Ethereum (ETH) is a cryptocurrency that resembles a combination of a currency, a stock, and a commodity. It is a non-dividend-paying crypto asset with a dynamic supply change parameter. Ethereum options have been traded since 2019. Deribit is the largest ETH options exchange by volume, with a market share of …

Forecasting is important in finance, as it helps investors, analysts, and institutions make informed decisions under uncertainty. Up to now, most forecasting techniques have relied on traditional time series methods, such as ARIMA, GARCH, and exponential smoothing. However, with recent advancements in machine learning and artificial intelligence, these technologies have …

Tail risk hedging is a strategy designed to protect portfolios against extreme market moves that occur infrequently but have a significant impact when they do. These “tail events” lie at the far ends of a return distribution and often coincide with financial crises, sharp market crashes, or systemic shocks. A …

Thanks to the advancement in computing technologies, we’re seeing more widespread use of machine learning, especially deep learning, in the financial services sector. It’s no longer just a theoretical tool; it’s showing up in everything from credit risk models to algorithmic trading strategies. Reference provides a comprehensive review of …

Financial asset fragility refers to the vulnerability of an asset’s price to sudden and disproportionate changes in response to shocks, even if those shocks are relatively small. This fragility often stems from factors like excessive leverage, crowded positioning, liquidity mismatches, or overreliance on certain market assumptions. Reference utilized the …

Low-volatility investing is a strategy that focuses on stocks with historically lower price fluctuations, aiming to achieve strong risk-adjusted returns. Despite conventional finance theory suggesting that higher risk should lead to higher returns, research has shown that low-volatility stocks often outperform their high-volatility counterparts on a risk-adjusted basis. By reducing …

Machine learning and AI are transforming investing by enabling data-driven decision-making, uncovering hidden patterns, and automating complex strategies. From algorithmic trading and portfolio optimization to risk management and sentiment analysis, AI-driven models process vast amounts of data with speed and precision, identifying opportunities that traditional methods might miss. Most ML …

Managing portfolios using volatility as a filter has proven effective. Reference builds on this research by proposing the use of volatility of volatility for portfolio management. The rationale behind using volatility of volatility is that it represents uncertainty. Unlike risk, which refers to situations where future returns are unknown …

Liquidity is an overlooked research area, yet it plays a crucial role in financial markets. Trading system developers often use the bid-ask spread as a proxy for liquidity, but this approach is less effective in the options market. Reference proposes a method for integrating liquidity into the option pricing …

Stress testing and scenario analysis are essential tools in portfolio management, helping portfolio and risk managers assess potential vulnerabilities under extreme market conditions. By simulating adverse scenarios such as financial crises, interest rate shocks, or geopolitical events, these techniques provide insights into how a portfolio might behave under stress and …