in business time may not hold in this situation. Also, it would be interesting to include other sources of information in the model, such as public news data and high-frequency option data, and assess whether they provide extra information to model high-frequency volatility in addition to the MMS variables used in this chapter. In Chapter 3, a potential limitation of the empirical findings is that the link from the regime classifications to the information content of the regimes requires further verification, due to the latent nature of information arrivals into the market. One could further augment the model by using physical news arrival data to examine the validity of this link.
4.4
Final Remarks
To sum up, this thesis presents important developments to the literature of point process based volatility estimators both in theory and applications. The findings show that the point process based estimator uses data more efficiently than the widely-applied RV approach, and has the advantage to use information other than the price process in volatility modelling and estimation. This thesis establishes the theoretical foundation of the point process based volatility estimator and advocates the use of point process based approach in future volatility modelling and MMS research.
Bibliography
Admati, A. R., and P. Pfleiderer (1988): “A Theory of Intraday Patterns:
Volume and Price Variability,”The Review of Financial Studies, 1(1), 3–40.
Ahn, H.-J., K.-H. Bae,andK. Chan(2001): “Limit Orders, Depth, and Volatility:
Evidence from the Stock Exchange of Hong Kong,”The Journal of Finance, 56(2), 767–788.
A¨ıt-Sahalia, Y., P. A. Mykland, and L. Zhang (2011): “Ultra high frequency
volatility estimation with dependent microstructure noise,”Journal of Econometrics, 160(1), 160–175.
Allassonni`ere, S., E. Kuhn, and A. Trouv´e(2010): “Construction of Bayesian
Deformable Models via Stochastic Approximation Algorithm: A Convergence Study,”Bernouilli, 16(3), 641–678.
Allen, D., F. Chan, M. McAleer, and S. Peiris (2008): “Finite sample
properties of the QMLE for the Log-ACD model: Application to Australian stocks,”
Journal of Econometrics, 147(1), 163–185.
Amihud, Y. (2002): “Illiquidity and stock returns: Cross-section and time-series
effects,”Journal of Financial Markets, 5(1), 31–56.
Andersen, T. G. (1996): “Return Volatility and Trading Volume: An Information
Flow Interpretation of Stochastic Volatility,”The Journal of Finance, 51, 169.
Andersen, T. G., and T. Bollerslev (1997a): “Heterogeneous Information
Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns,”The Journal of Finance, 52(3), 975–1005.
Andersen, T. G., and T. Bollerslev (1997b): “Intraday periodicity and
volatility persistence in financial markets,”Journal of Empirical Finance, 4(2-3), 115–158.
Andersen, T. G., T. Bollerslev, F. X. Diebold, and H. Ebens(2001): “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 (2000):
“Great Realizations,”Risk, 13(3), 105–108.
Andersen, T. G., T. Bollerslev, F. X. Diebold, and P. Labys (2001):
“The Distribution of Realized Exchange Rate Volatility,”Journal of the American Statistical Association, 96(453), 42–55.
Andersen, T. G., T. Bollerslev, and D. Dobrev (2007): “No-arbitrage semi-
martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and i.i.d. noise: Theory and testable distributional implications,”
Journal of Econometrics, 138(1), 125–180.
Andersen, T. G., D. Dobrev, and E. Schaumburg (2008): “Duration-Based
Volatility Estimation,”Working Paper, Northwestern University.
Andersen, T. G., D. Dobrev, and E. Schaumburg (2012): “Jump-robust
volatility estimation using nearest neighbor truncation,”Journal of Econometrics, 169(1), 75–93.
Arag´o, V., and L. Nieto (2005): “Heteroskedasticity in the returns of the
main world stock exchange indices: Volume versus GARCH effects,”Journal of International Financial Markets, Institutions and Money, 15(3), 271–284.
Bandi, F. M., and J. R. Russell (2008): “Microstructure noise, realized variance,
and optimal sampling,”Review of Economic Studies, 75(2), 339–369.
Barndorff-Nielsen, O. E., P. R. Hansen, A. Lunde, and N. Shephard
(2008a): “Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise,”Econometrica, 76(6), 1481–1536.
(2008b): “Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise,”Econometrica, 76(6), 1481–1536.
Barndorff-Nielsen, O. E., P. R. Hansen, A. Lunde, and N. Shephard
(2009): “Realized kernels in practice: Trades and quotes,”Econometrics Journal, 12(3).
Barndorff-Nielsen, O. E., P. R. Hansen, A. Lunde, and N. Shephard
(2011): “Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading,”Journal of Econometrics, 162, 149–169.
Barndorff-Nielsen, O. E., and N. Shephard(2002): “Econometric analysis of
realized volatility and its use in estimating stochastic volatility models,”Journal of the Royal Statistical Society. Series B: Statistical Methodology, 64(2), 253–280.
(2003): “Realized power variation and stochastic volatility models,” . (2004): “Power and Bipower Variation with Stochastic Volatility and Jumps,”
Journal of Financial Econometrics, 2, 1–48.
Barndorff-Nielsen, O. E., and A. Shiryaev (2010): Change of Time and Change of Measure. World Scientific Publishing Company, Singapore.
Bauwens, L., A. Dufays, and J. V. K. Rombouts (2014): “Marginal likelihood
for Markov-switching and change-point GARCH models,”Journal of Econometrics, 178, 508–522.
Bauwens, L., andN. Hautsch(2006): “Modelling Financial High Frequency Data
Bibliography | 137
Bauwens, L., A. Preminger, and J. V. K. Rombouts (2010): “Theory and
inference for a Markov switching GARCH model,”Econometrics Journal, 13(2), 218–244.
Beale, E. M. L., M. G. Kendall, and D. W. Mann(1967): “The discarding
of variables in multivariate analysis,”Biometrika, 54(3-4), 357–366.
Bertsimas, D., A. King, and R. Mazumder (2016): “Best subset selection via a
modern optimization lens,”Annals of Statistics, 44(2), 813–852.
Bessembinder, H., and P. Seguin (1993): “Price Volatility, Trading Volume, and
Market Depth: Evidence from Futures Markets,”The Journal of Financial and Quantitative Analysis, 28(1), 21–39.
Billingsley, P. (2009): Convergence of Probability Measures. Wiley, New York. Billio, M., R. Casarin, and A. Osuntuyi (2014): “Efficient Gibbs sampling for
Markov switching GARCH models,”Computational Statistics & Data Analysis, Accepted manuscript.
Bollerslev, T.(1986): “Generalised Autoregressive Conditional Heteroscedasticity,” Journal of Econometrics, 31, 307–327.
Bollerslev, T., J. Litvinova, andG. Tauchen (2006): “Leverage and volatility
feedback effects in high-frequency data,”Journal of Financial Econometrics, 4(3), 353–384.
Bollerslev, T., and M. Melvin (1994): “Bid-Ask Spreads and Volatility in
the Foreign Exchagne Market: An Empirical Analysis,”Journal of International Economics, 36, 355–372.
Bollerslev, T., and J. M. Wooldridge (1992): “Quasi-maximum likelihood
estimation and inference in dynamic models with time-varying covariances,”Econo- metric Reviews, 11(2), 143–172.
Booth, J. G., and J. P. Hobert (1999): “Maximizing generalized linear mixed
model likelihoods with an automated Monte Carlo EM algorithm,”Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(1), 265–285.
Bougerol, P., and N. Picard (1992): “Strict stationarity of generalized autore-
gressive processes,”The Annals of Probability, 20(4), 1714–1730.
Bowsher, C. G. (2007): “Modelling security market events in continuous time:
Intensity based, multivariate point process models,” Journal of Econometrics, 141(2002), 876–912.
Boyles, R. A. (1983): “On the Convergence of the EM Algorithm,”Journal of the Royal Statistical Society. Series B (Methodological), 45(1), 47–50.
Brennan, M. J., and A. Subrahmanyam (1996): “Market microstructure and
asset pricing: On the compensation for illiquidity in stock returns,”Journal of Financial Economics, 41(3), 441–464.
Brown, T. C., and M. G. Nair (1988): “A Simple Proof of the Multivariate
Random Time Change Theorem for Point Processes,”Journal of Applied Probability, 25(1), 210–214.
Cai, J. (1994): “A Markov Model of Switching-Regime ARCH,”Journal of Business & Economic Statistics, 12(3), 309–316.
Celeux, G., D. Chauveau, and J. Diebolt (1996): “Stochastic versions of the
em algorithm: an experimental study in the mixture case,”Journal of Statistical Computation and Simulation, 55(January 2015), 287–314.
Celeux, G., and J. Diebold (1985): “The SEM algorithm: a probabilistic teacher
algorithm derived from the EM algorithm for the mixture problems,”Computational Statistics Quarterly, 2, 73–82.
Celeux, G., and J. Diebolt (1992): “A stochastic approximation type EM
algorithm for the mixture problem,”Stochastics and Stochastic Reports, 41(1-2), 119–134.
Chan, K., and W.-M. Fong (2000): “Trade size, order imbalance, and the
volatility-volume relation,”Journal of Financial Economics, 57(2), 247–273.
Chen, F., F. X. Diebold, and F. Schorfheide (2013): “A Markov-switching
multifractal inter-trade duration model, with application to US equities,”Journal of Econometrics, 177(2), 320–342.
Chordia, T., R. Roll, and A. Subrahmanyam (2002): “Order imbalance,
liquidity, and market returns,”Journal of Financial Economics, 65(1), 111–130. (2005): “Evidence on the speed of convergence to market efficiency,”Journal of Financial Economics, 76(2), 271–292.
Christensen, K., and M. Podolskij(2007): “Realized range-based estimation of
integrated variance,”Journal of Econometrics, 141(2), 323–349.
Clark, P. K. (1973): “A Subordinated Stochastic Process Model with Finite
Variance for Speculative Prices,”Econometrica, 41, 135–155.
Coleman, R. (1982): “The Moments of Recurrence Time,”European Journal of Operational Research, 9(2), 181–183.
Copeland, T., and D. Galai (1983): “Information effects on the bid-ask spread,” The Journal of Finance, 38, 1457–1469.
Copeland, T. E. (1976): “A Model of Asset Trading under the Assumption of
Sequential Information Arrival,”The Journal of Finance, 31(4), 1149–1168.
Corsi, F. (2009): “A simple approximate long-memory model of realized volatility,” Journal of Financial Econometrics, 7, 174–196.
Daley, D. J., and D. Vere-Jones (2003): An introduction to the theory of point processes, vol. I. Springer Science & Business Media.
Darrat, A. F., M. Zhong, and L. T. W. Cheng (2007): “Intraday volume
and volatility relations with and without public news,”Journal of Banking and Finance, 31, 2711–2729.
Delattre, S., and J. Jacod (1997): “A central limit theorem for normalized
functions of increments of a diffusion process, in the presence of round-off errors,”
Bibliography | 139
Delyon, B., M. Lavielle, andE. Moulines(1999): “Convergence of a Stochastic
Approximation Version of the EM Algorithm,”The Annals of Statistics, 27(1), 94–128.
Dempster, A. P., N. M. Laird, and D. B. Rubin (1977): “Maximum likelihood
from incomplete data via the EM algorithm,”Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1–38.
Diamond, D. W., and R. E. Verrecchia (1987): “Constraints on short-selling
and asset price adjustment to private information,”Journal of Financial Economics, 18(2), 277–311.
Diebold, F. X., and R. S. Mariano (1995): “Comparing Predictive Accuracy,” Journal of Business & Economic Statistics, 20(1), 134–144.
Diebolt, J., and E. Ip (1996): “A stochastic EM algorithm for approximating the
maximum likelihood estimate,” in Markov Chain Monte Carlo in Practice, ed. by W. R. Gilks, S. Richardson, and D. J. Spiegelhalter. Chapman and Hall, London.
Doob, J. L. (1948): “Renewal Theory From the Point of View of the Theory of
Probability,”Transactions of the Americal Mathematical Society, 63(3), 422–438.
Dueker, M.(1997): “Markov Switching in GARCH Processes and Mean- Reverting
Stock-Market Volatility,”Journal of Business & Economics Statistics, 15(1), 26–34.
Dufour, A., and R. F. Engle (2000): “Time and the Price Impact of a Trade,” The Journal of Finance, 55(6), 2467–2498.
Easley, D., N. Kiefer, M. O’Hara, and J. Paperman (1996): “Liquidity,
information, and infrequently traded stocks,”Journal of Finance, 51, 1405–1436.
Easley, D., M. M. L´opez de Prado, and M. O’Hara (2012): “Flow Toxicity
and Liquidity in a High Frequency World,”Review of Financial Studies, 25, 1457– 1493.
Easley, D., and M. O’Hara (1992): “Time and the Process of Security Price
Adjustment,”The Journal of Finance, 47(2), 577–605.
Engle, R. F.(1982): “Autoregressive Conditional Heteroscedasticity with Estimates
of the Variance of United Kingdom Inflation,”Econometrica, 50, 987–1007.
Engle, R. F., andJ. R. Russell(1998): “Autoregressive Conditional Duration: A
New Model for Irregularly Spaced Transaction Data,”Econometrica, 66, 1127–1162.
Epps, T. W.,andL. M. Epps(1976): “The Stochastic Dependence of Security Price
Changes and Transaction Volumes: Implication For The Mixture-of-Distributions Hypothesis,”Econometrica, 44, 305–321.
Feller, W. (1941): “On the Integral Equation of Renewal Theory,”Annals of Mathematical Statistics, 12, 243–267.
Foucault, T.(1999): “Order flow composition and trading costs in a dynamic limit
order market,”Journal of Financial Markets, 2(2), 99–134.
Francq, C., and J.-M. Zako¨ıan (2001): “Stationarity of multivariate Markov-
Fukasawa, M. (2010a): “Central limit theorem for the realized volatility based on
tick time sampling,”Finance and Stochastics, 14(2), 209–233.
(2010b): “Realized volatility with stochastic sampling,”Stochastic Processes and their Applications, 120(6), 829–852.
Fukasawa, M., and M. Rosenbaum (2012): “Central limit theorems for realized
volatility under hitting times of an irregular grid,”Stochastic Processes and their Applications, 122(12), 3901–3920.
Gerhard, F., and N. Hautsch(2002): “Volatility estimation on the basis of price
intensities,”Journal of Empirical Finance, 9, 57–89.
Ghysels, E., and J. Jasiak (1998): “GARCH for Irregularly Spaced Financial
Data: The ACD-GARCH Model,”Studies in Nonlinear Dynamics & Econometrics, 2(4), 133.
Glosten, L. R., R. Jagannathan, and D. E. Runkle(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.
Glosten, L. R., and P. R. Milgrom(1985): “Bid, ask and transaction prices in
a specialist market with heterogeneously informed traders,”Journal of Financial Economics, 14, 71–100.
Gordin, M., and M. Peligrad (2011): “On the functional central limit theorem
via martingale approximation,”Bernoulli, 17(1), 424–440.
Gray, S. F. (1996): “Modeling the conditional distribution of interest rates as a
regime-switching process,”Journal of Financial Economics, 42(1), 27–62.
Griffin, J. E., and R. C. A. Oomen (2008): “Sampling Returns for Realized
Variance Calculations: Tick Time or Transaction Time?,”Econometric Reviews, 27(1-3), 230–253.
Gut, A. (2012): “Anscombe’s Theorem 60 Years Later,”Sequential Analysis, 31(3),
368–396.
Haas, M., S. Mittnik, and M. S. Paolella (2004): “A New Approach to
Markov-Switching GARCH Models,” Journal of Financial Econometrics, 2(4), 493–530.
Hamilton, J. D., andR. Susmel(1994): “Autoregressive conditional heteroskedas-
ticity and changes in regime,”Journal of Econometrics, 64(1-2), 307–333.
Handa, P., and R. A. Schwartz (1996): “Limit Order Trading,”The Journal of Finance, 51(5), 1835–1861.
Hansen, P. R., and A. Lunde (2006): “Realized Variance and Market Microstruc-
ture Noise,”Journal of Business & Economic Statistics, 24(2), 127–161.
Harvey, D., S. Leybourne, and P. Newbold (1997): “Testing the equality
of prediction mean squared errors,”International Journal of Forecasting, 13(2), 281–291.
Bibliography | 141
Hasbrouck, J.(1991): “Measuring the Information Content of Stock Trades,”The Journal of Finance, XLVI(1), 179–207.
Hastie, T., R. Tibshirani, and J. Friedman(2009): The Elements of Statistical Learming: Data Mining, Inference, and Predictions. Springer, New York, 2nd editio edn.
Hastie, T., R. Tibshirani,andR. J. Tibshirani(2017): “Extended Comparisons
of Best Subset Selection, Forward Stepwise Selection, and the Lasso,”Stanford University Working Paper.
H¨ausler, E., and H. Luschgy (2015): Stable Convergence and Stable Limit Theorems. Springer International Publishing, Switzerland.
Hautsch, N. (2012): Econometrics of Financial High-Frequency Data. Springer
Berlin Heidelberg, Berlin, Heidelberg.
Hautsch, N., and M. Podolskij (2013): “Preaveraging-Based Estimation of
Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence,” Journal of Business and Economic Statistics, 31(2), 165–183.
Hocking, R. R., and R. N. Leslie (1967): “Selection of the Best Subset in
Regression Analysis,”Technometrics, 9(4), 531–540.
Holden, C. W., and S. Jacobsen(2014): “Liquidity measurement problems in
fast, competitive markets: Expensive and cheap solutions,”Journal of Finance, 69(4), 1747–1785.
Huang, X., and G. Tauchen (2005): “The relative contribution of jumps to total
price variance,”Journal of Financial Econometrics, 3(4), 456–499.
Hujer, R., S. Vuletic, and S. Kokot (2002): “The Markov Switching ACD
Model,”Working Paper Series: Finance and Accounting, Johann Wolfgang Goethe- Universitat Frankfurt a.A., No.90.
Hussain, S. M. (2011): “The Intraday Behaviour of Bid-Ask Spreads , Trading
Volume and Return Volatility : Evidence from DAX30,”Journal of Economics and Finance, 3, 23–34.
Jacod, J., Y. Li, P. A. Mykland, M. Podolskij, andM. Vetter(2009): “Mi-
crostructure noise in the continuous case: The pre-averaging approach,”Stochastic Processes and their Applications, 119(7), 2249–2276.
Jacod, J., Y. Li, and X. Zheng (2017): “Statical Property of Market Microstruc-
ture Noise,”Econometrica, 8(4), 1133–1174.
Jank, W. (2005): “Quasi-Monte Carlo sampling to improve the efficiency of Monte
Carlo EM,”Computational Statistics & Data Analysis, 48(4), 685–701.
(2006): “Implementing and Diagnosing the Stochastic Approximation EM Algorithm,”Journal of Computational and Graphical Statistics, 15(4), 803–829.
Jondeau, E., J. Lahaye, and M. Rockinger (2015): “Estimating the price
impact of trades in a high-frequency microstructure model with jumps,”Journal of Banking and Finance, 61, S205–S224.
Kalev, P. S., W. M. Liu, P. K. Pham, and E. Jarnecic (2004): “Public
information arrival and volatility of intraday stock returns,”Journal of Banking and Finance, 28(6), 1441–1467.
Karr, A. (1991): Point Processes and Their Statistical Inferences. Dekker, New
York.
Klaassen, F.(2002): “Improving GARCH volatility forecasts with regime-switching
GARCH,”Empirical Economics, 27(2), 363–394.
Kuhn, E., and M. Lavielle (2004): “Coupling a stochastic approximation version
of EM with an MCMC procedure,”ESAIM: Probability and Statistics, 8(August), 115–131.
Kyle, A., and A. Obizhaeva (2012): “Market Microstructure Invariants: Theory
and Implications of Calibration,”Portfolio The Magazine Of The Fine Arts.
Kyle, A. S. (1985): “Continuous Auctions and Insider Trading,”Econometrica,
53(6), 1315–1335.
Lamoureux, C. G., and W. D. Lastrapes (1990): “Heteroskedasticity in Stock
Return Data: Volume versus GARCH Effects,”The Journal of Finance, 45(1), 221–229.
Lee, C. M. C., and M. J. Ready(1991): “Inferring Trade Direction from Intraday
Data,”The Journal of Finance, 46, 733–746.
Lee, S. S., and J. Hannig (2010): “Detecting jumps from L´evy jump diffusion
processes,”Journal of Financial Economics, 96(2), 271–290.
Li, Y., and P. A. Mykland (2015): “Rounding errors and volatility estimation,” Journal of Financial Econometrics, 13(2), 478–504.
Li, Y., I. Nolte, and S. Nolte (2018a): “Asymptotic Theory for Renewal Based
High-Frequency Volatility Estimation,” in Point Process Based High-Frequency Volatility Estimation: Theory and Applications (Doctoral Thesis), chap. 1. Lancaster
University, Lancaster, UK.
(2018b): “High-Frequency Volatility Estimation and the Relative Importance of Market Microstructure Effects,” in Point Process Based High-Frequency Volatil- ity Estimation: Theory and Applications (Doctoral Thesis), chap. 2. Lancaster University, Lancaster, UK.
(2018c): “High-Frequency Volatility Modelling : A Markov-Switching Au- toregressive Conditional Intensity Model,” in Point Process Based High-Frequency Volatility Estimation: Theory and Applications (Doctoral Thesis), chap. 3. Lan-
caster University, Lancaster, UK.
Liesenfeld, R., I. Nolte, and W. Pohlmeier (2006): “Modelling financial
transaction price movements: A dynamic integer count data model,”Empirical Economics, 30(4), 795–825.
Liu, L. Y., A. J. Patton, and K. Sheppard (2015): “Does anything beat
5-minute RV? A comparison of realized measures across multiple asset classes,”
Bibliography | 143
Ljung, G. M., and G. E. P. Box (1978): “On a measure of lack of fit in time
series models,”Biometrika, 65, 297–303.
Lomb, N. R.(1976): “Least-Squares Frequency-Analysis of Unequally Spaced Data,” Astrophysics and Space Science, 39(2), 447–462.
Lotov, I. V.(1996): “On Some Boundary Crossing Problems for Gaussian Random
Walks,”The Annals of Probability, 24(4), 2154–2171.
Louis, T. A. (1982): “Finding the Observed Information Matrix when Using the
EM Algorithm,”Journal of the Royal Statistical Society. Series B (Methodological), 44(2), 226–233.
Madhavan, A., M. Richardson, and M. Roomans(1997): “Why Do Security
Prices Change? A Transaction-Level Analysis of NYSE Stocks,”The Review of Financial Studies, 10(4), 1035–1064.
Manganelli, S.(2005): “Duration, volume and volatility impact of trades,”Journal of Financial Markets, 8(4), 377–399.
Næs, R., and J. A. Skjeltorp (2006): “Order book characteristics and the
volume-volatility relation: Empirical evidence from a limit order market,”Journal of Financial Markets, 9(4), 408–432.
Nielsen, F.(2000): “The Stochastic EM Algorithm: Estimation and Asymptotic
Results,”Bernoulli, 6(3), 457–489.
Nolte, I.(2008): “Modeling a multivariate transaction process,”Journal of Financial Econometrics, 6, 143–170.
Nolte, I., S. Taylor, and X. Zhao (2018): “More Accurate Volatility Estima-
tion and Forecasts Using Price Durations,”Working Paper, Lancaster University Management School.
Oomen, R. C. A. (2005): “Properties of bias-corrected realized variance under
alternative sampling schemes,”Journal of Financial Econometrics, 3(4), 555–577.
Oomen, R. C. A.(2006): “Properties of realized variance under alternative sampling
schemes,”Journal of Business and Economic Statistics, 24(2), 219–237.
Opschoor, A., N. Taylor, M. van der Wel, andD. van Dijk (2014): “Order
flow and volatility: An empirical investigation,”Journal of Empirical Finance, 28, 185–201.
Parlour, C. A. (1998): “Price Dynamics in Limit Order Markets,”Review of Financial Studies, 11(4), 789–816.
Peligrad, M. (1986): “Recent advances in the central limit theorem and its
weak invariance principle for mixing sequences of random variables (a survey),” in Dependence in Probability and Statistics: A Survey of Recent Results, ed. by