Financial Time Series: Stylized Facts and Models

Related lectures (32)

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

Explores long memory in time series and Autoregressive Conditional Heteroskedasticity processes in financial data.

Explores risk and return in finance, covering securities' performance, rates of return, variance, volatility, and portfolio diversification.

Covers stylized facts in finance and reproducible research in scientific computing.

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

Delves into implied volatility, historical volatility, option pricing implications, and various volatility models in financial innovation.

Explores forecasting in time series analysis, long memory processes, and ARCH models for volatility modeling.

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Explores the fundamentals of time series analysis, including stationarity, linear processes, forecasting, and practical aspects.

Explores ARCH and GARCH models, volatility clustering, time series, estimation, and filtering steps in financial and macroeconomic contexts.