Keywords
Stock price
Volatility
GARCH model
Submitted : 2022-09-21
Accepted : 2022-10-06
Published : 2022-10-21
Abstract
The financial market is the core of national economic development, and stocks play an important role in the financial market. Analyzing stock prices has become the focus of investors, analysts, and people in related fields. This paper evaluates the volatility of Apple Inc. (AAPL) returns using five generalized autoregressive conditional heteroskedasticity (GARCH) models: sGARCH with constant mean, GARCH with sstd, GJR-GARCH, AR(1) GJR-GARCH, and GJR-GARCH in mean. The distribution of AAPL’s closing price and earnings data was analyzed, and skewed student t-distribution (sstd) and normal distribution (norm) were used to further compare the data distribution of the five models and capture the shape, skewness, and loglikelihood in Model 4 – AR(1) GJR-GARCH. Through further analysis, the results showed that Model 4, AR(1) GJR-GARCH, is the optimal model to describe the volatility of the return series of AAPL. The analysis of the research process is both, a process of exploration and reflection. By analyzing the stock price of AAPL, we reflect on the shortcomings of previous analysis methods, clarify the purpose of the experiment, and identify the optimal analysis model.
References
Demirguc-Kunt A, Levine R, 1999, Stock Market Development and Financial Intermediaries. Stylized Facts. World Bank Economic Review, 10(2): 291–321.
Finkle TA, Mallin ML, 2010, Steve Jobs and Apple, Inc. Journal of the International Academy for Case Studies, 16(7): 31.
Ahmar A, 2016, Predicting Movement of Stock of Apple Inc. Using Sutte Indicator. Proceedings of the 3rd AISTSSE Trends in Science and Science Education, 35–38.
Mohan S, Mullapudi S, Sammeta S, et al., 2019, Stock Price Prediction Using News Sentiment Analysis. Proceedings of the 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 205–208.
Jeong J, 2022, Predicting Apple Stock Price Using News Headlines and Other Features with Classical Time Series Models, Supervised Models, and Machine Learning Models, dissertation, UCLA.
Kim JM, Jun S, 2017, Integer-Valued GARCH Processes for Apple Technology Analysis. Industrial Management & Data Systems, 117(10): 2381–2399.
Silvennoinen A, Terasvirta T, 2009, Multivariate GARCH Models, in Handbook of Financial Time Series, Springer, Berlin, Heidelberg, 201–229.
Ding X, Zhang Y, Liu T, et al., 2014, Using Structured Events to Predict Stock Price Movement: An Empirical Investigation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1415–1425.
Sharma S, Aggarwal V, Yadav MP, 2021, Comparison of Linear and Nonlinear GARCH Models for Forecasting Volatility of Select Emerging Countries. Journal of Advances in Management Research, 18(4): 526–547.
Ampountolas A, 2021, Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models. Forecasting, 3(3): 580–595.
Fa?dzinski M, Fiszeder P, Orzeszko W, 2020, Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression. Energies, 14(1): 6.
Salamat S, Lixia N, Naseem S, et al., 2020, Modeling Cryptocurrencies Volatility Using GARCH Models: A Comparison Based on Normal and Student’s T-Error Distribution. Entrepreneurship and Sustainability Issues, 7(3): 1580–1596.
Lee Y, Lee S, 2019, CUSUM Test for General Nonlinear Integer-Valued GARCH Models: Comparison Study. Annals of the Institute of Statistical Mathematics, 71(5): 1033–1057.
Emenogu NG, Adenomon MO, Nweze NO, 2020, On the Volatility of Daily Stock Returns of Total Nigeria Plc: Evidence from GARCH Models, Value-at-Risk and Backtesting. Financial Innovation, 6(1): 1–25.
Rossi E, 2004, Lecture Notes on GARCH Models, University of Pavia.
Bollerslev T, 1986, Generalized Autoregressive Conditional Heteroskedasticity. J Econometrics, 31: 307–327.