Abstract
This dissertation investigates the incorporation of Bitcoin into investment portfolios by
employing advanced statistical methods to improve portfolio optimisation and risk
management. The study investigates the effectiveness of pairs trading methods in
both advanced and emerging economies. It uses copula functions and quantile
cointegration to explore the correlations between Bitcoin and conventional financial
assets. The research commences by presenting the background, goals and
assumptions, underscoring the significance of sophisticated statistical techniques in
optimising portfolios that include cryptocurrency. The study seeks to evaluate the
efficacy of incorporating Bitcoin, analyse pairs trading techniques using copula
functions and quantile cointegration and compare the results across various economic
environments.
A comprehensive literature study offers valuable insights on the progression of
portfolio optimisation, the advantages of diversification and the distinctive attributes of
cryptocurrencies. The analysis highlights deficiencies in the current research,
including the absence of a comparative examination of the incorporation of
cryptocurrencies in both advanced and emerging economies. This review provides a
rationale for using copula functions and quantile cointegration as the main approaches
for this investigation. The methodology chapter provides a detailed description of the
techniques used to acquire and analyse data. It involves the use of financial data from
different economic contexts, specifically focusing on Bitcoin and traditional equities.
Copula functions are used to model the links between Bitcoin and traditional assets,
while quantile cointegration captures the long-term equilibrium relationships at various
quantiles. This provides a strong framework for implementing pairs trading techniques.
The performance evaluation centres on the return on used capital, assessing the
effectiveness and profitability of the trading techniques.
The results and discussion chapter showcases the empirical findings, which unveil
clear patterns in the data and demonstrate the effectiveness of copula functions and
quantile cointegration in optimising portfolios. The main discoveries are as follows.:
The examination of copulas showed considerable interdependencies between Bitcoin
and several market indices. Various copulas, namely Gaussian, Student-t and Clayton
copulas, were able to capture symmetric, tail and asymmetric dependencies. The
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approach of quantile cointegration offers a reliable analysis of non-linear connections
and identifies stable couples for trading. The investigation verified the existence of
stable and balanced correlations over a long period of time between Bitcoin and
specific market indexes.
The examination of the Sharpe ratio indicated that incorporating Bitcoin generally
enhanced the performance of the portfolio. The findings demonstrated that pairs
trading strategies using quantile cointegration outperformed those applying copula
functions, especially in advanced nations.
The study revealed notable disparities in the performance of investment portfolios
across advanced and emerging economies, emphasising the significance of the
economic environment in optimising portfolios.
The concluding chapter integrates the findings, delivering a thorough conclusion and
proposing suggestions for future study and practical implementations. The paper
highlights the strategic advantages of including Bitcoin in conventional investment
portfolios, emphasising the possibility of using sophisticated statistical methods to
improve portfolio optimisation and mitigate risks in the unpredictable cryptocurrency
markets. The statement proposes that authorities establish frameworks to supervise
the incorporation of cryptocurrencies and advises conducting additional studies on
emerging asset classes and technology developments in portfolio management. In
summary, this dissertation provides essential knowledge regarding the incorporation
of cryptocurrencies into investment portfolios. It showcases the potential of using
modern statistical techniques to enhance portfolio optimisation and properly handle
risks. The findings provide practical direction for investors, politicians and researchers,
facilitating more informed and strategic investment decisions in the digital era.