In one of our older posts titled ‘Case Study: Quantpedia’s Composite Seasonal / Calendar Strategy,’ we offer insights into seasonal trading strategies such as the Turn of the Month, FOMC Meeting Effect, and Option-Expiration Week Effect. These strategies, freely available in our database, are not only examined one by one, but are also combined and explored as a cohesive composite strategy. In partnership with Deltaray, using MesoSim — an options strategy simulator known for its unique flexibility and performance — we decided to explore and quantify how our Seasonal Strategy performs when applied to options trading. Our motivation is to investigate whether this strategy can be improved in terms of risk and return. We aim to systematically harvest the VRP (volatility risk premium) timing the entries using calendar strategy to avoid historically negative trading days.

What does one of the most popular and well-known metrics, VIX, tell us about future returns? Academic research (Bansal and Stivers, July 2023) shows that a common, intuitive 20/80 thumb rule can be applied as time-variation in the returns earned from equity-market exposure can be explained well by a simple 2-term risk-return specification, which predicts (1) much higher returns 20% of the time following after VIX exceeds a high threshold at around its 80th percentile and (2) lower excess returns following a high market sentiment. They argue that VIX and market sentiment tend to measure complementary aspects of risk: the level of risk (VIX) and the price of risk or risk appetite (sentiment), and that, thus, both terms should be accounted for when evaluating time variation in the equity market’s risk premium.

The expected return on the “variance factor” known as variance risk premium (VRP) is nothing new to options markets. For any investor interested in benefitting from this phenomenon, we present the study of Dörries et al. (2021), which provides a clear overview of different VRP-earning strategies.

Over the last few years, we may have noticed a significant growth in retail investing. No surprise, the COVID pandemic outbreak increased the numbers even more, and undoubtedly, options trading is no exception. According to the authors (de Silva, Smith, Co), retail traders seek options expecting spikes in volatility and, for that reason, incline toward firms with more media coverage. Furthermore, their trading increases around the time of firms’ earnings announcements. As a result, market makers benefit from the behavior mentioned above, which causes a large flow of money from retail to market makers.

Delta hedging is a trading strategy that aims to reduce the directional risk of short option strategy and reach a so-called delta-neutral position. It does so by buying or selling small increments of the underlying asset. Similarly, grid trading is a trading strategy that buys/sells an asset depending on its price moves. When the price falls, it buys and sells when the price rises a certain amount above the buying price. This article examines the similarities between delta hedging and grid trading. Additionally, it analyzes numerous versions of grid trading strategies and compares their advantages and disadvantages.

Volatility indexes are essential parts of the financial markets. They offer investable opportunities and exposure to the volatility, but most importantly, those indexes offer forward-looking measures of option-implied uncertainty. Therefore, such indexes are often used as indicators of risk or sentiment in the markets. For example, the well-known VIX index is often called the fear-index. The volatility indexes are not exclusive to the equity market. There are fixed-income option-implied volatility indexes for US Treasury futures, but the European fixed income market lacks such index. This novel research paper by Jaroslav Baran and Jan Voříšek fills this gap and proposes volatility indexes, connected to the euro bond futures using the Cboe TYVIX (US Treasury implied volatility index) (2018) methodology. As a result, the TYVIX and euro bond futures volatility indexes are directly comparable.

Authors: Jaroslav Baran and Jan Voříšek

Title: Volatility indices and implied uncertainty measures of European government bond futures