Research Focus: Uncovering Financial Insights through Data-Driven Analysis

Bastien's research is dedicated to exploring the multifaceted world of finance through a data-driven lens. His investigations encompass the financial stability implications of crypto-assets within the broader financial system and innovative investment strategies utilizing crypto-assets. Further, his reasearch around empirical asset pricing investigates model-free short-term predictors derived from high frequency data. Lastly, his interest of Sustainable Finance materalizes in two distinct lines of thinking: alternative taxation of financial markets to foster sustainable investments and local currencies to implement sustainable growth.

  1. Financial Stability and Crypto-assets: Bastien's research critically examines the intricate dynamics of financial stability concerning crypto-assets within the broader financial system. Employing data-driven methodologies, he seeks to unveil the complex connections and potential risks associated with these digital assets, contributing to a more comprehensive understanding of contemporary financial stability.
  2. Alternative Investment Strategies with Crypto-assets: Bastien dedicates his efforts to pioneering data-driven alternative investment strategies harnessing the potential of crypto-assets. His research extends beyond conventional approaches, providing quantitative insights that redefine asset allocation, risk management, and portfolio optimization in today's evolving investment landscape.
  3. Empirical Asset Pricing and High-Frequency Data Analysis: Bastien's expertise extends to empirical asset pricing, with a specialized focus on high-frequency data analysis. His research involves the meticulous decomposition of return distributions, unraveling intricate patterns and anomalies that shape asset valuations.
  4. Sustainable Finance: Bastien's interest in sustainable finance manifests in two distinct lines of thinking. Firstly, he explores the concept of alternative taxation for financial markets to encourage sustainable investments. Secondly, he contemplates the potential of local currencies as a means to implement sustainable growth. While these ideas are currently in their conceptual stage, they represent Bastien's vision for the future of Sustainable Finance.

By converging his passion for finance and data science, Bastien aspires to position himself as a dedicated data scientist in the field. His research not only contributes to our understanding of financial markets but also equips stakeholders with data-driven strategies to thrive in an increasingly intricate and digitalized financial landscape.

Working Papers

Tail Risk, Core Risk and Expected Stock Returns (with Johannes Breckenfelder and Roméo Tédongap)
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We decompose the realized variance into four components: downside tail, downside core, upside core and upside tail. This approach yields better prediction than established predictors such as VIX and the price-dividend ratio.

Multifractal Cryptocurrencies (with Veronika Czellar and Engin Iyidogan) [submitted]
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We propose a Markov-switching multifractal (MSM) model for the high-frequency return of cryptocurrencies. Maximum likelihood estimation in MSM would be prohibitively long to compute, especially in large samples such as high-frequency data. We therefore use Approximate Maximum Likelihood (AML; Czellar, Frazier, and Renault 2022) which is computationally feasible even in high-frequency data. Using AML we estimate all the parameters of the MSM model, including the number of hidden states. The extended model has powerful out-of-sample performance over its GARCH alternative. Our results further link the critical parameter k to the liquidity and other market dynamics of cryptocurrency returns. Finally, we create a trading strategy based on dynamic Sharpe ratio signals generated through the MSM-based model at high-frequency. The strategy generates substantial returns even in high trading fee regimes of cryptocurrency exchanges.

Bitcoin Options and High-Frequency Data for Derivative-Based Trading Strategies (with Juliane Proelss and Denis Schweizer)
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The return of Bitcoin is not predictable, however, the components of it are. Combining return forecasts with a dataset covering more than 10 million bitcoin option trades, we develop profitable trading strategies with long and short positions in bitcoin options.

How to Optimally Structure and Rebalance a Crypto-asset Portfolio? (with Juliane Proelss and Denis Schweizer)
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This paper investigates portfolio strategies involving only crypto-assets. We analyse optimal investment strategies for various types of investors. A concluding cluster analysis reveals which strategy performs best across various risk metrics.

Momentum Strategies and Risk Preferences of Crypto-asset Investors (with Juliane Proelss and Denis Schweizer) [submitted]
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This study delves into the momentum effects and investor risk aversion in the dynamic crypto-asset market. Utilizing the largest dataset yet in this context, we aim to illuminate the phenomena of momentum and reversal, addressing potential biases in cryptocurrency data. Our analysis allows us to explore the relationship between initial and subsequent returns and the impact of investor risk aversion. Significant momentum effects are observed for small coins in five out of six strategies, while mid-sized coins displayed consistent reversal effects. Large coins yielded mixed outcomes, implying varied risk aversion levels across coin sizes. The study underscores that constant relative risk aversion assumptions may not always correspond to actual investor behaviors, possibly influencing results.

Decrypting Crypto-assets: Introduction to an Emerging Asset Class

Crypto-assets constitute a large and heterogenous asset class. Only a minority of crypto-assets qualify as crypto-currencies, while the large majority provide services that go beyond Bitcoin’s peer-to-peer payment system pioneered by Nakamoto (2008). This paper introduces a comprehensive taxonomy spanning crypto-currencies, service crypto-assets, smart-contract platforms, distributed applications and tokenized assets (including both fungible and non-fungible tokens). To illustrate stylized facts and capture various risks of the cross-section of crypto-assets this paper relies on a novel dataset covering more than 14,000 crypto-assets.

Contagious Volatility

How does uncertainty of crypto-assets affect traditional asset classes? Using a vector autoregression (VAR) methodology, we answer this question by analyzing volatility spillovers between five asset classes (crypto-assets, stocks, bonds, fiat-currencies, and commodities). Given the vast heterogeneity within each asset class, our VAR specification accounts for cross-sectional variation across and within each asset class. By transforming the VAR residuals into sectoral shocks, we are able to distinguish between volatility spillovers across, and volatility co-movements within asset classes. We find that on average volatility of crypto-assets accounts for 15% of the volatility contagion received by traditional asset classes. The directional spillovers from crypto-assets to bonds and to fiat-currencies are particularly strong, capturing the wealth channel and the remittance channel, respectively.

Sectoral Impulse Response Functions: Spillovers in Global Stock Markets

As economies grow closer together it becomes increasingly important to understand linkages, both across and within different markets. To study these spillovers in a non parametric way the literature relies on the orthogonalized impulse responses functions (Sims, 1980) and the generalized impulse response functions (Koop et al., 1996). The present paper proposes an overarching theory, coined sectoral impulse responses functions, which not only includes orthogonalized and generalized impulse response functions as corner solution but also contains a wide a range of alternative specifications that allow to capture heterogeneous dynamics, both within and across markets.

Conferences, Seminars and Panels

2023 Cryptocurrency Research Conference, Monaco, Monaco
Digital, Innovation, Financing and Entrepreneurship Conference, Montreal, Canada
Forecasting Financial Markets, University of Rennes, Rennes, France
Cross Country Perspectives in Finance Conference, Paphos, Cyprus*
French Inter Business School Finance Conference, Toulouse, France*
Finance Seminar, University of St. Gallen, St. Gallen, Switzerland*
Finance Seminar, University of Luxembourg, Luxembourg, Luxembourg*
Finance Seminar, Rennes School of Business, Rennes, France*
2022 Finance Seminar, Concordia University, Montreal, Canada
Asset Pricing Breakfast, ESSEC Business School, Paris, France*
Finance Seminar, SKEMA Business School, Paris, France*
Panelist at Décryptons les cryptos, FrenchTec, Montreal, Canada
Cardiff FinTech Conference, Cardiff University, Cardiff, UK
Future of Finance, Suzhou University, Suzhou, China
FinTech Workshop, Inseec Research Center, Lyon, France
2021 Panelist at Cryptocurrency and Fund Performance, CAIA, Geneva, Switzerland
2020 Finance Seminar, NEOMA Business School, Paris, France
Finance Seminar, Boston University, Boston, USA
2019 Paris December Finance Meeting, Paris, France
Finance Seminar, ESSEC Business School, Paris, France
Finance Seminar, Bank of Canada, Ottawa, Canada
CEMA Annual Meeting, Carnegie Mellon University, Pittsburgh, USA
Asset and Risk Management, Amundi, Paris, France
Finance PhD Workshop, Université Paris-Dauphine, Paris, France
Fintech Adoption and Economic Behavior, EM Strasbourg, Strasbourg, France
2018 Finance Seminar, ESSEC Business Scholl, Paris, France
Computational and Financial Econometrics, University of Pisa, Pisa, Italy
FinTech and Crypto-Finance, NEOMA Business School, Paris, France

* presented by a coauthor