Overnight GARCH-Itô Volatility Models

References

• Aït-Sahalia, Y., Fan, J., and Xiu, D. (2010), “High-Frequency Covariance Estimates with Noisy and Asynchronous Financial Data,” Journal of the American Statistical Association, 105, 1504–1517. DOI: 10.1198/jasa.2010.tm10163.
   Web of Science ®Google Scholar
• Aït-Sahalia, Y., Jacod, J., and Li, J. (2012), “Testing for Jumps in Noisy High Frequency Data,” Journal of Econometrics, 168, 207–222. DOI: 10.1016/j.jeconom.2011.12.004.
   Web of Science ®Google Scholar
• Aït-Sahalia, Y., and Xiu, D. (2016), “Increased Correlation among Asset Classes: Are Volatility or Jumps to Blame, or Both?” Journal of Econometrics, 194, 205–219. DOI: 10.1016/j.jeconom.2016.05.002.
   Web of Science ®Google Scholar
• Andersen, T. G., Bollerslev, T., Diebold, F. X., and Labys, P. (2003), “Modeling and Forecasting Realized Volatility,” Econometrica, 71, 579–625. DOI: 10.1111/1468-0262.00418.
   Web of Science ®Google Scholar
• Andersen, T. G., Bollerslev, T., and Huang, X. (2011), “A Reduced form Framework for Modeling Volatility of Speculative Prices based on Realized Variation Measures,” Journal of Econometrics, 160, 176–189. DOI: 10.1016/j.jeconom.2010.03.029.
   Web of Science ®Google Scholar
• Andersen, T. G., Thyrsgaard, M., and Todorov, V. (2019), “Time-Varying Periodicity in Intraday Volatility,” Journal of the American Statistical Association, 114, 1695–1707. DOI: 10.1080/01621459.2018.1512864.
   Web of Science ®Google Scholar
• Barndorff-Nielsen, O. E., Hansen, P. R., Lunde, A., and Shephard, N. (2008), “Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise,” Econometrica, 76, 1481–1536.
   Web of Science ®Google Scholar
• Barndorff-Nielsen, O. E., and Shephard, N. (2006), “Econometrics of Testing for Jumps in Financial Economics using Bipower Variation,” Journal of Financial Econometrics, 4, 1–30. DOI: 10.1093/jjfinec/nbi022.
   Google Scholar
• Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31, 307–327. DOI: 10.1016/0304-4076(86)90063-1.
   Web of Science ®Google Scholar
• Bollerslev, T., Patton, A. J., and Quaedvlieg, R. (2018), “Modeling and Forecasting (un) Reliable Realized Covariances for More Reliable Financial Decisions,” Journal of Econometrics, 207, 71–91. DOI: 10.1016/j.jeconom.2018.05.004.
   Web of Science ®Google Scholar
• Christoffersen, P. F. (1998), “Evaluating Interval Forecasts,” International Economic Review, 39, 841–862. DOI: 10.2307/2527341.
   Web of Science ®Google Scholar
• Corsi, F. (2009), “A Simple Approximate Long-Memory Model of Realized Volatility,” Journal of Financial Econometrics, 7, 174–196. DOI: 10.1093/jjfinec/nbp001.
   Web of Science ®Google Scholar
• Corsi, F., Pirino, D., and Reno, R. (2010), “Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting,” Journal of Econometrics, 159, 276–288. DOI: 10.1016/j.jeconom.2010.07.008.
   Web of Science ®Google Scholar
• Demetrescu, M., Golosnoy, V., and Titova, A. (2020), “Bias Corrections for Exponentially Transformed Forecasts: Are They Worth the Effort?” International Journal of Forecasting, 36, 761–780. DOI: 10.1016/j.ijforecast.2019.09.001.
   Web of Science ®Google Scholar
• Diebold, F. X., and Mariano, R. S. (1995), “Comparing Predictive Accuracy,” Journal of Business and Economic Statistics, 13, 253–263.
   Web of Science ®Google Scholar
• Duffie, D., and Pan, J. (1997), “An Overview of Value at Risk,” Journal of Derivatives, 4, 7–49. DOI: 10.3905/jod.1997.407971.
   Google Scholar
• Engle, R. F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica: Journal of the Econometric Society, 50, 987–1007. DOI: 10.2307/1912773.
   Web of Science ®Google Scholar
• Engle, R. F., and Manganelli, S. (2004), “Caviar: Conditional Autoregressive Value at Risk by Regression Quantiles,” Journal of Business & Economic Statistics, 22, 367–381.
   Web of Science ®Google Scholar
• Fan, J., and Kim, D. (2018), “Robust High-Dimensional Volatility Matrix Estimation for High-Frequency Factor Model,” Journal of the American Statistical Association, 113, 1268–1283. DOI: 10.1080/01621459.2017.1340888.
   PubMed Web of Science ®Google Scholar
• Fan, J., and Wang, Y. (2007), “Multi-Scale Jump and Volatility Analysis for High-Frequency Financial Data,” Journal of the American Statistical Association, 102, 1349–1362. DOI: 10.1198/016214507000001067.
   Web of Science ®Google Scholar
• Fleming, J., Kirby, C., and Ostdiek, B. (2003), “The Economic Value of Volatility Timing using “realized” Volatility,” Journal of Financial Economics, 67, 473–509. DOI: 10.1016/S0304-405X(02)00259-3.
   Web of Science ®Google Scholar
• Francq, C., and Sucarrat, G. (2021), “Volatility Estimation when the Zero-Process is Nonstationary,” Journal of Business & Economic Statistics, 1–14.
   Web of Science ®Google Scholar
• Glosten, L. R., Jagannathan, R., and Runkle, D. E. (1993), “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,” Journal of Finance, 48, 1779–1801. DOI: 10.1111/j.1540-6261.1993.tb05128.x.
   Web of Science ®Google Scholar
• Hansen, P. R., Huang, Z., and Shek, H. H. (2012), “Realized Garch: A Joint Model for Returns and Realized Measures of Volatility,” Journal of Applied Econometrics, 27, 877–906. DOI: 10.1002/jae.1234.
   Web of Science ®Google Scholar
• Hansen, P. R., and Lunde, A. (2005), “A Realized Variance for the Whole Day based on Intermittent High-Frequency Data,” Journal of Financial Econometrics, 3, 525–554. DOI: 10.1093/jjfinec/nbi028.
   Google Scholar
• Jacod, J., Li, Y., Mykland, P. A., Podolskij, M., and Vetter, M. (2009), “Microstructure Noise in the Continuous Case: The Pre-averaging Approach,” Stochastic Processes and their Applications, 119, 2249–2276. DOI: 10.1016/j.spa.2008.11.004.
   Web of Science ®Google Scholar
• Kim, D., and Fan, J. (2019), “Factor garch-itô Models for High-Frequency Data with Application to Large Volatility Matrix Prediction,” Journal of Econometrics, 208, 395–417. DOI: 10.1016/j.jeconom.2018.10.003.
   Web of Science ®Google Scholar
• Kim, D., Liu, Y., and Wang, Y. (2018), “Large Volatility Matrix Estimation with Factor-based Diffusion Model for High-Frequency Financial Data,” Bernoulli, 24, 3657–3682. DOI: 10.3150/17-BEJ974.
   Web of Science ®Google Scholar
• Kim, D., and Wang, Y. (2016), “Unified Discrete-Time and Continuous-Time Models and Statistical Inferences for Merged Low-Frequency and High-Frequency Financial Data,” Journal of Econometrics, 194, 220–230. DOI: 10.1016/j.jeconom.2016.05.003.
   Web of Science ®Google Scholar
• Kim, D., Wang, Y., and Zou, J. (2016), “Asymptotic Theory for Large Volatility Matrix Estimation based on High-Frequency Financial Data,” Stochastic Processes and their Applications, 126, 3527–3577. DOI: 10.1016/j.spa.2016.05.004.
   Web of Science ®Google Scholar
• Kupiec, P. H. (1995), “Techniques for Verifying the Accuracy of Risk Measurement Models,” The Journal of Derivatives, 3, 73–84. DOI: 10.3905/jod.1995.407942.
   Google Scholar
• Mancini, C. (2004), “Estimation of the Characteristics of the Jumps of a General Poisson-Diffusion Model,” Scandinavian Actuarial Journal, 2004, 42–52. DOI: 10.1080/034612303100170091.
   Google Scholar
• Markowitz, H. (1952), “Portfolio Selection,” The Journal of Finance, 7, 77–91.
   Web of Science ®Google Scholar
• Martens, M. (2002), “Measuring and Forecasting s&p 500 Index-Futures Volatility using High-Frequency Data,” Journal of Futures Markets: Futures, Options, and Other Derivative Products, 22, 497–518.
   Web of Science ®Google Scholar
• Patton, A. J. (2011), “Volatility Forecast Comparison using Imperfect Volatility Proxies,” Journal of Econometrics, 160, 246–256. DOI: 10.1016/j.jeconom.2010.03.034.
   Web of Science ®Google Scholar
• Rockafellar, R. T., and Uryasev, S. (2000), “Optimization of Conditional Value-at-Risk,” Journal of Risk, 2, 21–42. DOI: 10.21314/JOR.2000.038.
   Google Scholar
• Sharpe, W. F. (1964), “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk,” The Journal of Finance, 19, 425–442.
   Web of Science ®Google Scholar
• Shephard, N., and Sheppard, K. (2010), “Realising the Future: Forecasting with High-Frequency-based Volatility (Heavy) Models,” Journal of Applied Econometrics, 25, 197–231. DOI: 10.1002/jae.1158.
   Web of Science ®Google Scholar
• Shin, M., Kim, D., and Fan, J. (2021), “Adaptive Robust Large Volatility Matrix Estimation Based on High-Frequency Financial Data,” arXiv preprint arXiv:2102.12752.
   Google Scholar
• Song, X., Kim, D., Yuan, H., Cui, X., Lu, Z., Zhou, Y., and Yazhen, W. (2020), “Volatility Analysis with Realized Garch-ito Models,” Journal of Econometrics, 222, 393–410. DOI: 10.1016/j.jeconom.2020.07.007..
   Web of Science ®Google Scholar
• Tao, M., Wang, Y., and Chen, X. (2013), “Fast Convergence Rates in Estimating Large Volatility Matrices using High-Frequency Financial Data,” Econometric Theory, 29, 838–856. DOI: 10.1017/S0266466612000746.
   Web of Science ®Google Scholar
• Taylor, N. (2007), “A Note on the Importance of Overnight Information in Risk Management Models,” Journal of Banking & Finance, 31, 161–180.
   Web of Science ®Google Scholar
• Todorova, N., and Souček, M. (2014), “Overnight Information Flow and Realized Volatility Forecasting,” Finance Research Letters, 11, 420–428. DOI: 10.1016/j.frl.2014.07.001.
   Web of Science ®Google Scholar
• Tseng, T.-C., Lai, H.-C., and Lin, C.-F. (2012), “The Impact of Overnight Returns on Realized Volatility,” Applied Financial Economics, 22, 357–364.
   Google Scholar
• Xiu, D. (2010), “Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data,” Journal of Econometrics, 159, 235–250. DOI: 10.1016/j.jeconom.2010.07.002.
   Web of Science ®Google Scholar
• Zhang, L. (2006), “Efficient Estimation of Stochastic Volatility using Noisy Observations: A Multi-Scale Approach,” Bernoulli, 12, 1019–1043. DOI: 10.3150/bj/1165269149.
   Web of Science ®Google Scholar
• Zhang, L., Mykland, P. A., and Aït-Sahalia, Y. (2005), “A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High-Frequency Data,” Journal of the American Statistical Association, 100, 1394–1411. DOI: 10.1198/016214505000000169.
   Web of Science ®Google Scholar
• Zhang, X., Kim, D., and Wang, Y. (2016), “Jump Variation Estimation with Noisy High Frequency Financial Data via Wavelets,” Econometrics, 4, 1–26.
   Google Scholar