The doctoral course "Dependence Structures in High Frequency Financial Data" focuses on sophisticated methods for detecting dependencies in financial datasets. It emphasizes the automatic detection of relationships among multiple vectors, employing advanced statistical and mathematical techniques. Key topics include identifying various patterns in data such as time-dependent trends, volatility clustering, seasonality, and the presence of fat tails. The course extensively covers the application of copulas—tools used to model and analyze the dependence between random variables—and spectral measures, which help in understanding the frequency components of dependencies. Through theoretical instruction and practical applications, students learn to analyze complex dependencies in high-frequency financial data, preparing them for research and applications in quantitative finance.