Stock Price Forecasting Using A Dependence Structure
##plugins.themes.bootstrap3.article.main##
It is important to incorporate diverse dependence structures between stocks when managing a stock portfolio. Copulas are a useful statistical tool to capture dependence structure, dealing with both the linear and non-linear association that may occur in the tails of data. Financial time series datasets often exhibit volatility clustering that affects price forecasting accuracy. This work proposes the initial use of the principal component analysis followed by a copula and GARCH model that filters the effect of the volatility clustering in the series. For illustration, we consider ten banks from which Bank of America and PNC Financial Services Group are chosen, and then we project their future price movements through simulations. Since they are selected in terms of the principal component analysis, the procedures could help the proposed model to become a more widely used tool in forecasting financial stock performance.
References
-
Hotelling H. Analysis of Complex of Statistical Variables into Principal Components. Journal of Educational Psychology. 1993; 24: 417-441.
Google Scholar
1
-
Tsay RS. Analysis of Financial Time Series, Wiley; 2002.
Google Scholar
2
-
Bollerslev T. Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics. 1986;31: 307-327.
Google Scholar
3
-
Engle R. Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica. 1982; 50: 987-1007.
Google Scholar
4
-
Embrechts P, McNeil A, Straumann D. Correlation and Dependency in Risk Management: Properties and Pitfall, In Risk Management: Value at Risk and Beyond, ed. M.A.H. Dempster, Cambridge University Press, Cambridge. 2002; 176-223.
Google Scholar
5
-
Box G, Jenkins G. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day; 1970.
Google Scholar
6
-
Nelsen R. An Introduction to Copulas, New York, Springer-Verlag; 2006.
Google Scholar
7
-
Sklar A. Functions de Repartition a n Dimensions et leurs Merges, Publication of the Institute of Statistics, University of Paris. 1959;8: 229-231. French.
Google Scholar
8
-
Demarta S, McNeil A. The t Copula and related Copulas, International Statistical Review. 2005; 73: 111-129.
Google Scholar
9
-
Aktas H, Oncu S. The Stock Market Reaction to Extreme Events: The Evidence from Turkey. International Research Journal of Finance and Economics. 2006; 6: 78-85.
Google Scholar
10
-
Joe H. Families of m-variate Distributions with Given Margins and m(m-1)/2 Bivariate Dependence Parematers. In L. Ruschendorf and B. Schweizer and M.D. Taylor (Ed.), Distributions with Fixed Marginals and Related Topics; 1996.
Google Scholar
11
-
Lee S, Lee E, Vlk J. Forecasting Stock Returns using a Copula-GARCH model, Lambert; 2017.
Google Scholar
12
-
Bakar HO, Sulong Z. The Role of Financial Sector on Economic Growth: Theoretical and Empirical Literature Reviews Analysis, Journal of Global Economics 6. 2018.
Google Scholar
13
-
Sarreal R. History of Online Banking: How Internet Banking Went Mainstream, Go Banking Rates; 2019.
Google Scholar
14
-
Phaneuf A. State of mobile banking in 2022: Top apps, features, statistics and mobile trends, Business Insider; 2022.
Google Scholar
15
-
Kolmar C. 39 MUST-KNOW WORK FROM HOME STATISTICS [2022], Zippia. 2021.
Google Scholar
16
-
Caminiti S. 4 gig economy trends that are radically transforming the US job market, CNBC; 2019.
Google Scholar
17
-
Derby M. Fed’s George: Current Fed Stance ‘Out Of Sync’ With Economic Outlook, Wall Street Journal; 2022.
Google Scholar
18
-
Derby, M. Fed’s Harker Leans Against 50 Basis-Point Rate Hike, Wall Street Journal; 2022.
Google Scholar
19
-
Timiraos N. Fed Officials Project Three Interest Rate Rises in 2022 and Accelerate Stimulus Wind-Down, Wall Street Journal; 2021.
Google Scholar
20
-
Lofton O, Ulate M. How Do Low and Negative Interest Rates Affect Banks?, Federal Reserve Bank of San Francisco Economic Letter; 2021.
Google Scholar
21
-
Ljung GM, Box GE. On a Measure of a Lack of Fit in Time Series Models, Biometrika. 1978; 65: 297-303.
Google Scholar
22
-
Bera A, Higgins M. ARCH models: Properties, Estimation and Testing, Journal of Economic Surveys. 1993; 7: 305-366.
Google Scholar
23
Most read articles by the same author(s)
-
Seung-Hwan Lee,
Eun-Joo Lee,
Modifying Weighted Kaplan-Meier Test for Two-Sample Survival Comparison , European Journal of Mathematics and Statistics: Vol. 3 No. 1 (2022)