Central limit theorems when data are dependent: addressing the pedagogical gaps
AbstractAlthough dependence in financial data is pervasive, standard doctoral-level econometrics texts do not make clear that the common central limit theorems (CLTs) contained therein fail when applied to dependent data. More advanced books that are clear in their CLT assumptions do not contain any worked examples of CLTs that apply to dependent data. We address these pedagogical gaps by discussing dependence in financial data and dependence assumptions in CLTs and by giving a worked example of the application of a CLT for dependent data to the case of the derivation of the asymptotic distribution of the sample variance of a Gaussian AR(1). We also provide code and the results for a Monte-Carlo simulation used to check the results of the derivation.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Institute for Empirical Research in Economics - University of Zurich in its series IEW - Working Papers with number 480.
Date of creation: Feb 2010
Date of revision:
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Bollerslev, Tim & Law, Tzuo Hann & Tauchen, George, 2008.
"Risk, jumps, and diversification,"
Journal of Econometrics,
Elsevier, vol. 144(1), pages 234-256, May.
- Michael R. Powers & Martin Shubik & Shuntian Yao, 1994.
"Insurance Market Games: Scale Effects and Public Policy,"
Cowles Foundation Discussion Papers
1076, Cowles Foundation for Research in Economics, Yale University.
- Michael Powers & Martin Shubik & Shun Yao, 1998. "Insurance market games: Scale effects and public policy," Journal of Economics, Springer, vol. 67(2), pages 109-134, June.
- Nikolas Topaloglou & Olivier Scaillet & University of Geneva, 2006.
"Testing foe Stochastic Dominance Efficiency,"
Computing in Economics and Finance 2006
74, Society for Computational Economics.
- Roll, Richard, 1984. " A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market," Journal of Finance, American Finance Association, vol. 39(4), pages 1127-39, September.
- Peter Carr & Liuren Wu, 2002.
"The Finite Moment Log Stable Process and Option Pricing,"
- Peter Carr & Liuren Wu, 2003. "The Finite Moment Log Stable Process and Option Pricing," Journal of Finance, American Finance Association, vol. 58(2), pages 753-778, 04.
- Lucas, Robert E, Jr, 1978. "Asset Prices in an Exchange Economy," Econometrica, Econometric Society, vol. 46(6), pages 1429-45, November.
- Jorge Carrera & Romain Restout, 2008.
"Long Run Determinants of Real Exchange Rates in Latin America,"
0811, Groupe d'Analyse et de Théorie Economique (GATE), Centre national de la recherche scientifique (CNRS), Université Lyon 2, Ecole Normale Supérieure.
- Jorge Carrera & Romain Restout, 2008. "Long Run Determinants of Real Exchange Rates in Latin America," Post-Print halshs-00276402, HAL.
- Jegadeesh, Narasimhan & Titman, Sheridan, 1993. " Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marita Kieser).
If references are entirely missing, you can add them using this form.