Financial Time Series: Stylized Facts and Modeling

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Page 3 of 4

Covers Parseval relations, Fourier series, energy calculation, and correlation in signal processing.

Covers volatility modeling in risk management, including ARMA, ARCH, GARCH models, causal representation, and forecasting.

Covers the Metropolis-Hastings algorithm and gradient-based approaches for biasing searches towards higher likelihood values.

Provides an overview of signal models and methods in statistical signal processing.

Explores statistical independence, correlation, Gauss functions, likelihood estimation, and Gaussian Mixture Models for clustering.

Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.

Delves into quantifying statistical dependence through covariance, correlation, and mutual information.

Covers the systematic derivation of a synchronization algorithm with a generalized likelihood test in wireless receivers.

Explores parametric estimation, integrated processes, seasonal modeling, and ARIMA model building in time series analysis.

Explores stationarity in continuous-time stochastic processes, focusing on autocorrelation and cross-correlation functions.