Daniil Parfenov

About me

My name is Daniil Parfenov. Currently I am a PhD student in Finance at Bocconi University.
I am also a member of cryptocurrency research laboratory at Bocconi and a part-time member to the "New Trends in International Finance" laboratory at the Moscow State Institute of International Relations (MGIMO-university).

I hold a Bachelor's Degree in Applied Economics (2020) from Moscow State Institute of International Relations and a MSc in International Financial Economics from University of Konstanz (2022).

Previously I was a member of M&A team at KPMG Moscow, and a research/teaching assistant at the Chair of Statistics and Econometrics at the University of Konstanz.

My primary research interests lie at the intersection of asset-pricing, financial econometrics and machine learning. I am also looking for alternative approaches to study macroeconomic growth, drawing inspiration largely from thermodynamics.

Contacts
daniil.i.parfenov@gmail.com
daniil.parfenov@phd.unibocconi.it
https://www.researchgate.net/profile/Daniil-Parfenov

Research papers
"Credit risk linkages in the international banking network, 2000–2019 ", Risk Management
with Mikhail Stolbov, July 2023
Risk Management,
25, 21, (2023)
https://doi.org/10.1057/s41283-023-00126-0

"Efficiency linkages between cryptocurrencies, equities and commodities at different time frames" , Procedia Computer Science
D. Parfenov, February 2022
Procedia Computer Science,
Volume 199, Pages 182-189, (2022), ISSN 1877-0509
https://doi.org/10.1016/j.procs.2022.01.023

Upcoming

"Network structure of cryptocurrency market and linkages to traditional assets" ,
D. Parfenov, August 2022 Monography chapter with "New Trends in International Finance" Lab (MGIMO-U)

Working papers

"Supply shocks, growth and blockchain ", Spring 2023 - ongoing with Max Croce, Thien Nguyen and Claudio Tebaldi

“Exploring Potential of clustering for portfolio management” March 2022 – ongoing

Application of computationally low-cost clusterization technique
to the S&P 500 constituents
Resulting buy-and-hold, 0-transaction cost portfolios deliver
20% CAGR robustly across 2003-2018 period
Currently testing methodology for other datasets

https://www.researchgate.net/publication/361373536_Exploring_Potential_of_Clustering_for_Portfolio_Management