We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short- term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps...

We use the database leak of Mt. Gox exchange to analyze the dynamics of the price of bitcoin from June 2011 to November 2013. This gives us a rare opportunity to study an emerging retail-focused, highly speculative and unregulated market with trader identifiers at a tick transaction level. Jumps...

We propose a technique to avoid spurious detections of jumps in high-frequency data via an explicit thresholding on available test statistics. We prove that it eliminates asymptotically all spurious detections. Monte Carlo results show that it performs also well in finite samples. In Dow Jones...

We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes...

We investigate the performance of non-linear return prediction models in the high complexity regime, i.e., when the number of model parameters exceeds the number of observations. We document a "virtue of complexity" in all asset classes that we study (US equities, international equities, bonds,...

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 average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best...

Using data for the 1978-2008 period, this study presents evidence for cointegration between securitized (NAREIT) and direct (NCREIF) total return indices. Cointegration between the indices indicates that REITs and direct real estate are substitutable in the portfolio of a long-horizon...

We introduce a new measure of activity of financial markets that provides a direct access to their level of endogeneity. This measure quantifies how much of price changes are due to endogenous feedback processes, as opposed to exogenous news. For this, we calibrate the self-excited conditional...

We use sector level REIT and transaction-based direct real estate data for the U.S. to provide a clearer understanding of the dynamic relations between public and private real estate returns. We exclude leverage from REIT returns to make the REIT data more comparable with the direct market data....

We introduce a model of super-exponential financial bubbles with two assets (risky and risk-free), in which fundamentalist and chartist traders co-exist. Fundamentalists form expectations on the return and risk of a risky asset and maximize their constant relative risk aversion expected utility...