Previsione della volatilità, indicatori e tecniche di trading

This thesis examines volatility trading with particular attention on investors who seek excess returns in periods of stability by shorting derivatives products. As will be evident, to do that I followed a scheme that gradually deepens my analysis into the topic because I needed to make my knowledge base stronger to face arguments that have become progressively more challenging. The first three chapters have a more technical soul. Chapter 1 introduces the concept of volatility, historical and implied, and replicates volatility smile and volatility surface starting from real VIX Options datasets. This is preparatory for the description of VIX, the most important volatility index continuously updated by Chicago Board Options Exchange based on S&P 500 index. VIX is also known as ”Fear Gauge” essentially because, a higher level of it, mirrors a high probability of market turmoil, with particular attention to the US. Pandemic chronicles led me to study this tool calculated on S&P 500 options. The third chapter shows a procedure to understand if VIX is underestimated or not to help market participants in choosing trades able to make their portfolio more valuable. In the following part, I mentioned the short volatility trading, the division between explicit and implicit tools, and the main issues that go hand in hand with those kinds of strategies: risk related to gamma exposure, volatility, correlation, and interest rate, passive investing and machine learning. It was then the turn to analyse better explicit short volatility trading instruments like the options, variance swaps, and Futures. In Chapter 6 and 7 are examined some common trading strategies based on these products. For what concerns futures and indexes adopted, the most important futures and strategic indexes are published by CBOE and S&P Global and each one is based either on VIX or directly on S&P 500 index. An interesting application of these tools useful to organize trading strategies consists in understanding if the market is currently in Contango or Backwardation. This experiment allows matching backwardation periods with uncommon and often disruptive facts in the market considering the last 15 years. It was then the turn of describing some VIX Allocation Strategy Indexes. A portfolio that replicates their philosophy substantially creates short exposures in rolling one-month implied volatility. VPD, VPN, VSTG, VXTH are the tickers of some of the more adopted and profitable strategic indexes of the last decade. Chapter 8 introduces SPIKES by MIAX, a new volatility index that is trying to compete with VIX. In doing so, SPIKES issuers have been focused on technological development introducing inno?vative techniques like price dragging during the selection of SPY options instead of SPX adopted by VIX. SPY is the most traded ETF in the world with a great level of liquidity, this feature should help in avoiding arbitrage practices and calculating a reliable estimate of the then-current volatility. Other interesting traits of SPIKES are the update speed (100 milliseconds against the 15 seconds of VIX) and lower fees. It is then the turn to test, with some descriptive statistics, the ability of VIX, SPIKES and VIX Put/Call ratio to explain residuals obtained through autoregression on S&P 500 index. This study should help identifying what indexes have a good capability to explain changes in the benchmark not due to economical factors, but to the current market sentiment.