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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.

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Communication clarity is crucial in central banking, yet its measure and impact remain under-explored. This study introduces a novel text-based model tailored exclusively for FOMC statements, extracting the text-implied policy stance from statements and offering a nuanced measure of communication clarity. The proposed clarity measure reflects to what extent the textual and numeric components of the FOMC communication are consistent. The clarity measure is considered as an information treatment in central banks’ communication strategies. The causal effect of communication clarity on the reduction of market uncertainty is significant; a one standard deviation increase in clarity results in a 22% reduction in VIX.

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The increasing trading volume and regulatory scrutiny of cryptocurrencies, especially Bitcoin, have solidified their pivotal role in today’s financial markets.

This study examines various factors influencing future cryptocurrency returns. To overcome multi-collinearity problem and enhance prediction, we employ powershap for variable selection, which is a based on model interpretation (Verhaeghe et al, 2022). Our investigations focus on various periods before and after the Covid-19 pandemic, revealing the importance of technical indicators, oil prices, and exchange rates. To showcase the practical application of our method in predicting BTC returns, we present a trading strategy that demonstrates its potential for generating higher returns and Sharpe ratios.

Watch an exclusive and insightful interview with professor Härdle where he answers questions and offers personal insights on topics such as Machine Learning, FinTech, Big Data and the applications of these in economic research. Moreover, he also offers recommendations for students interested in Machine Learning and Econometrics and he also discusses personal research.

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This seminar introduces the Financial Risk Meter (FRM), rigorously derived from advanced statistical techniques, and capable of predicting market risks effectively.

The Financial Risk Meter (FRM) is designed to effectively predict future market risks. It identifies systemic network risks and dependencies among extreme events across different asset classes and regions. FRM connects asset pricing kernels, the highest Sharpe ratios, and overall market volatility. On all the presented FRM channels on theIDA.net it demonstrates its strength in detecting systemic risks and reveals network interconnectedness in tail event situations. The FRM predicts recessions and highlights peak risks during crises. Overall, the FRM provides valuable insights into systemic risks across various markets, helping policymakers and investors make informed decisions.

The slides are available on Quantinar, where you can learn more about FRM’s and its usage in different channels.

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Topic modeling and LDA (Latent Dirichlet Allocation) have proven valuable in various fields as an innovative approach to studying areas of interest and identifying topics in a dynamic content. The underlying assumption is that techniques like LDA can swiftly capture emerging topics in textual documents compared to other categorization tools. These unsupervised approaches have been used to identify new industries and technological domains. However, our study on the nascent topic of “sustainability” within the corpora of SGX-listed companies highlights clear limitations in employing techniques like LDA on sparse data. The dynamic LDA approach, also called DTM (Dynamic Topic Modelling), based on an 11-year database of annual reports from publicly listed companies in Singapore, could not detect sustainability’s rise as a critical topic in corporate practice following policy changes. Moreover, despite sustainability reporting becoming mandatory, sustainability-related topics may still not receive significant attention.

The slides are available on Quantinar.

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Exchanges operate limit-order markets where non-synchronous investors can trade. An early investor can leave a price quote for a late investor to consume. High-tech, informed middlemen entered these markets in the last two decades. Naturally, one expects better informed middlemen to hurt gains from trade (i.e., welfare), because they adversely select investor quotes. But, might they raise welfare as market makers who quickly refresh quotes on incoming information? And, as market makers, they offer investors the option to temporarily park their asset. We offer a model with all these features and calibrate it to study how middlemen affect welfare.

The slides are available here.

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Investors face the risk of cryptocurrencies disappearing from the market and becoming zombies. Our study aims to predict which cryptocurrencies will become untradable using predictors based on descriptive statistics of yield, volume and market capitalization. The sample includes crypto assets that have been listed on the markets for at least 210 days in the period from January 2015 to December 2022. We apply various machine learning algorithms and novel XAI tools, namely permutation-based feature importance and PDPs, to identify the main factors explaining the disappearance of cryptos and to understand the shape of the relationships. Our study shows that machine learning models allow us to predict that cryptocurrencies will become zombies within the next 28 days with 84% out-of-time accuracy. The tree-based models, especially random forests, outperformed traditional econometric approaches. The variables with the greatest explanatory power are related to volumes and returns calculated in previous periods.

The slides are available on Quantinar.