom-up approach from a...

If the closed-form formula for the probability density function is not available, implementing the maximum likelihood estimation is challenging. We introduce a simple, fast, and accurate way for the estimation of numerous distributions that belong to the class of tempered stable probability...

Carr and Wu (2004), henceforth CW, developed a framework that encompasses almost all of the continuous-time models proposed in the option pricing literature. Their framework hinges on the stopping time property of the time changes. By ana- lyzing the measurability of the time changes with...

To facilitate crossing from the "black box" to "glass box" in the application of neural net- works, we extend Horel and Giesecke (2020) and develop a variable/feature significant test for multi-layer perceptrons (MLP). The proposed test permits one to assess the statistical significance of the...

Social interaction and information transmission are essential components of pricing and trading in financial markets. To investigate the behavior contagion and information cascades among investors and sectors, we deploy a jump-diffusion process on investor sentiment -- a novel dataset from...

We empirically study sources of abnormal changes, henceforth jumps, simultaneously in investor beliefs and asset prices using 164 million tweets from a social media investing platform, StockTwits. Before the COVID-19 pandemic, we find that on average 4.88% (7.88%) jumps in asset prices (investor...