Introducing the Cryptocurrency VIX: CVIX
Yosef Bonaparte (University of Colorado at Denver)
October 25, 2021
We present a theoretical and empirical methodology that reflects the Cryptocurrency version of VIX, which we name it as CVIX (Crypto VIX), and captures the future 30 days forward Crypto risk (fear). Our framework is built on idiosyncratic and systematic Crypto risk, and is not based on the option implied volatility model, that developed by the CBOE for the S&P Volatility Index VIX. For back testing, our CVIX projected with accuracy of over 89% the 30 days forward Crypto realized volatility. We apply our CVIX framework on the S&P index, and show it projects the 30 days forward realized S&P volatility with accuracy of 91.8%, while VIX’s accuracy is only 63.4%. Our framework is superior over the option based VIX due to the fact that the option market does not represents all the stock market, and our methodology accounts properly for the idiosyncratic risk.

Using Principal Component Analysis on Crypto Correlations to Build a Diversified Portfolio
María Guinda and Ritabrata Bhattacharyya (WorldQuant U.)
July 30, 2021
A simple look at cryptoassets’ historical can lead us think that in recent years most have followed Bitcoin’s wake. If so, it would be very difficult to build an exposure to this market without being highly exposed to Bitcoin, and on the other hand a portfolio with many cryptos poses a great operational risk due to the lack of institutional custody. To this aim, this paper presents an updated correlation analysis of 31 crypto assets, among them and with some equity and gold indices. Furthermore, we conduct a PCA to identify the group of cryptos that present different correlation patterns and may help us build a diversified portfolio…. When using the PCA to build a diversified portfolio we achieved better results in terms of return, risk-adjusted return and with a lower correlation to Bitcoin.

Boosting Cryptocurrency Return Prediction
Ilias Filippou (Washington U. in St. Louis)
August 30, 2021
We use boosted decision trees to generate daily out-of-sample forecasts of excess returns for Bitcoin and Ethereum, the two best-known and largest cryptocurrencies. The decision trees incorporate information from 39 predictors, including variables relating to cryptocurrency fundamentals, technical indicators, Google Trends searches, Reddit comments, and articles from Factiva. We use the XGBoost algorithm to boost trees and find that excess return forecasts based on boosted trees produce statistically and economically significant out-of-sample gains. We explore the importance of individual predictors and nonlinearities in the fitted boosted trees. We find that a broad array of predictors are relevant for forecasting daily cryptocurrency returns and that strong nonlinearities characterize the predictive relationships.

Does Bitcoin Behave Like a Commodity?
October 5, 2021
Moazzam Khoja (University of Houston)
The paper tests bitcoin returns on predictions made by a commodity theoretical model by Routledge et al (RM). RM predicts the spot price, futures, and implied volatilities behavior to the immediate-use-demand and inventory of a commodity. I find that bitcoins futures and implied volatility behave like any other commodity. However, I do not find predictable behavior in spot prices. Instead, I find a positive relationship between the number of new participants and spot price, which indicates a behavior like an asset driven by network effect versus a commodity.

Monetary policy shocks and Bitcoin prices
Shisong Hsiao(Xiao) (Hunan Unversity)
October 10, 2021
The paper analyzes the impact of changes in U.S. monetary policy on Bitcoin prices. We find that a hypothetical unanticipated monetary tightening, which increases 1 bp on two-year Treasury yield, is associated with about a 0.25\% decrease in Bitcoin price on the Federal Open Market Committee(FOMC) meeting day. The accumulated effect is much stronger in the next few days after FOMC meeting. Quantile regression indicates a greater impact of monetary policy surprises on Bitcoin during the market boom.

Embracing the Future or Buying into the Bubble: Do Sophisticated Institutions Invest in Crypto Assets?
Luke DeVault (Clemson U.) and Kainan Wang (U. of Toledo)
November 17, 2021
We document that institutional investors increasingly invest in assets that do business in the cryptocurrency industry, which we call crypto assets. Cryptocurrency is a new and risky asset class. Investing in such assets demonstrates that managers are willing to accept change in the financial markets. Investing in new assets and being wrong can lead to both reputational costs and career concerns. We examine the performance of institutions willing to make investments in this new industry relative to the performance of their peers who are not. The results show that institutions that invest in crypto assets outperform their peers by about 2.8% per year, suggesting that the willingness to invest in new and uncertain assets may be a predictor of institutional performance.

Are Cryptocurrency Markets Running Behind the Fed? A Significant Shift in Crypto Markets Microstructure
Roland Grinis (GrinisRIT; MIPT), et al.
October 20, 2021
In this research we show that 2021 became a year when crypto markets significantly adjusted behavioural patterns, showing an increased institutional influence. We have come to two key conclusions that might indicate significant changes in the cryptocurrency market microstructure. Firstly, in contrast to recent research, we note that BTC/USD was sensitive to major Fed policy announcements in Q2-Q3 2021 similar to main asset classes. Secondly, OTC Liquidity Providers tend to provide twice as narrow spreads in comparison to Centralised Crypto Exchanges during market volatility related to macroeconomic news.

Is Crypto Tradeable? A Perspective From The Point Of Weak-Form Inefficiency
Ritabrata Majumder (SRCC)
October 27, 2021
The idea of Efficient market hypothesis is long drawn and stems from the idea of informational efficiency. A large and liquid market where transaction costs are low form the basis of an efficient market- yet the crypto market is often illiquid, exhibit large fluctuations and attracts the attention of retail speculators with unreliable information. Thus, this study aims to establish inefficiency in the crypto market through showing presence of serial correlation, non-randomness and volatility clustering. The study assumes significance as it indicates that positive developments, like introduction of Bitcoin and Ethereum futures and options or ETFS, can be used to move the Cryptocurrencies towards being efficient in long run. Through finding inefficiency, a prima-facie advantage is found in trading crypto-rather than investing

What Uncertainties Matter to Cryptocurrencies?
Imtiaz Sifat (Monash University)
July 11, 2021
We contribute to financial literature by identifying economic uncertainties salient to the price dynamics of cryptocurrencies. To measure this relationship, we examine the common stochastic trends between cryptocurrencies and major text-based uncertainty indices — categorical and broad — from the US and major developed economies from 2015 to 2021. Results from static factor tests using high-dimensional stochastic volatility factor models indicate trivial associations between global uncertainties and the crypto-sphere. Further investigation from dynamic implied correlation matrices, however, suggest that this phenomenon has undergone several changes over time. In fact, ties between US-based monetary policy uncertainties were non-trivial between 2015 to 2016. Following the post-Trump election bull run, the association faded, before reappearing upon the advent of the COVID-19 pandemic. Uncertainties surrounding some decisions by US government register minor significance. Uncertainties from European countries and China show some influence, with Japan registering an inverse relationship. Causality tests largely approbate these results, while underscoring an important point that while the pricing dynamics of cryptocurrencies may be independent from global uncertainties, their second moments remain attached to global trends. In sum, volatility in the crypto market is impacted by global uncertainties; prices are less so.

Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno