Témata prací (Výběr práce)Témata prací (Výběr práce)(verze: 336)
Detail práce
   Přihlásit přes CAS
Portfolio diversification with cryptoassets
Název práce v češtině: Diverzifikace portfolia pomocí kryptoaktiv
Název v anglickém jazyce: Portfolio diversification with cryptoassets
Akademický rok vypsání: 2016/2017
Typ práce: bakalářská práce
Jazyk práce: angličtina
Ústav: Institut ekonomických studií (23-IES)
Vedoucí / školitel: prof. PhDr. Ladislav Krištoufek, Ph.D.
Řešitel: skrytý - zadáno vedoucím/školitelem
Datum přihlášení: 13.06.2017
Datum zadání: 13.06.2017
Datum a čas obhajoby: 29.01.2019 09:00
Místo konání obhajoby: Opletalova - Opletalova 26, O105, Opletalova - místn. č. 105
Datum odevzdání elektronické podoby:01.01.2019
Datum proběhlé obhajoby: 29.01.2019
Oponenti: PhDr. Boril Šopov, M.Sc., LL.M.
 
 
 
Kontrola URKUND:
Seznam odborné literatury
1. CHOWDHURY, Abdur. Is Bitcoin the “Paris Hilton” of the Currency World? Or Are the Early Investors onto Something That Will Make Them Rich?. The Journal of Investing, 2016, 25.1: 64-72.
2. BRIÈRE, Marie; OOSTERLINCK, Kim; SZAFARZ, Ariane. Virtual currency, tangible return: Portfolio diversification with Bitcoins. 2013.
3. DE ROON, Frans A.; NIJMAN, Theo E.; WERKER, Bas JM. Testing for spanning with futures contracts and nontraded assets: A general approach. Tilburg: Center for Economic Research, Tilburg University, 1996.
4. DASKALAKI, Charoula; SKIADOPOULOS, George. Should investors include commodities in their portfolios after all? New evidence. Journal of Banking & Finance, 2011, 35.10: 2606-2626.
5. PEIRO, Amado. Skewness in financial returns. Journal of Banking & Finance, 1999, 23.6: 847-862.
6. HUBERMAN, Gur; KANDEL, Shmuel. Mean‐variance spanning. The Journal of Finance, 1987, 42.4: 873-888.
7. DEROON, Frans A.; NIJMAN, Theo E. Testing for mean-variance spanning: a survey. Journal of empirical finance, 2001,
8.2: 111-155. 8. BELOUSOVA, Julia; DORFLEITNER, Gregor. On the diversification benefits of commodities from the perspective of euro investors. Journal of Banking & Finance, 2012, 36.9: 2455-2472.
9. KAN, Raymond, et al. Tests of mean-variance spanning. 2001.
10. ALI, Robleh, et al. Innovations in payment technologies and the emergence of digital currencies. 2014.
Předběžná náplň práce v anglickém jazyce
Topic characteristics

In my bachelor thesis, I would like to analyze in-sample portfolio effects resulting from the addition of two major cryptoassets (namely Bitcoin, Ethereum) to different kinds of portfolio (including stocks, bonds, hard currencies, commodities and hedge funds). The analysis is conducted over the sample of 7 years (2010 - 2017) using investors with a standard static asset allocation.

Motivation

Although according to Robleh Ali et al. (2014) the key innovation of Bitcoin is “distributed ledger” or “blockchain” which is already supported by many academic papers, its recent surge in market value can’t be ignored. As of May 2017 Bitcoin’s market cap reached as high as $47 billion. Since its creation (2008), Bitcoin has evolved from a mathematical proof of a concept to a rapidly expanding economic network. Its decentralized character, open source operating basis and limited supply securing impossibility of inflation policy made Bitcoin a very unique asset. Unfortunately there are also negatives in decentralized ecosystems, for instance it makes it harder to reach a consensus in certain very important themes. Paradoxically the recent increased demand caused community disagreement on how the system should expand further to handle more transactions at the time for less costs . Lack of authority, apart from others, created room for growth of other cryptocurrencies such as Ethereum. Personage of co-founder Vitalik Buterin appears to be a very important asset in connecting decentralized cryptocurrency world with public. Formation of Enterprise Ethereum Alliance supported by J.P. Morgan Chase, Microsoft, and Intel or latest tokenization of Singaporean Dollar through Ethereum’s Blockchain increased “Ether’s” market cap as high as $34 billion which Bitcoin first reached just in May of 2017.Even considering all this information, there is still a shortage of economic perspective on the topic. That’s why I decided to perform one of the first such analyses with both Bitcoin and Ethereum from investor’s and portfolio optimalization standpoint.


Contribution

My thesis improves upon Chowdhury (2014) and Brière et al (2013) in two asspects. Firstly I extend the sample period by almost 4 year long period which means inclusion of the biggest Bitcoin bubble burst in first half of 2014.In contrast to previous research I am able to analyse periods with considerable bearish sentiments and eliminate signs of early-stage behaviour.

Hypotheses

• Does introduction of Bitcoin and Ethereum in the investor’s asset universe yields diversification benefits within an in-sample mean-variance framework?
• Are the results robust for investors with utility function inconsistent with the mean-variance setting?

Methodology and Data

In previous years, many studies (see e.g. Brière et al (2013)) evaluated the diversification impact of commodities in the mean–variance framework of Markowitz (1952) to test the impact of the introduction of additional N risky assets (test assets) on the efficient frontier of an investment opportunity set of K benchmark assets. However, Daskalaki and Skiadopoulos (2011) have shown that violation of two important assumptions often occurs. Namely that asset returns are not distributed normally (see e.g., Peiro, 1999) and that investor’s preferences are not that well described by a quadratic utility function.With that beaing said I decided to follow (DeRoon et al., 1996) rigorous robust in-sample spanning test. I consider investor holding a diversified portfolio comprising both traditional assets (worldwide stocks, bonds, hard currencies) and alternative investments (commodities, hedge funds, real estate) represented by several liquid financial indices. Weekly closing prices of Bitcoin and Ethereum are also used. Finnaly, I investigate relative performances of protfolio with and without cryptoassets using Wald test statistics and respective p-values. Descriptive statistics containing Sharp ratio, coefficients of kurtosis and skewness are presented with additional comparison to previous papers.

Outline

1. Introduction
2. Literature Review
3. Features of Bitcoin and Ethereum
4. Methodology
5. Data
6. Results
7. Conclusion
 
Univerzita Karlova | Informační systém UK