Min-Bin Lin

Hi, I’m Min-Bin I'm a Postdoc researcher in Germany. I received my Ph.D. in Statistics and Econometrics from the International Research Training Group (IRTG) 1792 "High Dimensional Nonstationary Time Series" at Humboldt-Universität zu Berlin, Germany. I have an MSc in Industrial Engineering and Engineering Management (IEEM) from the National Tsing Hua University, Taiwan. My research interests center on 1) blockchain technology and its applications (i.e. cryptos, NFTs) aiming to contribute towards a better understanding towards blockchain technology from dynamical, statistical, and economic perspectives and further solve the problems discovered in the blockchain ecosystem; 2) data science working on devloping data intensive model and large data processing and understanding.

Research interests: econometrics, time series analysis, blockchain and cryptocurrency, Fintech, digital economy data science.

Selected Publications [Full List]

WP

Cross-exchange Crypto Risk: A High-frequency Dynamic Network Perspective

Lu WB, Wang YF*, Lin MB*, Ren R, and Härdle WK

Working Paper 2022

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Cross-exchange trading induces risk spillover in the crypto market, especially for centralized exchanges, which compound crypto volatility and counterparty risk. We propose a Multivariate Heterogeneous AutoRegression for Crypto Market (MHAR-CM) to specifically investigate interconnectedness among 9 different exchanges on Bitcoin in a high-frequency resolution via dynamic partial correlation networks. MHAR-CM accommodates the stylized facts of Bitcoin, such as trading 24/7 and the long-memory effect on returns. The monthly dependence coefficients of MHAR-CM reflect the evo- lution of Bitcoin returns and the persistent influence of events. We find that the scale of an exchange determines its influence towards the others, and the interconnectedness among these exchanges is stronger during a crisis. We also conclude with some remarks on FTX due to its recent bankruptcy. Furthermore, we demonstrate that a portfolio considering the dynamics of partial correlation networks offers a promising result in terms of risk diversification.
WP

DAI Digital Art Index: A Robust Price Index for Heterogeneous Digital Assets

Lin MB, Wang BL, Bocart F, Hafner C, and Härdle WK

Working Paper 2022

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The big bang of non-fungible tokens (NFTs) has caused the birth of a brand new era for digital art. NFTs, driven by blockchain and smart contracts, provide both artists and art collectors an unprece- dented marketplace equipped with more security, flexibility, publicity, and freedom to monetize. Unlike the conventional art market, NFT art market trades works of art in rapid succession and has no sluggish business cycle. Yet, the emergence of such a market has been considered to be packed with speculation and economic uncertainty, given the limited understanding towards this market. To precisely depict the NFT art market and offer a gauge for market volatility, we construct the price index – Digital Art Index (DAI) – using hedonic regression on the top 10 liquid NFT art collections and provide an empirical overview. Due to the common phenomenon of outlying price observations, we propose two innovative alternative procedures – Huberization and DCS-t filtering to create a robust index. Moreover, by considering each artwork’s time-variant and time-invariant characteristics, we are able to investigate price determinants and access the intrinsic value of the market.
AQAFA

Blockchain Mechanism and Distributional Characteristics of Cryptos

Lin MB, Khowaja K, Chen CYH, and Härdle WK

Advances in Quantitative Analysis of Finance & Accounting, AQAFA 2021

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We investigate the relationship between underlying blockchain mechanism of cryptocurren- cies and its distributional characteristics. In addition to price, we emphasise on using actual block size and block time as the operational features of cryptos. We use distributional charac- teristics such as fourier power spectrum, moments, quantiles, global we optimums, as well as the measures for long term dependencies, risk and noise to summarise the information from crypto time series. With the hypothesis that the blockchain structure explains the distribu- tional characteristics of cryptos, we use characteristic based spectral clustering to cluster the selected cryptos into five groups. We scrutinise these clusters and find that indeed, the clus- ters of cryptos share similar mechanism such as origin of fork, difficulty adjustment frequency, and the nature of block size. This paper provides crypto creators and users with a better un- derstanding toward the connection between the blockchain protocol design and distributional characteristics of cryptos.