Quantifying Long-Term Volatility for Developed Stock Markets: An Empirical Case Study Using PGARCH Model on Toronto Stock Exchange (TSX)

Abstract

High frequency data is a recent entrant to the world of statistics as they relate to the markets. This study measures the volatility of S& P / Toronto index by utilizing the GARCH family models (EGARCH, TGARCH, MGARCH and PGARCH models) using a daily database counted 7636 observations. The empirical results for the selected index for the whole-time frame show that it is volatile, and the PGARCH is a better-fitted model with the student's t distribution. Positive shocks have a greater impact on conditional volatility than negative shocks, according to the data. These extra analyses can be supplied to further corroborate their results and provide useful information for economic thespians interested in adding Toronto stock market index to their investment portfolios.

Keywords

• volatility
• forecasting
• pgarch model
• garch
• developed stock market