Seasonal Hybrid Extreme Studentized Deviate Test (S-H-ESD): Identifies seasonal anomalies by filtering for residuals outside the expected seasonal pattern.
10. Econometric Models
Vector Autoregression (VAR): Used for multivariate time series to capture linear interdependencies between multiple variables.
Cointegration Analysis: Tests for long-term equilibrium relationships between two or more time series, common in pairs trading.
Error Correction Models (ECM): Extends cointegration to account for short-term deviations while preserving long-term equilibrium, often used in currency and bond markets.
11. Factor Models and PCA
Principal Component Analysis (PCA): Reduces dimensionality by identifying key components or factors in high-dimensional financial data.
Factor Models (e.g., Fama-French, CAPM): Capture sources of risk and return by modeling asset returns as a function of various factors, used in portfolio analysis.
12. Volatility Forecasting
EWMA (Exponentially Weighted Moving Average): Simplified model to forecast volatility that places greater weight on recent observations.
GARCH-family Models: Includes extensions like EGARCH, TGARCH that account for asymmetric responses to positive and negative shocks, common in stock returns.
13. Event-Driven & Regime-Switching Models
Markov Regime-Switching Models: Capture sudden changes in behavior by switching between multiple regimes, e.g., bear and bull markets.
Event Study Analysis: Examines the impact of specific events (e.g., earnings announcements, economic reports) on asset prices by isolating abnormal returns.
These methods often complement each other, with a typical analysis involving several techniques to capture different aspects of the data, from trend to seasonality, volatility, and structural breaks.
10. Econometric Models
Vector Autoregression (VAR): Used for multivariate time series to capture linear interdependencies between multiple variables.
Cointegration Analysis: Tests for long-term equilibrium relationships between two or more time series, common in pairs trading.
Error Correction Models (ECM): Extends cointegration to account for short-term deviations while preserving long-term equilibrium, often used in currency and bond markets.
11. Factor Models and PCA
Principal Component Analysis (PCA): Reduces dimensionality by identifying key components or factors in high-dimensional financial data.
Factor Models (e.g., Fama-French, CAPM): Capture sources of risk and return by modeling asset returns as a function of various factors, used in portfolio analysis.
12. Volatility Forecasting
EWMA (Exponentially Weighted Moving Average): Simplified model to forecast volatility that places greater weight on recent observations.
GARCH-family Models: Includes extensions like EGARCH, TGARCH that account for asymmetric responses to positive and negative shocks, common in stock returns.
13. Event-Driven & Regime-Switching Models
Markov Regime-Switching Models: Capture sudden changes in behavior by switching between multiple regimes, e.g., bear and bull markets.
Event Study Analysis: Examines the impact of specific events (e.g., earnings announcements, economic reports) on asset prices by isolating abnormal returns.
These methods often complement each other, with a typical analysis involving several techniques to capture different aspects of the data, from trend to seasonality, volatility, and structural breaks.
