  Cryptocurrency market is known for exhibiting significantly higher volatility
than traditional asset classes. Efficient and adequate risk calculation is
vital for managing risk exposures in such market environments where extreme
price fluctuations occur in short timeframes. The objective of this thesis is
to build a real-time computation workflow that provides VaR estimates for
non-linear portfolios of cryptocurrency derivatives. Many researchers have
examined the predictive capabilities of time-series models within the context
of cryptocurrencies. In this work, we applied three commonly used models -
EMWA, GARCH and HAR - to capture and forecast volatility dynamics, in
conjunction with delta-gamma-theta approach and Cornish-Fisher expansion to
crypto derivatives, examining their performance from the perspectives of
calculation efficiency and accuracy. We present a calculation workflow which
harnesses the information embedded in high-frequency market data and the
computation simplicity inherent in analytical estimation procedures. This
workflow yields reasonably robust VaR estimates with calculation latencies on
the order of milliseconds.
