Hermann Elendner F5 Crypto Capital GmbH Director of Research & Development Ritterstraße 2A 10969 Berlin Germany  preferred contact: GnuPG: My key file,  its fingerprint: 0104 D496 59A3 C7D6 3121   849D 6FD4 B8E3 7D19 B071   Affiliations up ↑  academic  industry Austrian Blockchain Center Senior Scientist Centre for Blockchain Technologies, University College London Research Associate F5 Crypto Fonds 1 InvAG m.v.K. Non-Executive Director argo IT eG Consultant    Activities up ↑   I have developed a crypto-currency investment strategy ⌊see the whitepaper for details⌉ based on my crypto momentum factor for F5 Crypto Capital, who are licensing the F5 Crypto Index and investing in it via their BaFin-registered German open-end crypto fund.   I am co-organising Blockchain Nights, the monthly discussion series on Crypto-Currencies, Blockchain Technology, and Smart Contracts to bring together students, researchers, and entrepreneurs from business, economics, and technology backgrounds.   I have co-organised CCConf 2021   (ccconf.org), the 3rd Berlin Conference on Crypto-Currencies in a Digital Economy (as I did the 2nd CCConf in 2018 and the 1st CCConf in 2017).

My research focus is

primarily:

• Cryptocurrencies: Pricing, Investments, and Portfolio Management
• Cryptocurrency Markets: Micro-structure
• Blockchain Protocols and Value Stabilisation

secondarily:

• Mutual Funds, Credit Risk and Ratings

Cryptocurrencies and the Velocity of Money ⌊SSRN⌉, ⌊DOI⌉, ⌊github⌉, with I.G.A. Pernice, G. Gentzen,  Cryptoeconomic Systems Conference (CES) 2020. 
Velocity of money is central to the quantity theory of money, which relates it to the general price level. While the theory motivated countless empirical studies to include velocity as price determinant, few find a significant relationship in the short or medium run. Since the velocity of money is generally unobservable, these studies were limited to use proxy variables, leaving it unclear whether the lacking relationship refutes the theory or the proxies. Cryptocurrencies on public blockchains, however, visibly record all transactions, and thus allow to measure – rather than approximate – velocity. This paper evaluates most suggested proxies for velocity and also proposes a novel measurement approach. We introduce velocity measures for UTXO-based cryptocurrencies focused on the subset of the money supply effectively in use for the processing of transactions. Our approach thus explicitly addresses the hybrid use of cryptocurrencies as media of exchange and as stores of value, a major distinction in recently proposed theoretical pricing models. We show that each of the velocity estimators is approximated best by the simple ratio of on-chain transaction volume to total coin supply. Moreover, "coin days destroyed", if used as an approximation for velocity, shows considerable discrepancy from the other approaches.

Monetary Stabilization in Cryptocurrencies—Design Approaches and Open Questions ⌊SSRN⌉, ⌊DOI⌉, with I.G.A. Pernice, S. Henningsen, R. Proskalovich, M. Florian, and B. Scheuermann,  IEEE Crypto Valley Conference on Blockchain Technology (CVCBT) 2019. 
The price volatility of cryptocurrencies is often cited as a major hindrance to their wide-scale adoption. Consequently, during the last two years, multiple so called stablecoins have surfaced—cryptocurrencies focused on maintaining stable exchange rates. In this paper, we systematically explore and analyze the stablecoin landscape. Based on a survey of 24 specific stablecoin projects, we go beyond individual coins for extracting general concepts and approaches. We combine our findings with learnings from classical monetary policy, resulting in a comprehensive taxonomy of cryptocurrency stabilization. We use our taxonomy to highlight the current state of development from different perspectives and show blank spots. For instance, while over 91% of projects promote 1-to-1 stabilization targets to external assets, monetary policy literature suggests that the smoothing of short term volatility is often a more sustainable alternative. Our taxonomy bridges computer science and economics, fostering the transfer of expertise. For example, we find that 38% of the reviewed projects use a combination of exchange rate targeting and specific stabilization techniques that can render them vulnerable to speculative economic attacks – an avoidable design flaw.

Investing with Cryptocurrencies – Evaluating their Potential for Portfolio Allocation Strategies ⌊SSRN⌉, ⌊DOI⌉, with A. Petukhina, S. Trimborn, and W.K. Härdle.    
Cryptocurrencies (CCs) have risen rapidly in market capitalization over the past years. Despite striking volatility, their high average returns and low correlations have established CCs as alternative investment assets for portfolio and risk management. We investigate the benefits of adding CCs to well-diversified portfolios of conventional financial assets for different types of investors, including risk-averse, return-maximizing and diversification-seeking investors who may trade at different frequencies, namely, daily, weekly or monthly. We calculate out-of-sample performance and diversification benefits for the most popular portfolio-construction rules, including mean-variance optimization, risk-parity, and maximum-diversification strategies, as well as combined strategies. Our results demonstrate that CCs can improve the risk-return profile of portfolios, but their benefit depends on investor objectives. In particular, diversification strategies (maximizing the portfolio diversification index or equating risk contributions) draw appreciably on CCs and show, in line with spanning tests, CCs to be non-redundant extensions of the investment universe. However, when we introduce liquidity constraints via the LIBRO method to account for illiquidity of many CCs, out-of-sample performance drops considerably, while the diversification benefits persist. We conclude that the utility of CC investments strongly depends on investor characteristics.

The New IOV Financial Ecosystem ⌊DOI⌉, ⌊pdf of report⌉, in Enabling the Internet of Value: How Blockchain Connects Global Businesses, edited by N. Vadgama, J. Xu, and P. Tasca. 
The IoV has extended digitisation from the realms of information and communication to now also encompass digital scarcity. This has allowed for a virtual representation of economic wealth and its easy and efficient transfer. As a consequence, economic institutions and activity have rapidly shifted to near-instantaneous computer networks. The first functions to develop in the IoV, with blockchain technology, were payments and investments, in the form of cryptocurrencies. Business cases quickly proceeded to securitise illiquid assets or usage rights from file storage to real estate. The close-to-frictionless mode of operation in the IoV is proving superior to traditional marketplaces in terms of transaction costs, operating hours, and ease of access. This has led to a wave of disintermediation also in the realms of innovation financing and private equity: startups are increasingly applyingthe tokenisation of their product as a means to both raise funds as well as target and influence their customer markets. Recently, the developments have bifurcated into centralised service providers catering to blockchain ecosystems, and an equally strongpush to build as much financial infrastructure as possible in a fully decentralised manner, true to the fundamental blockchain idea, under the label of Decentralised Finance (DeFi).

The Cross-Section of Crypto-Currencies as Financial Assets ⌊DOI⌉ with S. Trimborn, B. Ong, T. M. Lee, in Handbook of Blockchain, Digital Finance, and Inclusion: Cryptocurrency, FinTech, InsurTech, and Regulation: Volume 1, edited by D. Lee, and R. Deng. 
Crypto-currencies have developed a vibrant market since bitcoin, the first crypto-currency, was created in 2009. We look at the properties of crypto-currencies as financial assets in a broad cross-section. We discuss approaches of altcoins to generate value and their trading and information platforms. Then we investigate crypto-currencies as alternative investment assets, studying their returns and the co-movements of altcoin prices with bitcoin and against each other. We evaluate their addition to investors' portfolios and document they are indeed able to enhance the diversification of portfolios due to their little co-movements with established assets, as well as with each other. Furthermore, we evaluate pure portfolios of crypto-currencies: an equally-weighted one, a value-weighted one, and one based on the CRypto-currency IndeX (CRIX). The CRIX portfolio displays lower risk than any individual of the liquid crypto-currencies. We also document the changing characteristics of the crypto-currency market. Deepening liquidity is accompanied by a rise in market value, and a growing number of altcoins is contributing larger amounts to aggregate crypto-currency market capitalization.

Cryptocurrency: Speculative Asset and Medium of Exchange ⌊SSRN⌉, with I.G.A. Pernice and A. Almosova.  
Cryptocurrency, characterized by its usage, is both—speculative investment and medium-of-exchange. We combine two models to form an abstraction capable of mirroring this Janus-like quality. Our approach merges an asset-flow approach modeling speculation around an asset's fundamental value into the transactions-form of the quantity equation of money, developed to analyze money as a medium of exchange. Our approach allows us to analyze the inter-temporal relation of the quantity theory's key variables for cryptocurrencies with their inflexible token supply. Due to the intuitive component models, our model is simple to interpret and to apply both empirically and in simulations. We demonstrate its usefulness via simulations of how cryptocurrency prices adjust after changes in fundamental values. We find that a higher fraction of tokens held by investors (rather than circulated by users) lead to disproportionate increases in the instability of the price process in response to shocks. We conclude that a stabilization of cryptocurrency prices is unlikely unless accompanied by low levels of tokens held by investors.

Liquidity and Resiliency of Crypto-currency Markets.
Crypto-currencies (CCs) are traded in fragmented markets without market makers, exhibiting high volatilities and little relation to known risk factors. Seemingly unaffected by these challenges and repeated spectacular losses, many have attracted exponential growth in trading volumes. This paper is the first to study the resiliency of CC markets by focusing on liquidity and how it reacts to returns. First, I find the main CCs to be surprisingly liquid despite the high fragmentation of trading, implying that trading venues are highly integrated. Second, I show that similarly to stock markets, liquidity drops subsequent to price shocks; however, it is frequently replenished remarkably fast, especially for more mature CCs. These results suggest liquidity measures as more appropriate indicators of CC maturity; price discovery and information transmission to work effectively across numerous trading platforms; and cast doubt on the common interpretation of spillover effects in liquidity being driven by funding constraints of market makers, as the same effect obtains in CC markets where there are none.

Mutual Funds' End-of-Period Trading and Stock Prices, with L. Klipper.  
We provide an easy way to measure trading activity by mutual funds in the last three days of their reporting periods. Heavy end-of-period (EoP) traders report more winner and fewer loser stocks, yet perform no better. Consistent with window dressing, their rank and return gaps loom higher. Aggregating net EoP purchases minus sales over all funds, we impute trade imbalances for individual stocks. Stocks with high positive EoP trade imbalance experience significant price increases of about 20 bps over those three days. Inconsistent with information trading, prices revert within a week. In line with price pressure from institutional investors, liquid stocks appreciate less strongly and revert more quickly. Period-ends differ, however, across calendar months: shares with EoP imbalance appreciate at the end of all months, but reversals only follow June and December. Finally, we show window dressing, portfolio pumping, or fund flows alone are unlikely to explain our results.

The Value of Money: An Asset-Pricing Investigation, with J. Radwanski.  
We propose a method to quantify the value of the convenience yield that flows to the holders of money. Our asset-pricing approach builds on two key assumptions: (1) the information set of the economy is spanned by a set of state variables that follow a VAR process, and (2) there exists no arbitrage. Otherwise, our procedure is model-free. By computing the value of the convenience yield, we are able to estimate the price-dividend ratio for money, and decompose its variance into parts due to rationally expected dividend growth and to expected discount rates. The price-dividend ratio of money is mainly driven by expectations of the real value of the convenience yield flowing to the holders of money, and to a much smaller extent by time-varying discount rates. There appears to be virtually no time variation in excess returns on money. The real returns on money are not too volatile, but driven by large and offsetting revisions in rational expectations of future cash flows and discount rates, which sheds light on price stickiness from an asset-pricing perspective.

Rating-Induced Default Risk and Downgrade Hesitation.
Credit ratings should reflect credit risk. Mounting evidence implies they also impact credit risk. As strategic agencies will take this into account, I build a model to show how this feedback effect incentivizes raters to postpone or omit downgrades which they know would be warranted. If agencies succumb to the conflict of interest, they restrict self-inflicted fee losses by optimally hesitating to announce a strictly positive fraction of merited announcements. In equilibrium, ratings are informative, but only partially, because the agency withholds information---irrespective of any level of reputation costs. To devise a test design, I derive empirical predictions: It is shown that the probability of agencies concealing downgrades is increasing with obligors' proximity to default and their reliance on external financing, while decreasing in distance to the default boundary and subsequent to crises due to higher reputation costs. For opaque firms more information is held back. Finally, I detail the identification strategy for an empirical test of the predictions based on CDS spreads and market-implied ratings.

Exploring the Performance of Government Debt Issuance  ⌊SSRN⌉, with A. Eisl and S. Pichler.  
The financial performance of governments in issuing debt is an open empirical question. We develop performance measures for the decisions debt management offices (DMOs) face: The amount to issue is largely exogenous to them, but they determine its distribution across issue dates (timing) and the choice of instruments (allocation). For a unique dataset of five European DMOs, we apply our measures to assess DMOs' issuance strategies with respect to prevailing market rates, their country's credit spread, or the mispricing between primary and secondary markets. With a single exception, we find no ability to outperform secondary markets. We do find evidence of increased volatility. Most importantly, Austria, France, and Italy time primary-market effects negatively: While not underpricing issues severely, they allot more volume on dates of higher underpricing compared to other DMOs. Thus, although government issuance shows no strong impact on secondary markets, DMOs need to take primary-market effects into account.

Re-Mapping Credit Ratings ⌊SSRN⌉, with A. Eisl and M. Lingo. 
Rating agencies report ordinal ratings in discrete classes. We question the market's implicit assumption that agencies define their classes on identical scales, e.g. that AAA by Standard & Poor's is equivalent to Aaa by Moody's. To this end, we develop a non-parametric method to estimate the relation between rating scales for pairs of raters. For every rating class of one rater this, scale relation identifies the extent to which it corresponds to any rating class of another rater, and hence enables a rating-class specific re-mapping of one agency's ratings to another's. Our method is based purely on ordinal co-ratings to obviate error-prone estimation of default probabilities and the disputable assumptions involved in treating ratings as metric data. It estimates all rating classes' relations from a pair of raters jointly, and thus exploits the information content from ordinality. We find evidence against the presumption of identical scales for the three major rating agencies Fitch, Moody's, and Standard & Poor's, provide the relations of their rating classes and illustrate the importance of correcting for scale relations in benchmarking.

On the Emergence of Money: the formation of media of exchange in artificial societies,   ISBN 978-3-639-21111-5. ⌊amazon⌉
A fundamental question in monetary theory is, why does money circulate? The search-cost model of Kiyotaki and Wright proves that objects gain value by acting as media of exchange. It thus provides a rationale for the existence of money, explaining why it is not abolished once in circulation. However, it cannot reveal how the historically prevalent equilibrium of nobody accepting money was surmounted, or how out-of-equilibrium dynamics evolve. This work exhibits the pure emergence of money from a setting where everybody refuses goods of no use to them, yet acts with a `trembling hand.' Artificial society simulations of a model built in the spirit of Epstein and Axtell show, a set of boundedly rational agents who dynamically update individual expectations through strictly local interactions is sufficient for the onset of money. Speculation arises, too, though the dynamics show it requires time to evolve. This book contains also a survey of the search-cost literature and is addressed to both monetary theorists as well as researchers and policy makers interested in analysis by agent-based simulation.

Highlighted conferences are those where I have presented.

Discussion slides are designed to be presented, not self-explanatory.

For jointly taught courses, see the links for the names of colleagues.

Fixed-Income and Credit Derivatives,  for master's students of Quantitative Asset and Risk Management at the University of Applied Sciences bfi Vienna.
winter 2022, winter 2021 (),   winter 2020 (),   winter 2019 (),   winter 2018 (),   winter 2017 (),   winter 2016 (),   winter 2015 (),   winter 2014,   winter 2013,   winter 2012,   winter 2011
syllabus

Blockchains and Crypto-Currencies,  for master's students at ISSEM at Universidad de La Habana.
summer 2019

Crypto-Currencies,  for master's students at Humboldt-Universität zu Berlin.
winter 2018 (),   winter 2017 ()

Advanced Financial Economics: Corporate Finance,  for PhD students at Humboldt-Universität zu Berlin.
winter 2017: course + lab ();   winter 2016: course + lab ();   summer 2016: course + lab (),   summer 2015: course, lab ();   summer 2014: course, lab ();   winter 2012: course, lab ()

Private Equity,  for master's students at Humboldt-Universität zu Berlin.
summer 2017 (),   summer 2016 (),   winter 2014 (),   winter 2013 (),   winter 2012 ()
Teaching Award: Best Lecture at the School of Business and Economics in the academic year 2015/2016

Investmentanalyse und Portfoliomanagement, a.k.a. Grundzüge der Finanzierung 2,  for bachelor's students at Humboldt-Universität zu Berlin.
winter 2017 ();   summer 2017: course + exercises ()

Corporate Finance,  for master's students at Humboldt-Universität zu Berlin.
winter 2016 ();   winter 2015 (),   winter 2014 (),   winter 2013 (),   summer 2013 ()

Financial-Markets Regulation,  for PhD students at Humboldt-Universität zu Berlin.
summer 2015 (),   summer 2014,   summer 2012

Introduction to Corporate Finance,  for bachelor's students at Sofia University St. Kliment Ohridski.
summer 2014 (),  

Private Wealth Management,  for master's students of Finance and Accounting at WU Vienna.
winter 2011

Management Science Lab: Financial Management,  for master's students of Management Science at WU Vienna.
winter 2010,   winter 2009,   winter 2008

Analysis and Decision-Making in Financial Management,  for master's students of Management Science at WU Vienna.
summer 2010,   summer 2009,   summer 2008

Principles of Academic Work: LaTeX workshop,  for undergraduates at WU Vienna.
summer 2012,   winter 2011,   summer 2011,   winter 2010,   summer 2010,   winter 2009

Theory of Computer Science,  for master's students of Computer Science at TU Vienna.
summer 2004

Development and Implementation of Commercial Information Systems,  for undergraduates at TU Vienna.
winter 2003

Introduction to Programming,  for undergraduates at TU Vienna.
winter 2001

PhD theses, in progress:

• Ingolf Pernice

PhD theses, in progress:

• Ahmed Sultan

Master's theses, completed:

• Ingolf Pernice, 2018, Predicting volatility in cryptocurrency currency markets: machine learning vs. GARCH and VAR models
• Ahmed Sultan, 2017, Towards More Rationality: Confirmation Bias in Cryptocurrency Markets
• Florian Schwarz, 2017, Liquidity Commonality in Crypto Currencies
• Florian Döhnert, 2017, Issuance of a crypto-currency ETF – necessity, computation and challanges
• Patrick Steffen, 2017, What drives Bitcoin returns? An analysis of intra- and extra-currency events on short-run Bitcoin exchange-rate fluctuations
• Tony Meinke, 2016, Transparency versus Professionalism in Delegated Investment Management: Analyzing wikifolio performances
• Sara Makarem, 2016, Hate the player or the game? Managerial biases with perfect real-time disclosure
• Claas Stüdemann, 2016, Crowdfunding
• Eldar Erkinbek uulu, 2016, The Relationship Between Stage Financing and Syndication in Venture Capital
• Marlene Zetsche, 2016, The Listing of Private Equity and IPO Underpricing of Portfolio Companies
• Hasmik Saturyan, 2015, Behavioural Biases and Transparency: The case of wikifolio fund managers
• Ulrike Pasda, 2015, The Relation of Return and Liquidity in Bitcoin Market Places
• Oliver Gruß, 2015, Quantifying Risk Compensation in Private Equity Performance
• Lukas Kramer, 2014, German Venture-capital-backed IPOs and Underpricing
• Kirill Shchelkunov, 2014, Performance Measurement of Private Equity: Blackstone Case
• Paul Dohse, 2013, The Impact of Enforcement Announcements

Distressingly, the university library does not archive master's theses, so no links can be provided. Please contact the Finance Group secretary to obtain access.

Bachelor's theses, completed:

• Julian Winkel, 2018, Order Imbalances and Returns in Cryptocurrency Markets
• Matthias Witt, 2017, Blockchain – Technik, Skalierbarkeit und Innovationspotenzial
• David Schulze, 2014, Public Debt Issuance in the People's Republic of China: Process and Framework
• Tony Meinke, 2014, CAPM and the Fama–French Three-Factor Model for Germany in 2000–2014

Master's theses, completed:

• Michael Auttrit, 2012, Regulation of Rating Agencies in Europe
• Arthur Längle, 2012, Market-Implied Ratings: What CDS tell about Re-Ratings that Agencies do not
• Thomas Braun, 2012, The Practice of Issuing Government Debt: International Comparison
• Aili Wang, 2012, Barking up the wrong tree: Saving sovereigns to bail out banks?
• Patrick Marek, 2011, Aid for Greece: Reliable News or Cheap Talk?
  ⌊full text⌉ (in German)
• Edis Halilovic, 2011, Public Debt and Causes of National Bankruptcy
• Bettina Rothensteiner, 2011, Sovereign Default – Objectives, challenges and measures in the process of restricting sovereign debt crises
• David Polacek, 2010, Too late, too little? An event study of rating downgrades during the crisis
• Kajan Saied, 2009, Copulae and Value at Risk – A Theoretical Application to Deposit Insurance

Bachelor's thesis, completed:

• Arthur Längle, 2011, R and SQL for Finance: Ratings and CDS