Value-at-risk
Introduction
In the intricate world of finance, where myriad factors converge to determine the trajectory of investments, there exists a sophisticated tool designed to quantify potential loss: the Value at Risk, or VaR. Esteemed and pivotal, VaR serves as an essential metric in modern risk management practices.
Understanding VaR
VaR, in its essence, quantifies the maximum potential loss an investment portfolio could encounter over a defined period for a specified confidence interval. For instance, if a portfolio has a one-day VaR of $1 million at a 95% confidence level, there is a 5% probability that the portfolio will experience a loss exceeding $1 million over a single day.
Methodologies Employed
Three primary approaches dominate the VaR calculation landscape:
Parametric VaR: Rooted in the assumption of a specific distribution, typically the normal distribution, this approach employs the mean and variance of returns.
Semi-Parametric VaR: A hybrid approach that combines the strengths of parametric and non-parametric methodologies, offering a more flexible modeling structure.
Non-Parametric VaR: This approach is entirely data-driven, leveraging historical data without superimposing a predetermined distribution.
Advantages and Limitations
VaR's prominence arises from its clear-cut representation of risk. Financial entities, ranging from investment banks to regulatory bodies, frequently employ VaR for its:
Clarity: Provides a concise measure of potential losses.
Adaptability: Applicable across diverse assets and portfolios.
Regulatory Compliance: Many regulatory frameworks either recommend or mandate VaR as a risk metric.
However, it's crucial to recognize VaR's inherent limitations:
Tail Risk Blindness: VaR does not provide a complete picture of losses beyond its confidence level.
Assumption Sensitivity: Particularly in the Variance-Covariance approach, the assumption of normally distributed returns might not always be fitting.
Value at Risk (VaR) in Cryptocurrencies: An Insightful Overview
Navigating the volatile crypto market requires robust tools, and the Value at Risk (VaR) stands out in risk quantification. Our study demystifies VaR's mathematical basis and evaluates its three methodologies: Parametric, Semi-Parametric, and Non-Parametric. Through rigorous backtesting, we gauge each method's accuracy against actual crypto market data. The findings not only showcase VaR's relevance in crypto portfolio management but also its potential in shaping trading strategies. For an in-depth exploration, my full research paper (completed during my internship at a data provider called Kaiko) is available below:
