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ASSET ALLOCATION WITH DYNAMIC CONDITIONAL CORRELATIONS (DCC) MODEL: AN IMPLEMENTATION IN THE R PROGRAM

Yıl 2022, Sayı: 51, 149 - 175, 09.08.2022

Metin Ilbasmis

https://doi.org/10.30794/pausbed.1090560

Öz

This study demonstrates how to use the R programming language to estimate time varying volatility in
returns and correlations between several asset classes by employing a model called Dynamic Conditional
Correlations (DCC) and to form portfolios using those estimates. A number of user-written R commands
are presented in the study, designed for practitioners, academics, and students of nance interested in
active portfolio optimization. The study uses these commands to access nancial data, analyze statistical
characteristics of the data, estimate dynamic correlations, and nally compute the optimal weights of several
asset classes in portfolios optimized for a variety of purposes.

Anahtar Kelimeler

R program Estimation DCC Correlations Portfolio optimization

Kaynakça

  • Ardia, D., Bolliger, G., Boudt, K., & Gagnon-Fleury, J.-P. (2017). The impact of covariance misspecification in risk-based portfolios. Annals of Operations Research, 254 , 1–16.
  • Billio, M., Caporin, M., & Gobbo, M. (2006). Flexible dynamic conditional correlation multivariate garch models for asset allocation. Applied Financial Economics Letters, 2 , 123–130.
  • Bouri, E., Moln´ar, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of bitcoin: Is it really more than a diversifier? Finance Research Letters, 20 , 192–198.
  • Briere, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversification with bitcoin. Journal of Asset Management , 16 , 365–373.
  • Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial econometrics, 4 , 537–572.
  • Chen, Y., & Nie, Y. (2018). Value at risk when covariance is misspecified. Proceedings of the International Conference on Industrial Engineering and Operations Management, Washington DC, USA, . URL: http: //ieomsociety.org/dc2018/papers/182.pdf.
  • Conover, C. M., Jensen, G. R., Johnson, R. R., & Mercer, J. M. (2010). Is now the time to add commodities to your portfolio? The Journal of Investing , 19 , 10–19.
  • Eddelbuettel, D. (2022). Cran task view: Empirical finance, .
  • Elton, E. J., & Gruber, M. J. (1997). Modern portfolio theory, 1950 to date. Journal of banking & finance, 21 , 1743–1759.
  • Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20 , 339–350.
  • Ensor, K. B., & Koev, G. M. (2014). Computational finance: correlation, volatility, and markets. Wiley Interdisciplinary Reviews: Computational Statistics, 6 , 326–340.
  • Galanos, A. (2022). rmgarch: Multivariate GARCH models.. R package version 1.3-9.
  • Gao, X., & Nardari, F. (2018). Do commodities add economic value in asset allocation? new evidence from time-varying moments. Journal of Financial and Quantitative Analysis, 53 , 365–393.
  • Ghalanos, A. (2022). rugarch: Univariate GARCH models.. R package version 1.4-7.
  • Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48 , 1779–1801.
  • Gorton, G., & Rouwenhorst, K. G. (2006). Facts and fantasies about commodity futures. Financial Analysts Journal , 62 , 47–68.
  • Hafner, C., & Franses, P. H. (2003). A generalized dynamic conditional correlation model for many asset returns. Technical Report.
  • Hillier, D., Draper, P., & Faff, R. (2006). Do precious metals shine? an investment perspective. Financial Analysts Journal , 62 , 98–106.
  • Hyndman, R. J. (2022). Cran task view: Time series analysis, .
  • Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, 26 , 1–22. doi:10.18637/jss.v027.i03.
  • Klein, T., Thu, H. P., & Walther, T. (2018). Bitcoin is not the new gold–a comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis, 59 , 105–116.
  • Komsta, L., & Novomestky, F. (2015). moments: Moments, cumulants, skewness, kurtosis and related tests. URL: https://CRAN.R-project.org/package=moments r package version 0.14.
  • Liu, Q., Tse, Y., & Zhang, L. (2018). Including commodity futures in asset allocation in china. Quantitative Finance, 18 , 1487–1499.
  • Markowitz, H. (1952). The utility of wealth. Journal of political Economy , 60 , 151–158.
  • McAleer, M., Chan, F., Hoti, S., & Lieberman, O. (2008). Generalized autoregressive conditional correlation. Econometric Theory, 24 , 1554–1583.
  • Mullen, K. M. (2014). Continuous global optimization in r. Journal of Statistical Software, 60 , 145. URL: https://statistik-jstat.uibk.ac.at/index.php/jss/article/view/v060i06. doi:10.18637/ jss.v060.i06.
  • NYU-Libraries, N. Y. U. N. (2022). Quantitative analysis guide: Which statistical software to use? (2022) (Accessed: 13 March 2022). URL: https://guides.nyu.edu/quant/statsoft#s-lib-ctab-6295863-7.
  • Peterson, B. G., & Carl, P. (2020). PerformanceAnalytics: Econometric Tools for Performance and Risk Analysis. URL: https://CRAN.R-project.org/package=PerformanceAnalytics r package version 2.0.4.
  • Platanakis, E., & Urquhart, A. (2020). Should investors include bitcoin in their portfolios? a portfolio theory approach. The British accounting review , 52 , 100837.
  • R Core Team (2021). R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing Vienna, Austria. URL: https://www.R-project.org/.
  • RStudio Team (2016). RStudio: Integrated Development for R. R Foundation for Statistical Computing Boston, MA. URL: https://www.rstudio.com/.
  • Ryan, J. A., & Ulrich, J. M. (2020). quantmod: Quantitative Financial Modelling Framework . URL: https://CRAN.R-project.org/package=quantmod r package version 0.4.18.
  • Sharpe, W. F. (1966). Mutual fund performance. The Journal of business, 39 , 119–138.
  • Trapletti, A., & Hornik, K. (2021). tseries: Time Series Analysis and Computational Finance. URL: https://CRAN.R-project.org/package=tseries r package version 0.10-49. Zeileis, A. (2005). Cran task views. R News, 5 , 39–40.
  • Zivot, E. (2008). Computing efficient portfolios in R. University of Washington - Technical Report .

DİNAMiK KOŞULLU KORELASYONLAR (DCC) MODELİ İLE VARLIK TAHSİSİ: R PROGRAMINDA BİR UYGULAMA

Yıl 2022, Sayı: 51, 149 - 175, 09.08.2022

Metin Ilbasmis

https://doi.org/10.30794/pausbed.1090560

Öz

Bu çalışma, dinamik koşullu korelasyon (DCC) modeli kullanarak varlık sınıfları arasındaki zamanla değişen korelasyonların tahmini ve bu tahminleri kullanarak portföy oluşumu sürecini R programının kullanımınıyla sunmaktadır. Portföy optimizasyonu ile ilgilenen yatırımcılar, akademisyenler ve finans öğrencileri için tasarlanan bu çalışmada, kullanıcı tarafından yazılan bir dizi R komutu sunulmaktadır. Bu komutlar, finansal verilere erişmek, verilerin istatistiksel özelliklerini analiz etmek, dinamik korelasyonları tahmin etmek ve son olarak çeşitli amaçlar için optimize edilmiş portföylerdeki birçok varlık sınıflarının optimal ağırlıklarını hesaplamak için kullanılmıştır.

Anahtar Kelimeler

R programı Tahmin DCC Korelasyonlar Portföy optimizasyonu

Kaynakça

  • Ardia, D., Bolliger, G., Boudt, K., & Gagnon-Fleury, J.-P. (2017). The impact of covariance misspecification in risk-based portfolios. Annals of Operations Research, 254 , 1–16.
  • Billio, M., Caporin, M., & Gobbo, M. (2006). Flexible dynamic conditional correlation multivariate garch models for asset allocation. Applied Financial Economics Letters, 2 , 123–130.
  • Bouri, E., Moln´ar, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of bitcoin: Is it really more than a diversifier? Finance Research Letters, 20 , 192–198.
  • Briere, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversification with bitcoin. Journal of Asset Management , 16 , 365–373.
  • Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial econometrics, 4 , 537–572.
  • Chen, Y., & Nie, Y. (2018). Value at risk when covariance is misspecified. Proceedings of the International Conference on Industrial Engineering and Operations Management, Washington DC, USA, . URL: http: //ieomsociety.org/dc2018/papers/182.pdf.
  • Conover, C. M., Jensen, G. R., Johnson, R. R., & Mercer, J. M. (2010). Is now the time to add commodities to your portfolio? The Journal of Investing , 19 , 10–19.
  • Eddelbuettel, D. (2022). Cran task view: Empirical finance, .
  • Elton, E. J., & Gruber, M. J. (1997). Modern portfolio theory, 1950 to date. Journal of banking & finance, 21 , 1743–1759.
  • Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20 , 339–350.
  • Ensor, K. B., & Koev, G. M. (2014). Computational finance: correlation, volatility, and markets. Wiley Interdisciplinary Reviews: Computational Statistics, 6 , 326–340.
  • Galanos, A. (2022). rmgarch: Multivariate GARCH models.. R package version 1.3-9.
  • Gao, X., & Nardari, F. (2018). Do commodities add economic value in asset allocation? new evidence from time-varying moments. Journal of Financial and Quantitative Analysis, 53 , 365–393.
  • Ghalanos, A. (2022). rugarch: Univariate GARCH models.. R package version 1.4-7.
  • Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48 , 1779–1801.
  • Gorton, G., & Rouwenhorst, K. G. (2006). Facts and fantasies about commodity futures. Financial Analysts Journal , 62 , 47–68.
  • Hafner, C., & Franses, P. H. (2003). A generalized dynamic conditional correlation model for many asset returns. Technical Report.
  • Hillier, D., Draper, P., & Faff, R. (2006). Do precious metals shine? an investment perspective. Financial Analysts Journal , 62 , 98–106.
  • Hyndman, R. J. (2022). Cran task view: Time series analysis, .
  • Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, 26 , 1–22. doi:10.18637/jss.v027.i03.
  • Klein, T., Thu, H. P., & Walther, T. (2018). Bitcoin is not the new gold–a comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis, 59 , 105–116.
  • Komsta, L., & Novomestky, F. (2015). moments: Moments, cumulants, skewness, kurtosis and related tests. URL: https://CRAN.R-project.org/package=moments r package version 0.14.
  • Liu, Q., Tse, Y., & Zhang, L. (2018). Including commodity futures in asset allocation in china. Quantitative Finance, 18 , 1487–1499.
  • Markowitz, H. (1952). The utility of wealth. Journal of political Economy , 60 , 151–158.
  • McAleer, M., Chan, F., Hoti, S., & Lieberman, O. (2008). Generalized autoregressive conditional correlation. Econometric Theory, 24 , 1554–1583.
  • Mullen, K. M. (2014). Continuous global optimization in r. Journal of Statistical Software, 60 , 145. URL: https://statistik-jstat.uibk.ac.at/index.php/jss/article/view/v060i06. doi:10.18637/ jss.v060.i06.
  • NYU-Libraries, N. Y. U. N. (2022). Quantitative analysis guide: Which statistical software to use? (2022) (Accessed: 13 March 2022). URL: https://guides.nyu.edu/quant/statsoft#s-lib-ctab-6295863-7.
  • Peterson, B. G., & Carl, P. (2020). PerformanceAnalytics: Econometric Tools for Performance and Risk Analysis. URL: https://CRAN.R-project.org/package=PerformanceAnalytics r package version 2.0.4.
  • Platanakis, E., & Urquhart, A. (2020). Should investors include bitcoin in their portfolios? a portfolio theory approach. The British accounting review , 52 , 100837.
  • R Core Team (2021). R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing Vienna, Austria. URL: https://www.R-project.org/.
  • RStudio Team (2016). RStudio: Integrated Development for R. R Foundation for Statistical Computing Boston, MA. URL: https://www.rstudio.com/.
  • Ryan, J. A., & Ulrich, J. M. (2020). quantmod: Quantitative Financial Modelling Framework . URL: https://CRAN.R-project.org/package=quantmod r package version 0.4.18.
  • Sharpe, W. F. (1966). Mutual fund performance. The Journal of business, 39 , 119–138.
  • Trapletti, A., & Hornik, K. (2021). tseries: Time Series Analysis and Computational Finance. URL: https://CRAN.R-project.org/package=tseries r package version 0.10-49. Zeileis, A. (2005). Cran task views. R News, 5 , 39–40.
  • Zivot, E. (2008). Computing efficient portfolios in R. University of Washington - Technical Report .
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Finans
Bölüm Makaleler
Yazarlar

Metin Ilbasmis 0000-0001-9657-4604

Erken Görünüm Tarihi 26 Ağustos 2022
Yayımlanma Tarihi 9 Ağustos 2022
Kabul Tarihi 27 Mayıs 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 51

Kaynak Göster

APA Ilbasmis, M. (2022). ASSET ALLOCATION WITH DYNAMIC CONDITIONAL CORRELATIONS (DCC) MODEL: AN IMPLEMENTATION IN THE R PROGRAM. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(51), 149-175. https://doi.org/10.30794/pausbed.1090560
AMA Ilbasmis M. ASSET ALLOCATION WITH DYNAMIC CONDITIONAL CORRELATIONS (DCC) MODEL: AN IMPLEMENTATION IN THE R PROGRAM. PAUSBED. Ağustos 2022;(51):149-175. doi:10.30794/pausbed.1090560
Chicago Ilbasmis, Metin. “ASSET ALLOCATION WITH DYNAMIC CONDITIONAL CORRELATIONS (DCC) MODEL: AN IMPLEMENTATION IN THE R PROGRAM”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 51 (Ağustos 2022): 149-75. https://doi.org/10.30794/pausbed.1090560.
EndNote Ilbasmis M (01 Ağustos 2022) ASSET ALLOCATION WITH DYNAMIC CONDITIONAL CORRELATIONS (DCC) MODEL: AN IMPLEMENTATION IN THE R PROGRAM. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 51 149–175.
IEEE M. Ilbasmis, “ASSET ALLOCATION WITH DYNAMIC CONDITIONAL CORRELATIONS (DCC) MODEL: AN IMPLEMENTATION IN THE R PROGRAM”, PAUSBED, sy. 51, ss. 149–175, Ağustos 2022, doi: 10.30794/pausbed.1090560.
ISNAD Ilbasmis, Metin. “ASSET ALLOCATION WITH DYNAMIC CONDITIONAL CORRELATIONS (DCC) MODEL: AN IMPLEMENTATION IN THE R PROGRAM”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 51 (Ağustos 2022), 149-175. https://doi.org/10.30794/pausbed.1090560.
JAMA Ilbasmis M. ASSET ALLOCATION WITH DYNAMIC CONDITIONAL CORRELATIONS (DCC) MODEL: AN IMPLEMENTATION IN THE R PROGRAM. PAUSBED. 2022;:149–175.
MLA Ilbasmis, Metin. “ASSET ALLOCATION WITH DYNAMIC CONDITIONAL CORRELATIONS (DCC) MODEL: AN IMPLEMENTATION IN THE R PROGRAM”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 51, 2022, ss. 149-75, doi:10.30794/pausbed.1090560.
Vancouver Ilbasmis M. ASSET ALLOCATION WITH DYNAMIC CONDITIONAL CORRELATIONS (DCC) MODEL: AN IMPLEMENTATION IN THE R PROGRAM. PAUSBED. 2022(51):149-75.
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