Financial Forecasting by Autoregressive Conditional Heteroscedasticity (ARCH) Family: A Case of Mexico

Abstract

Understanding and modeling the volatility measurements is important for forecasting the risk and for evaluating asset allocation decisions of stock market. The study have used the daily frequency data from January 1, 2002 to September 30, 2016 as an in-sample period to perform empirical analyses for modeling and predicting the volatility dynamics of Mexican stock market (IPC). To facilitate the variance forecast, the competing models are ARCH (p, q), GARCH (p, q), and its variations i.e. Glosten Jagnnathon Runkle GARCH, GARCH in Mean, Exponential GARCH, and Quadratic GARCH. The results of residual diagnostics suggested that stock market of Mexico is characterized by heteroskedasticity, multicolinearity, non-normality, and serial correlation. Volatility measurements by ARCH and GARCH signify that the current conditional variance of Mexico is determined by its past price behavior and previous day volatility. Today's volatility does impact the current stock returns as indicated by GARCH-M. Results of EGARCH explained that any large size news produces high volatility as compared to small size news. Effects of bad news are greater on the volatility of the Mexican stock market than good news. GJR GARCH described the asymmetric behavior of returns and variance in the politically conflicted regime during 2006-2012. Moreover, QGARCH effect is not linear. Findings have the implications for individuals and corporate investors about retaining their risky stocks.

Analysis of Rewards Results of Banking Shares Book III Registered in Indonesia Stock Exchange

“…Research [14] explained that the forecasting model with GARCH obtained statistically significant results compared to other volatility forecasting models such as MLP, GRNN, GMDH, RF, QRRF, and QRNN on eight financial data sets. It was also found that ARCH / GARCH could be used to study voltages in other fields such as agriculture ( [15] & [16]), and gold by [17]- [26] are similar in relation to the use of the ARCH / GARCH method in dealing with the symptoms of volatility. and [27].…”

Algorithm trading strategy based on GARCH and LSTM models

“…al. [7] use the daily frequency data from 2002 to 2016 as an in-sample period to perform empirical analyses for modelling and predicting the volatility dynamics of the Mexican stock market (IPC). Kim and Won [8] propose a new LSTM model and combine it with multiple GARCH-type models to forecast the volatility of the stock price index using KOSPI 200 index data.…”