IEDA Seminar - TriSNAR: A Three-Layer Sparse Estimator for Large-Scale Network AutoRegressive Models
Understanding multi-market interactions and identifying leading markets in the global financial network is of interest to investors, regulators and policymakers. To discover the essential dynamic dependencies of digital currency exchanges, we propose TriSNAR, a three-layer sparse estimator for large-scale network autoregressive models, which imposes a structure on the lag-, network/group- and individual-level effects. We determine the asymptotic properties of the sparse estimator and investigate its finite-sample performance in extensive simulations. Numerical analysis shows that TriSNAR obtains a higher accuracy with less computational time per model contestant. We explore the applicability of TriSNAR on a network of 26 cryptocurrency exchanges with hourly pricing information. TriSNAR not only provides good out-of-sample prediction accuracy, but also exactly detects each leading exchange in North America, Europe and Asia.
SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3573336
Simon Trimborn is currently an Assistant Professor at the Department of Management Sciences at College of Business, City University of Hong Kong. I conducted my PhD studies under the supervision of Prof. Wolfgang Karl Härdle at the Humboldt-University at Berlin (Humboldt-Universität zu Berlin) and after my PhD studies I was employed as Research Fellow for 2 years at National University of Singapore in the group of Assoc. Prof. Ying Chen. I defended my PhD thesis with the title "Statistics of Digital Finance" in 2018 and was awarded my doctorate with summa cum laude.
His work focuses on high dimensional data analysis for time series data with which he tackle specific problems of the cryptocurrency market and the blockchain from an econometric and statistical point of view.
IEDA Seminar Zoom Meeting
https://hkust.zoom.us/j/96809553140?pwd=VTMyNlpJVVJLWWU0aTVaYUFIc29uZz09
Meeting ID: 968 0955 3140
Passcode: iesem3010