The Introduction of Bitcoin Futures: An Examination of Volatility and Potential Spillover Effects

Theory in Stein (1987) suggests that introducing derivative contracts, such as futures, can destabilize underlying asset prices if the contracts attract enough speculative traders. This paper examines how the introduction of Bitcoin futures influences the underlying Bitcoin market. Consistent with Stein (1987), we find that that Bitcoin\u27s volatility increases significantly during the post-introduction period. Perhaps more importantly, however, we observe significant spillover effects into related markets. For instance, in other cryptocurrencies, the increase in volatility in these markets is greater than the post-introduction increase in Bitcoin

PP-waves on Superbrane Backgrounds

In this paper we discuss a method of generating supersymmetric solutions of the Einstein equations. The method involves the embedding of one supersymmetric spacetime into another. We present two examples with constituent spacetimes which support "charges", one of which was known previously and the other of which is new. Both examples have PP-waves as one of the embedding constituents.Comment: 6 pages no figure

The Information Content of Option Ratios

A broad stream of research shows that information flows into underlying stock prices through the options market. For instance, prior research shows that both the Put–Call Ratio (P/C) and the Option-to-Stock Volume Ratio (O/S) predict negative future stock returns. In this paper, we compare the level of information contained in these two commonly used option volume ratios. First, we find that P/C ratios contain more predictability about future stock returns at the daily level than O/S ratios. Second, in contrast to our first set of results, O/S ratios contain more predictability about future returns at the weekly and monthly levels than P/C ratios. In fact, our tests show that while P/C ratios contain predictability about future daily returns and, to some extent, future weekly returns, the return predictability in P/C ratios is fleeting. O/S ratios, on the other hand, significantly predict negative returns at all levels: daily, weekly, and monthly. While Pan and Poteshman (2006) show that signed P/C ratios, which require proprietary data, have predictive power, we find that unsigned P/C ratios, which do not require proprietary data, also have predictive power

Comovement in the Cryptocurrency Market

This study examines the comovement between 17 of the most active cryptocurrencies. We are unable to statistically reject the presence of perfect comovement between Bitcoin and six of the 16 non-Bitcoin cryptocurrencies. Consistent with the friction-based explanation for the presence of comovement, once the CBOE introduced futures contracts on Bitcoin, we find that all 16 cryptocurrencies comove with Bitcoin. These results suggest that introducing futures contracts improves the informational environment of the entire cryptocurrency market, which helps explain the unusual comovement in the cryptocurrency market

Gambling Preferences, Options Markets, and Volatility

Abstract This study examines whether the gambling behavior of investors affects volume and volatility in financial markets. Focusing on the options market, we find that the ratio of call option volume relative to total option volume is greatest for stocks with return distributions that resemble lotteries. Consistent with the theoretical predictions of Stein (1987), we demonstrate that gambling-motivated trading in the options market influences future spot price volatility. These results not only identify a link between lottery preferences in the stock market and the options market, but they also suggest that lottery preferences can lead to destabilized stock prices

Typology of Web 2.0 spheres: Understanding the cultural dimensions of social media spaces

It has taken the past decade to commonly acknowledge that online space is tethered to real place. From euphoric conceptualizations of social media spaces as a novel, unprecedented and revolutionary entity, the dust has settled, allowing for talk of boundaries and ties to real-world settings. Metaphors have been instrumental in this pursuit, shaping perceptions and affecting actions within this extended structural realm. Specifically, they have been harnessed to architect Web 2.0 spaces, be it chatrooms, electronic frontiers, homepages, or information highways for policy and practice. While metaphors are pervasive in addressing and

KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response.

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics