Blockchain Papers

Reza Lotfi, Sara Ghaboulian Zare, Alireza Gharehbaghi, Sima Nazari · 5 authors

No abstract is available for this record.

Tetsuo Kurosaki, Young Shin Kim

We study portfolio optimization of four major cryptocurrencies. Our time series model is a generalized autoregressive conditional heteroscedasticity (GARCH) model with multivariate normal tempered stable (MNTS) distributed residuals used to capture the non-Gaussian cryptocurrency return dynamics. Based on the time series model, we optimize the portfolio in terms of Foster-Hart risk. Those sophisticated techniques are not yet documented in the context of cryptocurrency. Statistical tests suggest that the MNTS distributed GARCH model fits better with cryptocurrency returns than the competing GARCH-type models. We find that Foster-Hart optimization yields a more profitable portfolio with better risk-return balance than the prevailing approach.

Yilei Wang, Guoyu Yang, Andrea Bracciali, Ho-fung Leung · 7 authors

No abstract is available for this record.

Dinesh Sivagourou

Investoren und Vermögensverwalter suchen nach Finanzinstrumenten, die die erwartete Rendite ihrer Investition bei gleichzeitiger Minimierung des potenziellen Risikos erhöhen. In der Praxis diversifizieren Vermögensverwalter ihre Vermögensallokation auf verschiedene Anlageklassen allen voran Aktien, Anleihen und Rohstoffe. In den letzten Jahren gewinnt die neue Anlageklasse der Kryptowährungen immer mehr an Einfluss - Anlagekapital. Die erste unter ihnen, Bitcoin, wurde wegen ihrer Technologie sehr berühmt: dezentrale Datenhaltung, sicheres und schnelles elektronisches Tausch- und Zahlungsmittel. Diese Arbeit konzentriert sich auf die Leistung der Core-Satellite Strategie mit diesen neuen digitalen Währungen als Satellit. Die Herausforderung besteht darin, die Kryptowährungen zu finden, die Abwärtstrends kompensieren kann und dem Anleger bessere Renditen erwirtschaftet. Die Korrelationsstruktur der Kryptowährungen muss untersucht werden, sodass gegenläufige Kryptowährungen ausgewählt werden können. Dazu verwenden wir TEDAS – Tail Event Driven Asset allocation, eine aktive Investitionsstrategie zur Auswahl der Kryptowährungen. Diese Methode untersucht die Abhängigkeit von Kryptowährungen in verschiedenen Quantilen am linken Rand der Verteilung. Die Arbeit vergleicht verschiedene auf TEDAS basierenden Investitionsstrategie.

Pedro Bonillo Bueno, Emilio Aragon Fortes, Konstantinos Vlachoski

Since its launch in 2008, Bitcoin becomes one of the most successful and fast-growing alternative currencies. As of 2017, the market capitalization is around $46 billion and arguably expected to continue growing. The Bitcoin to the US dollar exchange rate has been very volatile and fluctuating significantly. Although Bitcoin was designed as a medium of exchange, it is now more as an investment tool and thus the development of effective quantitative risk management tools becomes quite urgent for all the market participants. In this paper, we investigate empirical distribution of the Bitcoin exchange rate returns by using four types of widelyused heavy-tailed distribution and show that the Skewed t distribution has the best empirical performance. We further calculate the VaR based risk measures and found the Skewed t distribution generates the VaR values, which are closest to historical VaR values. Our results could be directly used in the industry’s stress testing practice, and help financial institutions fulfill the regulatory requirements.

Andrew Mastin, Patrick Jaillet, Sang Chin

The minmax regret problem for combinatorial optimization under uncertainty\ncan be viewed as a zero-sum game played between an optimizing player and an\nadversary, where the optimizing player selects a solution and the adversary\nselects costs with the intention of maximizing the regret of the player. The\nexisting minmax regret model considers only deterministic solutions/strategies,\nand minmax regret versions of most polynomial solvable problems are NP-hard. In\nthis paper, we consider a randomized model where the optimizing player selects\na probability distribution (corresponding to a mixed strategy) over solutions\nand the adversary selects costs with knowledge of the player's distribution,\nbut not its realization. We show that under this randomized model, the minmax\nregret version of any polynomial solvable combinatorial problem becomes\npolynomial solvable. This holds true for both the interval and discrete\nscenario representations of uncertainty. Using the randomized model, we show\nnew proofs of existing approximation algorithms for the deterministic model\nbased on primal-dual approaches. Finally, we prove that minmax regret problems\nare NP-hard under general convex uncertainty.\n

Alexander Eisl, S. Gasser, Karl Weinmayer

Bitcoin is an unregulated digital currency originally introduced in 2008 without legal tender status. Based on a decentralized peer-to-peer network to confirm transactions and generate a limited amount of new bitcoins, it functions without the backing of a central bank or any other monitoring authority. In recent years, Bitcoin has seen increasing media coverage and trading volume, as well as major capital gains and losses in a high volatility environment. Interestingly, an analysis of Bitcoin returns shows remarkably low correlations with traditional investment assets such as other currencies, stocks, bonds or commodities such as gold or oil. In this paper, we shed light on the impact an investment in Bitcoin can have on an already well-diversified investment portfolio. Due to the non-normal nature of Bitcoin returns, we do not propose the classic mean-variance approach, but adopt at Conditional Value-at-Risk framework that does not require asset returns to be normally distributed. Our results indicate that Bitcoin should be included in optimal portfolios. Even though an investment in Bitcoin increases the CVaR of a portfolio, this additional risk is overcompensated by high returns leading to better risk-return ratios.