Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

Abstracts of Presentations

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Keynote Talk


Michael Kumhof (Bank of England)

The Macroeconomics of Central-Bank-Issued Digital Currencies

We study the macroeconomic consequences of issuing central bank digital currency (CBDC) – a universally-accessible and interest-bearing central bank liability, implemented via distributed ledgers, that competes with bank deposits as medium of exchange. In a DSGE model calibrated to match the pre-crisis US, we find that CBDC issuance of 30% of GDP, against government bonds, could permanently raise GDP by as much as 3%, due to reductions in real interest rates, distortionary taxes, and monetary transaction costs. Countercyclical CBDC price or quantity rules, as a second monetary policy instrument, could substantially improve the central bank’s ability to stabilise the business cycle.


Session Talks


Anna Almosova (HU Berlin)

Costs of the Digital Currency Provision from the Macroeconomic Prospective

Rapid development of private digital currencies stimulated an active research on private currency provision and its macroeconomic implications. Standard monetary models, however, assume that money can be created with no (or almost zero) costs. I argue that this assumption is inappropriate for modern digital currencies that operate on blockchain platforms. According to a blockchain protocol money creation is associated with a min- ing activity which is costly in terms of computational time and, thus, energy. Mining costs are both quantitatively relevant and, more importantly, con- stitute an integral part of the blockchain algorithm. I use a Lagos-Wright search theoretic monetary model augmented with privately issued currencies as in Fernandez-Villaverde and Sanches (2016). Provision of private money is assumed to be costly with a linear costs function. I show that the costs of money creation change dynamic properties of the monetary system and characteristics of the effcient allocation.


Cathy YH Chen (HU Berlin)

Dynamic Topic Modelling and Crypto Fora

Cryptocurrencies are more and more used in official cash flows and exchange of goods. Bitcoin and the underlying blockchain technology have been looked at by big companies that are adopting and investing in this technology. The CRIX Index of cryptocurrencies indicates a wider acceptance of cryptos. One reason for its prosperity certainly being a security aspect, since the underlying network of cryptos is decentralized. It is also unregulated and highly volatile, making the risk assessment at any given moment difficult. In message boards one finds a huge source of information in the form of unstructured text written by e.g. Bitcoin developers and investors. We collect from a popular crypto currency message board texts, user information and associated time stamps. We then provide an indicator for fraudulent schemes. This indicator is constructed using dynamic topic modelling, text mining and unsupervised machine learning. We study how opinions and the evolution of topics are connected with big events in the cryptocurrency universe. Furthermore, the predictive power of these techniques are investigated, comparing the results to known events in the cryptocurrency space. We also test hypothesis of self-fulling prophecies and herding behaviour using the results.


Shi Chen (KIT, HU-Berlin) 

Time-varying volatility estimation with high frequencyCryptocurrencies

As blockchain technology arisen, the cryprocurrency (CC) market gains wide attention. However the CCs are not backed with real companies with earnings etc, it imposes challenges for researchers to model them in traditional way. Nevertheless the cryptos are generally traded in a very frequent way with numerous volumes. This motivates our research based on the high frequency CC exchange market. In this study, we will not only discuss the exchange price dynamics of CC in general, but also analyse what affects the price. To solve this, we make use of the full information contained in the limit order book of CC exchange data. Order book depth (total quantity of orders) can be used as a way to quantify the market's intentions to buy and sell.


Ying Chen (NUS) 

A sparse network autoregressive model for Cryptocurrencies

We propose a sparse-group network autoregressive (SGNAR) model to describe the dynamics of large-dimensional network. Under both group sparsity and elementary sparsity, we estimate the essential dynamics of Cryptocurrency and detect the influential nodes in the market. Numerical analysis demonstrates the stable performance of the proposed estimation with practically oriented simulations and real data. This is a joint work with Wolfgang Karl Haerdle, Simon Trimborn and Jiejie Zhang.


Hao Cheng (SMU - Singapore Mniversity of Management)

What drives Bitcoin?

Due to lack of knowledge about economics fundamental of bitcoin, the risk-reward structure of bitcoin is puzzling to investors. This study investigates this issue. We conduct a simple but effective analysis to understand the natural of the price evolution of bitcoin using the information of global equity markets. We find that the uncertainty driven information spillover from Chinese stock market. The effect is especially strong for Shen Zhen B share market. Using a dynamic partial least square (PLS) method to construct an index (PLS_SZB) by effectively extracting/aggregating information of individual stocks in Shen Zhen B share markets, we find that PLS_SZB significantly and positively predicts the bitcoin return of in-sample R-square of 15% and out-of sample R-square of 17%. The result is robust to potential selection bias, different estimation methodologies and volatility measures, among numerous other robustness checks. Furthermore, our findings consistent with hypothesis that high uncertainty of Chinese economy drives investors in Chinese stock markets and in bitcoin market. In particular, the Chinese macroeconomics uncertainty (proxied by volatility of first principle component of daily Chinese sovereign CDS curve) and equity market uncertainty (proxied by the cross-sectional dispersion of equity returns from China) can jointly explain over 50% variations of the predictive effect.


Paolo Giudici (University of Pavia)

What determines the price in cryptocurrency markets?

Trading of cryptocurrencies is spread about multiple venues, where buying and selling is offered in various currencies. However, all markets trade one common good and, by the law of one price, different prices should not deviate from each other, in the long run. In this context, the talk will address two main issues: 1) which platform is the most important, in terms of price discovery? To this end, we use a pairwise approach, accounting for the potential impact of exchange rates, based on Hasbrouck's and Gonzalo and Granger's information share. 2) which explanatory variables mostly drive prices, in a cross sectional and time varying perspective? To this end, we employ vector autoregressive models, along with correlated network models. In both cases our models are applied to real data, extracted from the available cryptomarket information.


Li Guo (SMU - Singapore Mniversity of Management)

What determines the price in cryptocurrency markets?

In this paper, we study the community detection problem in the dynamic stochastic blockmodel and dynamic stochastic co-blockmodel with node covariates and their applications in understanding latent group structure of cryptocurrencies. Covariate-assisted spectral clustering methods for estimating dynamic directed and undirected graphs are developed respectively. Weak consistency property is proved and degree correction is also studied. Applying our methods, we show return inferred network structure and node features, including algorithm, prooftypes, premined value and total coin supply, jointly determine the market segmentation. Node features reveals more within-group connections while return inferred network structure reflects more cross-group connections. Further analysis discover that the group with a higher centrality score suffers higher information asymmetry, and thus enjoys stronger market overreaction. Time-series and cross-sectional return predictability also confirm the economic meaning of our clustering results.


Wolfgang Karl Härdle (HU Berlin)

Pricing Cryptocurrency options: with applications to CRIX

The CRIX CRyptocurrency IndeX has been constructed based on approximately 30 cryptos and captures high coverage of available market capitalisation. Details of sub indices like ECRIX (Exact CRIX), EFCRIX (Exact Full CRIX) and also intraday CRIX movements may be found on the webpage ( In this paper, we propose a first step towards option pricing of this new asset class. After a careful econometric pre-analysis we motivate an affine jump diffusion model, i.e., the SVCJ (Stochastic Volatility with Correlated Jumps) model. We estimate the SVCJ and several nested models and then use them to price CRIX option. Our results indicate that the jumps presented in the cryptocurrency is an essential component which should be considered in the cryptocurrency option pricing. Concrete examples are given that allow for a timely establishment of an OCRIX exchange trading options on CRIX.


Tony Klein (TU Dresden)

Are Cryptocurrencies the New Gold? – A Portfolio-Based Analysis

The market capitalization of cryptocurrencies has risen significantly in the last few years. It indicates that cryptocurrencies are establishing themselves as an investment asset class. This study aims to identify the advantages cryptocurrencies as an asset in portfolio management. Applying CRIX index as a benchmark, the evidence of long memory and asymmetric volatility is demonstrated for cryptocurrencies. This behavior is comparable to Gold. However, the hypothesis of cryptocurrency as the new safe-haven in highly volatile market cannot be confirmed. This finding is based on portfolio optimization techniques which incorporate dynamic correlation estimated with diagonal BEKK model. We discover that cryptocurrencies are more applicable to target return portfolio strategy than minimum variance portfolio strategy.


Vytautas Karalevičius ( KU Leuven University )

The impact of blockchain technology for securities transaction lifecycle.

Currently, the most applicable use case of blockchain is payments: cryptocurrencies. However, a number of practitioners and academics are looking for the potential of blockchain technology’s applications outside of payments. One of the area there many believe, blockchain technology might have the biggest impact is securities transaction lifecycle. In this presentation different potential scenarios of applying blockchain technology at different stages of securities transaction lifecycle including, exchange, reconciliation, clearing and settlement. Impacts for both existing securities transaction lifecycle as well as for new arising digital securities are covered.


Jörg Osterrieder (ZHAW)

Cryptocurrencies – Not for the faint-hearted

Cryptocurrencies became popular with the emergence of Bitcoin and have shown an unprecedented growth over the last few years. As of October 2017, more than 1100 cryptocurrencies exist, with Bitcoin still being the most popular one. We provide both a statistical analysis as well as an extreme value analysis of the returns of the most important cryptocurrencies. A particular focus is on the tail risk characteristics and we will provide an in-depth univariate and multivariate extreme value analysis. The tail dependence of cryptocurrencies is investigated (using empirical copulas). For investors - especially institutional ones - as well as regulators, an understanding of the risk and tail characteristics is of utmost importance. For cryptocurrencies to become a mainstream investable asset class, studying these properties is necessary. Our findings show that cryptocurrencies exhibit strong non-normal characteristics, large tail dependencies, depending on the particular cryptocurrencies and heavy tails. Statistical similarities can be observed for cryptocurrencies that share the same underlying technology. This has implications for risk management, financial engineering (such as derivatives on cryptocurrencies) - both from an investor's as well as from a regulator's point of view.


Simon Trimborn (NUS)

Investing with cryptocurrencies - A liquidity constrained investment approach

Cryptocurrencies have left the dark side of the finance universe and become an object of study for asset and portfolio management. Since they have a low liquidity compared to traditional assets, one needs to take into account liquidity issues when one puts them into the same portfolio. We propose use a LIquidity Bounded Risk-return Optimization (LIBRO) approach, which is a combination of the Markowitz framework under the liquidity constraints. The results show that cryptocurrencies add value to a portfolio and the optimization approach is even able to increase the return of a portfolio and lower the volatility risk.