Data-driven risk forecasting and algorithmic trading models for cryptocurrencies
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Date
2022-11-23
Authors
Singh, Japjeet
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Abstract
Risk modelling is an active area of research in computational finance, which involves design of models for quantifying and forecasting the uncertainty associated with investments. Unlike traditional financial assets, the risk modelling for blockchain powered digital assets is rarely studied. The main contributions of this thesis lie in design and application of the novel data-driven models to risk forecasting of the price and algorithmic returns of cryptocurrency assets.
In our first study, we proposed data-driven and neuro-volatility risk forecasting models for cryptocurrencies. Our results are quantified with two widely used risk metrics - Value at Risk (VaR) and Expected Shortfall (ES). Both the data-driven and neuro-volatility estimates demonstrated a significantly higher risk for cryptocurrencies as compared to traditional technology stocks. We observed that the data-driven models produced better forecasts for cryptocurrencies, while better forecasts resulted for the regular stocks and indexes with the neuro-volatility model. Also, the data-driven models are more efficient in terms of computation time. We did another study with the algorithmic returns for simple moving average crossover strategy using Sharpe ratio as risk quantifier, which we extended to develop a novel algorithmic trading strategy for cryptocurrencies. As a case study, we applied this knowledge to decentralized finance (DeFi) ecosystem.
The main findings from the studies indicate superiority of the data-driven approach proposed in this research for the risk forecasting problems. The data-driven approach performed equivalently well or better compared to neuro volatility models in different studies reported in this thesis and also that the data-driven approach was significantly efficient in terms of computation time in each of these studies.
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Keywords
Cryptocurrency, Risk forecasting, Volatility forecasting, Data-driven models, Neuro volatility models, Fuzzy intervals, Fuzzy alpha cuts, Value at risk (VaR), Expected shortfall (ES), Algorithmic trading, Sharpe ratio, Long short-term memory (LSTM), Algo returns, Decentralized Finance (DeFi), Decentralized Exchange (DeX), Liquidity pools, Uniswap protocol