英文标题：
《Forecasting the Volatilities of Philippine Stock Exchange Composite
Index Using the Generalized Autoregressive Conditional Heteroskedasticity
Modeling》
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作者：
Novy Ann M. Etac and Roel F. Ceballos
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最新提交年份：
2019
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英文摘要：
This study was conducted to find an appropriate statistical model to forecast the volatilities of PSEi using the model Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Using the R software, the log returns of PSEi is modeled using various ARIMA models and with the presence of heteroskedasticity, the log returns was modeled using GARCH. Based on the analysis, GARCH models are the most appropriate to use for the log returns of PSEi. Among the selected GARCH models, GARCH (1,2) has the lowest AIC value and also has the highest LL value implying that GARCH (1,2) is the best model for the log returns of PSEi.
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中文摘要：
本研究旨在利用广义自回归条件异方差（GARCH）模型，寻找一个合适的统计模型来预测PSEi的波动性。使用R软件，使用各种ARIMA模型对PSEi的对数收益进行建模，并且在存在异方差的情况下，使用GARCH对对数收益进行建模。根据分析，GARCH模型最适合用于PSEi的对数收益。在所选的GARCH模型中，GARCH（1,2）的AIC值最低，LL值也最高，这意味着GARCH（1,2）是PSEi对数收益的最佳模型。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics 统计学
二级分类：Computation 计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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