Network Event - AI in Digital Finance - Risk Premia in the Bitcoin Market
We are delighted to announce a collaborative initiative between IDA, the Digital Assets Institute, AI4EFIN, COST FIN-AI, MSCA DIGITAL FINANCE and Institute for Economic Forecasting, Romanian Academy!
Time & Location
13 Dec 2024, 11:00 – 12:00 EET
https://ase.zoom.us/j/84350805723?pwd=qoaiN
About the event
AI in Digital Finance — Online seminars
We are delighted to announce a collaborative initiative between IDA, the Digital Assets Institute, AI4EFIN, COST FIN-AI, MSCA DIGITAL FINANCE and Institute for Economic Forecasting, Romanian Academy: a regular seminar dedicated to exploring the intersections of Artificial Intelligence (AI) and Digital Finance. This seminar series promises to be an enriching platform for learning, discussion, and networking within the area of FinTech and AI innovations.
When? Every month, we host a session that brings together leading minds and passionate professionals from the fields of AI and digital finance. These gatherings are designed to offer insights into the latest research, technological advancements, and trends that are shaping the financial landscape of tomorrow.
Who? This series is tailored for researchers and professionals who see the need of navigating between complexities and opportunities in a fast convergence of finance and technology. Whether you are a professional in the industry, an academic researcher, or a student eager to expand your knowledge, these sessions will provide valuable perspectives and networking opportunities.
Details
Frequency: Monthly
Format: Hybrid, accommodating both in-person and online participants
Venue: Details will be provided for each session
Transparency: Slides on quantinar.com; Codes on quantlet.com; Data on blockchain-research-center.com
Virtual Participation: Zoom link: https://ase.zoom.us/j/84350805723?pwd=qoaiNnbVKQEF8T1jCLDme8BmbGXplw.1
Announcement: Via Meetup and Linkedin pages of IDA and AI4EFin
We invite you to join us and to be part of a community at the forefront of FinTech innovation.Keep an eye out for updates and further information on how you can be a part of these exciting discussions.
AI in Digital Finance — 13 December 2024
Title: Risk Premia in the Bitcoin Market
Speaker: Maria Grith (Erasmus University Rotterdam), Joint with Caio Almeida, Ratmir Miftachov and Zijin Wang
Date and time: December 13, 2024 (Friday), 11:00 AM (EEST) / 10:00 AM (CEST)
Register on Meetup.
Abstract Based on options and realized returns, we analyze risk premia in the Bitcoin market through the lens of the Pricing Kernel (PK). We identify that: 1) The projected PK into Bitcoin returns is W-shaped and steep in the negative returns region; 2) Negative Bitcoin returns account for 33% of the total Bitcoin index premium (BP) in contrast to 70% of S&P500 equity premium explained by negative returns. Applying a novel clustering algorithm to the collection of estimated Bitcoin risk-neutral densities, we find that risk premia vary over time as a function of two distinct market volatility regimes. In the low-volatility regime, the PK projection is steeper for negative returns and has a more pronounced W-shape than the unconditional one, implying particularly high BP for both extreme positive and negative returns and a high Variance Risk Premium (VRP). In high-volatility states, the BP attributable to positive and negative returns is more balanced, and the VRP is lower. Overall, Bitcoin investors are more worried about variance and downside risk in low-volatility states.
Speaker Bio
Dr. Maria Grith is an Assistant Professor in Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. She earned her Ph.D. in Economics from Humboldt University of Berlin in 2013. Prior to her current position, she held postdoctoral appointments at Humboldt University of Berlin, the University of Pennsylvania, and Singapore Management University.
Dr. Grith's research focuses on developing statistical methods for analyzing dynamic high-dimensional data. She utilizes nonparametric statistics, functional data analysis, machine learning, and graphical models to address complex challenges in finance. Her work aims to provide innovative solutions in areas such as asset pricing, option pricing, risk management, and the evolving landscape of digital finance.
Among her notable publications is "Functional Principal Component Analysis for Derivatives of Multivariate Curves," co-authored with Wolfgang K. Härdle, Alois Kneip, and Heiko Wagner, and published in Statistica Sinica in 2018.