This study employs machine learning models to forecast and comprehend the implied volatility of China ETF50. We develop a hybrid model named LSTM-ML, leveraging historical implied volatility, moneyness, and time-to-maturity as input features. The LSTM component captures dynamic hidden...

I present evidence that a moving average (MA) trading strategy has a greater average return and skewness as well as a lower variance compared to buying and holding the underlying asset using monthly returns of value-weighted US decile portfolios sorted by market size, book-to-market, and...

Introduction -- ARMA models -- Forecasting stationary processes -- Estimation of Mean and Autocovariance Function -- Estimation of ARMA Models -- Spectral Analysis and Linear Filters -- Integrated Processes -- Models of Volatility -- Multivariate Time series -- Estimation of Covariance Function...