Cryptos

Talks/Editorials:

Papers:

Abstract: We study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis. This is a multiscale noise-assisted approach that decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. The decomposition is adaptive to the time-varying volatility of each cryptocurrency price evolution. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the instantaneous energy-frequency spectrum of each cryptocurrency to illustrate the properties of various timescales embedded in the original time series.

Abstract: In this paper, we analyze the process of constructing cointegrated portfolios of cryptocurrencies. Our procedure involves a series of statistical tests, including the Johansen cointegration test and Engle-Granger two-step approach. Among our results, we construct cointegrated portfolios involving four cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Bitcoin Cash (BCH), and Litecoin (LTC). We develop a number of trading strategies under different entry/exit thresholds and risk constraints, and examine their performance in details through backtesting and comparison analysis. Our methodology can be applied more generally to create new cointegrated portfolio using other cryptocurrencies.

Abstract: We study a mean-field game framework in which agents expend costly effort in order to transition into a state where they receive cash flows, such as bitcoin mining. As more agents transition into the cash flow receiving state, the magnitude of all remaining cash flows decreases, introducing an element of competition whereby agents are rewarded for transitioning earlier. An equilibrium is reached if the optimal expenditure of effort produces a transition intensity which is equal to the flow rate at which the continuous population enters the receiving state. We give closed-form expressions which yield equilibrium when the cash flow horizon is infinite or exponentially distributed. When the cash flow horizon is finite, we implement an algorithm which yields equilibrium if it converges. We show that in some cases, a higher cost of effort results in the agents placing greater value on the potential cash flows in equilibrium. We also present cases where the algorithm fails to converge to an equilibrium.

In the Press: 

Teaching: 

CFRM 525 FinTech, Blockchains, and Cryptocurrencies (4 credits), Winter 2019

Course Description

Financial technology (FinTech) is rapidly shaping the finance industry and has led to increasingly technological approaches to the financial services and investing. This CFRM course provides a graduate-level introduction to FinTech, as well as blockchain, cryptocurrencies (e.g., Bitcoin and Ethereum). Through this course, students are expected to develop a broad understanding of recent FinTech innovations and development, as well as the associated computational finance and risk management methods and perspectives. The course will cover a series of real-world applications, including robo-advising, peer-to-peer lending, micro-finance tool, AI and Machine Learning for trading, investing, and asset/securities rating.  

Learning Objectives

Upon successful completion of the course students are expected to acquire a broad understanding of recent FinTech innovations, and their business models as well as market mechanisms. In addition, students will be able to understand the basics of Blockchain technology and its applications, with focus on Cryptocurrencies. The following topics will be covered: