Yıl 2010,
Cilt: 2 Sayı: 2, 73 - 96, 01.09.2010
### Öz

### Anahtar Kelimeler

### Kaynakça

We study time-varying realized volatility and related correlation measures as proxies for the true volatility and correlation. We investigate measures of Two- Scale realized Absolute Volatility (TSAV) and correlation (TSACORxy) which are helpful to cope effectively with the problem of market microstructure effects at very high frequency financial time series. The measures are constructed based on subsampling and averaging method so that they possess rather less bias even in presence of market microstructure noise. Absolute transformation of return values has been proved in literature to be more robust than squared transformation when considering large values. With respect to some stylized facts of markets, realized squared correlation does not display dynamic behavior. Motivated by robustness of realized absolute volatility, we study an alternative measure of correlation, built on absolute-transformed volatility. This measure of correlation exhibits experimentally some dynamics and hence some predictability capability on minute-by-minute frequency exchange market data. We show that the distribution of realized correlation series computed based on TSACORxy tends to comply a rightward asymmetric shape implying that upside co-movements are greater than downside ones. Moreover we study the association between realized volatility and correlation. According to the two-scale measure, our findings empirically suggest that when returns in Euro/USD exchange rate are highly volatile, the relation between Euro/USD and Euro/GBP exchange markets is strong, and when Euro/USD calms down, the relationship relaxes.

Realized Volatility and Correlation Long Memory Scaling Law SelfSimilarity Dimension Market Microstructure Effects.

- Andersen, T.G. and T. Bollerslev (1997). Heterogeneous information arrivals and return volatility dynamics: Uncovering the long-run in high frequency returns. Journal of Finance, 52, 975-1005.
- Andersen, T.G. and T. Bollerslev (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39, 885-905.
- Andersen, T.G., T. Bollerslev, F.X. Diebold and P. Labys (1999a). Understanding, Optimizing, http://www.stern.nyu.edu/fin/workpapers/papers99/wpa99061.pdf (accessed February 26, 2011). and Forecasting) Realized Volatility and Correlation.
- Andersen, T.G., T. Bollerslev and S. Lange (1999b). Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon. Journal of Empirical Finance, 6, 457-477.
- Andersen, T.G., T. Bollerslev, F.X. Diebold and H. Ebens (2001a). The distribution of realized stock return volatility. Journal of Financial Economics, 61, 43-76.
- Andersen, T.G., T. Bollerslev, F.X. Diebold and P. Labys (2001b). The distribution of realized exchange rate volatility. Journal of the American Statistical Association, 96(453), 42-55.
- Andreou, E. and E. Ghysels (2002). Rolling-sample volatility estimators: Some new theoretical, simulation, and empirical results. Journal of Business and Economic Statistics, 20(3), 363-376.
- Andersen, T.G., T. Bollerslev, P.F. Christoffersen and F.X. Diebold (2006). Volatility and correlation forecasting. Elsevier B.V. Handbook of Economic Forecasting, 1(15).
- Baillie, R.T. and T. Bollerslev (1992). Prediction in dynamic models with time dependent conditional variances. Journal of Econometrics, 52, 91-113.
- Bandi, F. and J. Russell (2005). Microstructure noise, realized volatility, and optimal sampling. Working paper. Graduate School of Business, University of Chicago.
- Bandi, F. and J. Russell (2006). Separating microstructure noise from volatility. Journal of Financial Economics, 79(3), 655-92.
- Barndorff-Nielsen, O.E. and N. Shephard (2003). Realized power variation and stochastic volatility models. Bernoulli, 9(2), 243-265.
- Barndorff-Nielsen, O.E. and N. Shephard (2004a). Power and bipower variation with stochastic volatility and jumps. Journal of Financial Econometrics, 2, 1-37.
- Barndorff-Nielsen, O.E., P.R. Hansen, A. Lunde and N. Shephard (2004b). Regular and modified kernel-based estimators of integrated variance: The case with independent noise. University of Aarhus, Working Paper No. 196. CAF: Centre for Analytical Finance.
- Bollen, B. and B. Inder (2002). Estimating daily volatility in financial markets utilizing intraday data. Journal of Financial Economics, 9, 551-562.
- Bouchaud, J.-P. (2002). An introduction to statistical finance. Physica A, 313, 238-251.
- Cont, R. (2001). Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance, 1, 223-236.
- Ding, Z., C.W.J. Granger and R.F. Engle (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1, 83-106.
- Drost, F.C. and B.J.M. Werker (1996). Closing the GARCH gap: Continuous time GARCH modelling. Journal of Econometrics, 74, 31-57.
- Efron, B. and G. Gong (1983). A leisurely look at the bootstrap, the jackknife, and cross- validation. The American Statistician, 37(1), 36-48.
- Engle, R.F. and T. Bollerslev (1986). Modeling the Persistence of Conditional Variances. Econometric Reviews, 5, 1-50.
- Forsberg, L. and E. Ghysels (2005). Why do absolute returns predict volatility so well? In Princeton-Chicago Conference on the Econometrics of High Frequency Financial Data, Bendheim Center for Finance, Princeton University, 2005.
- Ghysels, E., P. Santa-Clara and R. Valkanov (2006). Predicting volatility: Getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131, 59-95.
- Ghysels, E. and A. Sinko (2006). Volatility forecasting and microstructure noise. In Colloque CIREQ Conference: Realized Volatility, 22-23 April 2006, Montreal.
- Goncalves, S. and N. Meddahi (2005). Bootstrapping realized volatility. Working Paper. Departement de sciences economiques, CIREQ and CIRANO, Universite de Montreal.
- Granger, C.W.J. and C.-Y. Sin (2000). Modelling the absolute returns of different stock indices: exploring the forecastability of an alternative measure of risk. Journal of Forecasting, 19(4), 277-298.
- Hansen, P. and A. Lunde (2006). Realized Variance and Market Microstructure Noise. Journal of Business and Economic Statistics, 24, 127-161.
- Huang, C.-H. and C.-C. Nieh (2004). Realize the realized stock index volatility. Asian Economic Journal, 18(1), 59-80.
- Hurst, H. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, 770-808.
- Hurst, H. (1955). Methods of using long-term storage in reservoirs. Proceedings of the Institution of Civil Engineers, 1, 519-577.
- Maheu, J.M. and T.H. McCurdy (2002). Nonlinear features of realized FX volatility. Review of Economic Statistics, 84, 668-681.
- Mandelbrot, B. (1986). Self-affine fractal sets. In Fractals in Physics, ed. L. Pietronero and E. Tosatti. Amsterdam: North Holland.
- Merton, R.C. (1980). On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics, 8, 323-361.
- Nelson, D.B. (1990). ARCH models as diffusion approximations. Journal of Econometrics, 45, 7-38.
- Peters, E.E. (1996). Chaos and order in the capital markets: A New View of Cycles, Prices, and Market Volatility. Second Edition, John Wiley and Sons.
- Safari, A. and D. Seese (2008). Distributional and dynamical properties of realized volatility and correlation. Forthcoming in Quantitative Finance.
- Schlottmann, F. and D. Seese (1999). Die Skalierung der Preisschwankungen an einem virtuellen Kapitalmarkt mit probabilistischen und trendverfolgenden Agenten. In Angewandte Informatik und Formale Beschreibungsverfahren ed. Georg Lausen, Andreas Oberweis and Gunter Schlageter Stuttgart: Teubner-Verlag, 212-222.
- Solnik, B., C. Boucrelle and Y. Le Fur (1996). International Market Correlation and Volatility. Financial Analysts Journal, September-October, 17-34.
- Taylor, S.J. (1986). Modelling financial time series. Wiley: New York.
- Zhang, L., P.A. Mykland and Y. Aїt-Sahalia (2005). A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High Frequency Data. Journal of the American Statistical Association, 100(472), 1394-1411.
- Zhou, B. (1996). High-frequency data and volatility in foreign-exchange rates. Journal of Business and Economic Statistics, 14(1), 45-52.

Yıl 2010,
Cilt: 2 Sayı: 2, 73 - 96, 01.09.2010
### Öz

### Kaynakça

- Andersen, T.G. and T. Bollerslev (1997). Heterogeneous information arrivals and return volatility dynamics: Uncovering the long-run in high frequency returns. Journal of Finance, 52, 975-1005.
- Andersen, T.G. and T. Bollerslev (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39, 885-905.
- Andersen, T.G., T. Bollerslev, F.X. Diebold and P. Labys (1999a). Understanding, Optimizing, http://www.stern.nyu.edu/fin/workpapers/papers99/wpa99061.pdf (accessed February 26, 2011). and Forecasting) Realized Volatility and Correlation.
- Andersen, T.G., T. Bollerslev and S. Lange (1999b). Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon. Journal of Empirical Finance, 6, 457-477.
- Andersen, T.G., T. Bollerslev, F.X. Diebold and H. Ebens (2001a). The distribution of realized stock return volatility. Journal of Financial Economics, 61, 43-76.
- Andersen, T.G., T. Bollerslev, F.X. Diebold and P. Labys (2001b). The distribution of realized exchange rate volatility. Journal of the American Statistical Association, 96(453), 42-55.
- Andreou, E. and E. Ghysels (2002). Rolling-sample volatility estimators: Some new theoretical, simulation, and empirical results. Journal of Business and Economic Statistics, 20(3), 363-376.
- Andersen, T.G., T. Bollerslev, P.F. Christoffersen and F.X. Diebold (2006). Volatility and correlation forecasting. Elsevier B.V. Handbook of Economic Forecasting, 1(15).
- Baillie, R.T. and T. Bollerslev (1992). Prediction in dynamic models with time dependent conditional variances. Journal of Econometrics, 52, 91-113.
- Bandi, F. and J. Russell (2005). Microstructure noise, realized volatility, and optimal sampling. Working paper. Graduate School of Business, University of Chicago.
- Bandi, F. and J. Russell (2006). Separating microstructure noise from volatility. Journal of Financial Economics, 79(3), 655-92.
- Barndorff-Nielsen, O.E. and N. Shephard (2003). Realized power variation and stochastic volatility models. Bernoulli, 9(2), 243-265.
- Barndorff-Nielsen, O.E. and N. Shephard (2004a). Power and bipower variation with stochastic volatility and jumps. Journal of Financial Econometrics, 2, 1-37.
- Barndorff-Nielsen, O.E., P.R. Hansen, A. Lunde and N. Shephard (2004b). Regular and modified kernel-based estimators of integrated variance: The case with independent noise. University of Aarhus, Working Paper No. 196. CAF: Centre for Analytical Finance.
- Bollen, B. and B. Inder (2002). Estimating daily volatility in financial markets utilizing intraday data. Journal of Financial Economics, 9, 551-562.
- Bouchaud, J.-P. (2002). An introduction to statistical finance. Physica A, 313, 238-251.
- Cont, R. (2001). Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance, 1, 223-236.
- Ding, Z., C.W.J. Granger and R.F. Engle (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1, 83-106.
- Drost, F.C. and B.J.M. Werker (1996). Closing the GARCH gap: Continuous time GARCH modelling. Journal of Econometrics, 74, 31-57.
- Efron, B. and G. Gong (1983). A leisurely look at the bootstrap, the jackknife, and cross- validation. The American Statistician, 37(1), 36-48.
- Engle, R.F. and T. Bollerslev (1986). Modeling the Persistence of Conditional Variances. Econometric Reviews, 5, 1-50.
- Forsberg, L. and E. Ghysels (2005). Why do absolute returns predict volatility so well? In Princeton-Chicago Conference on the Econometrics of High Frequency Financial Data, Bendheim Center for Finance, Princeton University, 2005.
- Ghysels, E., P. Santa-Clara and R. Valkanov (2006). Predicting volatility: Getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131, 59-95.
- Ghysels, E. and A. Sinko (2006). Volatility forecasting and microstructure noise. In Colloque CIREQ Conference: Realized Volatility, 22-23 April 2006, Montreal.
- Goncalves, S. and N. Meddahi (2005). Bootstrapping realized volatility. Working Paper. Departement de sciences economiques, CIREQ and CIRANO, Universite de Montreal.
- Granger, C.W.J. and C.-Y. Sin (2000). Modelling the absolute returns of different stock indices: exploring the forecastability of an alternative measure of risk. Journal of Forecasting, 19(4), 277-298.
- Hansen, P. and A. Lunde (2006). Realized Variance and Market Microstructure Noise. Journal of Business and Economic Statistics, 24, 127-161.
- Huang, C.-H. and C.-C. Nieh (2004). Realize the realized stock index volatility. Asian Economic Journal, 18(1), 59-80.
- Hurst, H. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, 770-808.
- Hurst, H. (1955). Methods of using long-term storage in reservoirs. Proceedings of the Institution of Civil Engineers, 1, 519-577.
- Maheu, J.M. and T.H. McCurdy (2002). Nonlinear features of realized FX volatility. Review of Economic Statistics, 84, 668-681.
- Mandelbrot, B. (1986). Self-affine fractal sets. In Fractals in Physics, ed. L. Pietronero and E. Tosatti. Amsterdam: North Holland.
- Merton, R.C. (1980). On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics, 8, 323-361.
- Nelson, D.B. (1990). ARCH models as diffusion approximations. Journal of Econometrics, 45, 7-38.
- Peters, E.E. (1996). Chaos and order in the capital markets: A New View of Cycles, Prices, and Market Volatility. Second Edition, John Wiley and Sons.
- Safari, A. and D. Seese (2008). Distributional and dynamical properties of realized volatility and correlation. Forthcoming in Quantitative Finance.
- Schlottmann, F. and D. Seese (1999). Die Skalierung der Preisschwankungen an einem virtuellen Kapitalmarkt mit probabilistischen und trendverfolgenden Agenten. In Angewandte Informatik und Formale Beschreibungsverfahren ed. Georg Lausen, Andreas Oberweis and Gunter Schlageter Stuttgart: Teubner-Verlag, 212-222.
- Solnik, B., C. Boucrelle and Y. Le Fur (1996). International Market Correlation and Volatility. Financial Analysts Journal, September-October, 17-34.
- Taylor, S.J. (1986). Modelling financial time series. Wiley: New York.
- Zhang, L., P.A. Mykland and Y. Aїt-Sahalia (2005). A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High Frequency Data. Journal of the American Statistical Association, 100(472), 1394-1411.
- Zhou, B. (1996). High-frequency data and volatility in foreign-exchange rates. Journal of Business and Economic Statistics, 14(1), 45-52.

Toplam 41 adet kaynakça vardır.

Konular | İşletme |
---|---|

Diğer ID | JA62EC55RS |

Bölüm | Makaleler |

Yazarlar | |

Yayımlanma Tarihi | 1 Eylül 2010 |

Gönderilme Tarihi | 1 Eylül 2010 |

Yayımlandığı Sayı | Yıl 2010 Cilt: 2 Sayı: 2 |