In financial time series data of a stock market, what type of model would be ideal for predicting future stock prices considering past trends and volatilities?

GARCH models are well-suited for financial time series data as they account for volatility clustering and changing variances over time. ARIMA and LSTM are more focused on capturing patterns in the mean, while Random Forest is generally not used for time series forecasting in financial markets.