The rapid rise in volatility of Bitcoin has many traders and investors worried about their digital assets. How can one manage BTC risk in a world of unknown values? Despite the volatility of Bitcoin, there are ways to measure risk and determine potential opportunities. A robust approach starts with understanding the fundamentals of Bitcoin and blockchain technology. Next, it’s important to identify key risk factors and assess their potential impact on prices. Finally, it must develop a strategy to mitigate those risks while pursuing opportunities.
The global financial crisis of 2008 was a turning point for many people, including those in the financial industry. At the time, some rogue traders were betting on risky mortgage-backed securities, and their actions led to the near-collapse of the global financial system. This experience has led many financiers to adopt risk management techniques that have helped prevent another disaster. One such technique is forecasting.
How to manage BTC risk in a world of unknown values?
The answer is to use the right tools for your job. There are many ways to measure risk and price volatility: standard deviation, variance, sharpe ratio, etc. Most people think these are interchangeable measures with similar properties because they tell you how much your returns vary over time; however, they measure different things. Standard deviation tells you how far away each point on a chart is from the mean (average). Variance measures how much those deviations change from day to day.
Sharpe ratio compares standard deviation to return, but it doesn’t account for lost opportunities or negative returns. The Black-Scholes formula is used to price options on financial instruments. You probably have seen our bitcoin information page explaining how to use this equation to estimate bitcoin’s fair value. However, if you are new to it or want a refresher, here is a simple explanation: The first thing we do is calculate something called volatility (vol):
Volatility = annualised standard deviation ÷ sample size
Forecast Accuracy Measure
Forecast accuracy measures are used to quantify forecast performance. They provide a way of comparing forecasts with actual outcomes and can be used to identify anomalies that might suggest a need for further investigation. When analysing forecast accuracy, you will typically be interested in four types of error:
- Bias: The difference between your current forecast and its long-term average (or mean) value. A positive bias means that your current estimate is higher than expected. A negative bias indicates lower than expected values
- Variance: The degree of dispersion around the mean value of your forecast series over time
- Accuracy: A measure of how closely each observation in the actual series matches its respective predicted value from the model. This is also known as cross-validation accuracy or calibration efficiency because it assesses whether or not observations are correctly assigned their corresponding probability based on values predicted by your model
VaR and CVaR exhibit significant time-varying behaviours driven by asymmetric fat-tailed distributional features. In addition, they observe that the marginal contribution of volatility to CVaR is positive while it is negative for VaR. This suggests that the greater the standard deviation, the higher the asset’s riskiness, especially when making short sales.
To account for uncertainties and build robust models for VaR and CVaR forecasting, they employ stochastic programming with discretely observed data and develop a new powerful algorithm for solving the resulting optimization problems. Their approach is based on a non-linear model of bitcoin’s price dynamics, which allows them to determine the model parameters jointly with the optimal payoff function.
The resulting problem is solved by numerical integration involving Fourier series expansions of the solution function. Experiments show that this approach can provide accurate estimates even when there are strong market movements or short periods of high volatility in bitcoin prices.
Bitcoin is a new and rapidly growing digital asset class that has captured the attention of many investors. The above-mentioned method for forecasting risk measures for Bitcoin investments is based on historical data from other assets and can help investors make more informed decisions about their risk tolerance and investment goals.