Title: Inverse Leverage Effect: From Cryptocurrency to Meme Stocks

Abstract: In the existing continuous time finance literature, most financial assets exhibit a traditional leverage effect; i.e., a negative correlation between an asset's price and its volatility.  In this chapter, we propose that assets which are highly speculative in nature exhibit an inverse leverage effect, or a positive correlation between its price and volatility.  We propose to model these highly speculative assets jointly with the S&P 500 to a stochastic volatility model with bivariate asymmetric Laplacian (ALD) jumps in returns that is flexible to allow for both independent jump times and contemporaneous jump times.  Monte Carlo Markov Chain (MCMC) methods with the particle Gibbs with ancestor sampling (PGAS) algorithm are developed to estimate the model parameters and latent state variables, such as SV, jump times, jump sizes, and is validated through simulation studies.  The algorithm is validated through simulation studies. The method is applied to fit daily returns of S&P 500 paired with an array of speculative stocks including Bitcoin, Dogecoin, AMC, and Gamestop.  We find strong evidence of an inverse leverage effect across these stocks that is robust to distributional assumptions.