| Περίληψη: | In recent years, the cryptocurrency market has gained increased interest among
investors, academics, policy makers and regulators all over the world, aiming to
understand the unique characteristics and dynamics of cryptocurrencies price
formation. The highly volatile nature and jump behavior in cryptocurrency markets as
compared to traditional currencies, highlights the importance of defining a good proxy
of volatility; this, will allow us to also account for the discontinuous movements in
cryptocurrency prices to accurate model and forecast volatility.
The overall purpose of our research is to model and forecast the volatility of
cryptocurrencies, considering its critical role in many applications such as decision
making, risk management and hedging. To this end, we consider the Heterogeneous
Autoregressive model of Realized Volatility (HAR-RV). We utilize realized variance
estimated from high frequency cryptocurrency price data. Specifically, we propose a
non-parametric approach to estimate realized volatility and subsequently decompose
it into continuous path variation and discontinuous path variation, to further estimate
realized volatility jumps. First, we assess the predictive value of transaction activity in
the Bitcoin Blockchain network, in an attempt to forecast the realized volatility of
Bitcoin returns. We further explore extended versions of the HAR-RV specification,
that include upside and downside jumps measures (positive and negative realized
volatility jumps).
The second area of interest focuses on the interconnectedness in the cryptocurrency
market. To this end, we measure spillovers among major cryptocurrencies in terms of
market capitalization, namely Bitcoin, Ripple, Ethereum and Litecoin. Specifically,
we study the interconnectedness in the cryptocurrency market. We employ the
proposed HAR-RV framework and its various extensions with the inclusion of
different jump variations. In a univariate level of analysis, we examine the
significance of heterogeneity and discontinuity to predict realized volatility of
cryptocurrencies based on the HAR-RV framework. Additionally, we explore the
spillover transmission mechanism among cryptocurrencies realized volatility by
examining the relevance of realized volatility jumps and covariances. For this reason,
we perform a comparative spillover analysis at a multivariate level of analysis.
Multivariate HAR models (MHAR) are further examined in two versions, with and
without the inclusion of covariances.
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