We combine self-collected historical data from 1867 to 1907 with CRSP data from 1926 to 2012, to examine the risk and return over the past 140 years of one of the most popular mechanical trading strategies - momentum. We find that momentum has earned abnormally high risk-adjusted returns - a...

We use the GARCH-MIDAS model to extract the long- and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We find that S&P 500...

We examine price discovery and liquidity provision in the secondary market for bitcoin-an asset with a high level of speculative trading. Based on BTC-e's full limit order book over the 2013-2014 period, we find that order informativeness increases with order aggressiveness within the first 10...

We provide an innovative methodological contribution to the measurement of returns on infrequently traded assets using a novel approach to repeat-sales regression estimation. The model for price indices we propose allows for correlation with other markets, typically with higher liquidity and...

We show that using data which are properly available in real time when assessing the sensitivity of asset prices to economic news leads to different empirical findings than when data availability and timing issues are ignored. We do this by focusing on a particular example, namely Chen, Roll and...

The early work of Tobin (1958) showed that portfolio allocation decisions can be reduced to a two stage process: first decide the relative allocation of assets across the risky assets, and second decide how to divide total wealth between the risky assets and the safe asset. This so called...

We provide empirical evidence for the existence, magnitude, and economic impact of stigma associated with banks borrowing from the Federal Reserve's discount window facility. We find that, during the height of the financial crisis, banks were willing to pay an average premium of at least 37...

This paper proposes a new approach to extract quantile-based inflation risk measures using Quantile Autoregressive Distributed Lag Mixed-Frequency Data Sampling (QADL-MIDAS) regression models. We compare our models to a standard Quantile Auto-Regression (QAR) model and show that it delivers...