Real-time VaR Calculations for Crypto Derivatives in kdb+/q

TL;DR

This paper develops a real-time workflow for calculating Value at Risk (VaR) for cryptocurrency derivatives using high-frequency data and analytical models, achieving millisecond latency for risk management in volatile markets.

Contribution

It introduces a novel real-time VaR calculation workflow for crypto derivatives that combines high-frequency data analysis with efficient analytical models, enhancing speed and accuracy.

Findings

Achieves VaR calculation latency of milliseconds.

Demonstrates robustness of VaR estimates in volatile crypto markets.

Combines high-frequency data with analytical models for improved risk assessment.

Abstract

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.