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The factor structure of financial markets: a simulation study of the Italian case

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  • Michele Costa

Abstract

This article develops a new information criterion for the analysis of the factor structure of financial markets. The new proposal is obtained by resorting to a Monte Carlo experiment, which allows one to evaluate the behaviour of different information criteria by a priori knowing the true number of unobservable factors. The financial markets factor structure is found to be different from those suggested by traditional factor analysis methods, and for the Italian stock market in particular, only two or three factors are signalled.

Suggested Citation

  • Michele Costa, 2003. "The factor structure of financial markets: a simulation study of the Italian case," Applied Economics Letters, Taylor & Francis Journals, vol. 10(2), pages 83-86.
    • Handle: RePEc:taf:apeclt:v:10:y:2003:i:2:p:83-86
    DOI: 10.1080/13504850210150924
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    References listed on IDEAS

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    1. Hansen, Lars Peter & Jagannathan, Ravi, 1997. "Assessing Specification Errors in Stochastic Discount Factor Models," Journal of Finance, American Finance Association, vol. 52(2), pages 557-590, June.
    2. Trzcinka, Charles A, 1986. "On the Number of Factors in the Arbitrage Pricing Model," Journal of Finance, American Finance Association, vol. 41(2), pages 347-368, June.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    5. Connor, Gregory & Korajczyk, Robert A, 1993. "A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-1291, September.
    6. John H. Cochrane, 1999. "Portfolio advice of a multifactor world," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 23(Q III), pages 59-78.
    7. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    8. Engle, Robert F. & Ng, Victor K. & Rothschild, Michael, 1990. "Asset pricing with a factor-arch covariance structure : Empirical estimates for treasury bills," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 213-237.
    9. repec:bla:jfinan:v:44:y:1989:i:5:p:1247-62 is not listed on IDEAS
    10. Conway, Delores A & Reinganum, Marc R, 1988. "Stable Factors in Security Returns: Identification Using Cross-Validation," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(1), pages 1-15, January.
    11. Priestley, Richard, 1996. "The arbitrage pricing theory, macroeconomic and financial factors, and expectations generating processes," Journal of Banking & Finance, Elsevier, vol. 20(5), pages 869-890, June.
    12. Conway, Delores A & Reinganum, Marc R, 1988. "Stable Factors in Security Returns: Identification Using Cross-Validation: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(1), pages 24-28, January.
    13. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
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