Those who can capture more of the actual reality that is the markets will have much more opportunities than those who do not. In our example the ability to deal with sharp market shifts or not is a serious edge Bob has over the textbook model of Alice. 11/11
Rough Volatility is a newer concept in financial mathematics that depicts how market volatility behaves. It's named 'rough' due to the irregular and complex patterns it uncovers in volatility data, akin to a rugged landscape rather than smooth changes. 1/11
Adopting Rough Volalitilty and other model improvements is not just about feeling smart using modern mathematical models. It is about getting our models as close to reality as we can. Capturing as much of the market dynamics as possible. 10/11
Choosing the right (dis)similarity metric and transformation functions can significantly impact your trading strategy's performance. A well-informed choice, based on the specific application and the attributes of the function will help your strategies signal reaction function 7/7
The use of similarity and dissimilarity metrics is fundamental to many quantitative finance tasks, such as portfolio optimization and pairs trading. This thread aims to shed light on these key concepts and their applications in finance. Correlation is not the only way. 1/7
To convert dissimilarities into similarities (essential for correlation analysis and portfolio diversification), we can employ functions like f(x) = 1 - x, with x = distance/sum(distances), or use flexible kernel functions like the Gaussian kernel. 6/7