Fama-French Model: Multiscale Portfolio Analysis
| Název práce v češtině: | Fama-French Model: Víceškálová Analýza Portfolia |
|---|---|
| Název v anglickém jazyce: | Fama-French Model: Multiscale Portfolio Analysis |
| Klíčová slova: | Fama-French tří-faktorový model, oceňování aktiv, vlnky, víceškálová analýza |
| Klíčová slova anglicky: | Fama-French three-factor model, asset pricing, wavelets, multiscale analysis |
| Akademický rok vypsání: | 2016/2017 |
| Typ práce: | bakalářská práce |
| Jazyk práce: | angličtina |
| Ústav: | Institut ekonomických studií (23-IES) |
| Vedoucí / školitel: | Mgr. Lucie Kraicová |
| Řešitel: | skrytý - zadáno vedoucím/školitelem |
| Datum přihlášení: | 23.05.2017 |
| Datum zadání: | 23.05.2017 |
| Datum a čas obhajoby: | 10.09.2018 09:00 |
| Místo konání obhajoby: | Opletalova - Opletalova 26, O601, Opletalova - místn. č. 601 |
| Datum odevzdání elektronické podoby: | 31.07.2018 |
| Datum proběhlé obhajoby: | 10.09.2018 |
| Oponenti: | prof. PhDr. Petr Teplý, Ph.D. |
| Kontrola URKUND: | |
| Seznam odborné literatury |
| Fama, E. F. and French, K. R. (1992). „The Cross-Section of Expected Stock Returns.“ The Journal of Finance, 47(2), pp. 427-465. DOI: 10.1111/j.1540- 6261.1992.tb04398.x.
Fama, E. F. and French, K. R. (1995). „Size and Book-to-Market Factors in Earnings and Returns.“ The Journal of Finance, 50(1), pp. 131-155. DOI: 10.1111/j.1540-6261.1995.tb05169.x. Gencay, R., Selcuk F. and Whitcher B. (2005). „Multiscale systematic risk". Journal of International Money and Finance, 24(1), pp. 55-70. DOI: 10.1016/j.jimonfin.2004.10.003. In, F. and Kim. S. (2012). An Introduction to Wavelet Theory in Finance. World Scientific. DOI: 10.1142/8431. Kang, B. U., In. F. and Kim, T. S. (2017). „Timescale betas and the cross section of equity returns: Framework, application, and implications for interpreting the Fama-French factors". Journal of Empirical Finance, 42, pp. 15-39. DOI: 10.1016/j.jempfin.2017.01.004. Percival, D. B. and Walden, A.T. (2000). Wavelet Methods for Time Series Analysis. Cambridge University Press. ISBN: 0-521-64068-7. Trimech, A., Kortas, H., Benammou, S. and Benammou, S. (2009). „Multiscale Fama-French model: application to the French market“. The Journal of Risk Finance, 10(2), pp. 179-192. DOI: 10.1108/15265940910938251. |
| Předběžná náplň práce v anglickém jazyce |
| Research question and motivation
Using a combination of the Fama-French model and wavelet-based methods, I will study the empirical relationship between asset returns and Fama-French risk factors at various scales. The Fama-French model extends the capital asset pricing model. It claims that asset returns depend not only on market risk, but also on other factors. Because of its high explanatory power, the model became a useful tool for investment analysis and portfolio optimization. Wavelet-based methods are modern econometric tools that often uncover relationships hidden to traditional time-domain methods. As they offer a scale-by-scale decomposition of time series, they are frequently used for analysis of the relationships between variables at individual scales. Revisions of models initially defined in the time domain then often lead to the discovery of interesting interdependences. The Fama-French model is a case where such a revision looks promising. In and Kim (2012) found that the effect of factors varies across scales. Kang, In and Kim (2017) argue that “the model's well-known empirical success is largely due to the beta components associated with a timescale just short of a business cycle (i.e., wavelet scale 3)”. Trimech et al. (2009) conclude that, in the case of the French market, “the explanatory power of the Fama-French three-factor model becomes stronger as the wavelet scale increases.” As the Fama-French model has direct applications in portfolio optimization, successful research in this area would be of great interest to academics and practitioners alike. Contribution In my work, I will use the already developed methods to analyze a different dataset and compare my results with those in the literature. Despite the availability of literature on this topic, estimating the Fama-French model at various scales is still a challenging task due to both the complexity of the model and the advanced mathematics behind wavelet-based methods. Moreover, the publications on the timescale betas are still quite rare, so apart from the advancement of my knowledge, skills and experience, the thesis should also provide original results which may be of interest for other researchers and/or a good basis for my future research. Methodology I will analyze a ready-to-use dataset provided by Kenneth R. French on his website (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html). First, I will decompose the individual time series using the wavelet transform to obtain wavelet details and smooths. Then, I will analyze the dynamics of the transformed data and use them to estimate the Fama-French model at various scales. Finally, I will discuss the results and compare them with existing literature. Outline 1. Introduction 2. Literature review 3. Methodology • Fama-French model • Wavelet methods 4. Empirical part • Data • Model estimation • Discussion of results 5. Conclusion |