recent developments in high frequency financial ... - Index of

More documents

Recommendations

Info

196 References R. Liesenfeld et al. Andersen TG (1996) Return volatility and trading volume: an information flow interpretation of stochastic volatility. J Financ 51:169–204, 169–231 Ball C (1988) Estimation bias induced by discrete security prices. J Financ 43:841–865 Bauwens L, Giot P (2003) Asymmetric ACD models: introducing price information in ACD models with a two-state transition model. Empir Econ 28:709–731 Berndt EK, Hall BH, Hall RE, Hausman JA (1974) Estimation and inference in nonlinear structural models. Ann Econ Soc Meas 3/4:653–665 Bollerslev T, Melvin M (1994) Bid-ask spreads and volatility in the foreign exchange market – an empirical analysis. J Int Econ 36:355–372 Cameron AC, Trivedi PK (1998) Regression analysis of count data. Cambridge University Press, Cambridge Campbell JY, Lo AW, MacKinlay AC (1997) The econometrics of financial markets. Princeton University Press Cho DC, Frees EW (1988) Estimating the volatility of discrete stock prices. J Financ 43:451–466 Clark PK (1973) A subordinated stochastic process model with finite variance for speculative prices. Econometrica 41:135–155 Cox D (1981) Statistical analysis of time series: some recent developments. Scand J Statist 8:93– 115 Davis R, Dunsmuir W, Streett S (2003) Observation driven models for poisson counts. Biometrika 90:777–790 Denuit M, Lambert P (2005) Constraints on concordance measures in bivariate discrete data. J Multivar Anal 93:40–57 Diamond DW, Verrecchia RE (1987) Constraints on short-selling and asset price adjustment to private information. J Financ Econ 18:277–311 Diebold FX, Gunther TA, Tay AS (1998) Evaluating density forecasts, with applications to financial risk management. Int Econ Rev 39:863–883 Easley D, O’Hara M (1987) Price, trade size, and information in securities markets. J Financ Econ 19:69–90 Easley D, O’Hara M (1992) Time and the process of security price adjustment. J Financ 47:577– 607 Engle R (2000) The econometrics of ultra-high-frequency data. Econometrica 68(1):1–22 Harris L (1990) Estimation of stock variances and serial covariances from discrete observations. J Financ Quant Anal 25:291–306 Hausman JA, Lo AW, MacKinlay AC (1992) An ordered probit analysis of transaction stock prices. J Financ Econ 31:319–379 Jung R, Liesenfeld R (2001) Estimating time series models for count data using efficient importance sampling. Allg Stat Arch 85:387–407 Liesenfeld R (1998) Dynamic bivariate mixture models: modelling the behavior of prices and trading volume. J Bus Econ Stat 16:101–109 Liesenfeld R (2001) A generalized bivariate mixture model for stock price volatility and trading volume. J Econ 104:141–178 Lütkepohl H (1993) Introduction to multiple time series analysis. Springer, Berlin Heidelberg New York Mullahy J (1986) Specification and testing of some modified count data models. J Econ 33:341– 365 Nelson D (1991) Conditional heteroskedasticity in asset returns: a new approach. J Econ 43:227– 251 O’Hara M (1995) Market microstructure theory. Blackwell Publishers, Oxford Pohlmeier W, Ulrich V (1995) An econometric model of the two-part decision process in the demand for health. J Hum Resour 30:339–361 Russel J, Engle R (2002) Econometric analysis of discrete-valued, irregularly-spaced financial transactions data using a new autoregressive conditional multinomial model. University of California, San Diego (revised version of Discussion Paper 98–10) Russell JR, Engle RF (1998) Econometric analysis of discrete-valued, irregularly-spaced financial transactions data using a new autoregressive conditional multinomial model, presented at Second International Conference on High Frequency Data in Finance, Zurich, Switzerland
Modelling financial transaction price movements: a dynamic integer count data model 197 Rydberg T, Shephard N (2002) Dynamics of trade-by-trade price movements: decomposition and models. Discussion paper, Nuffiled College, Oxford University, will be published in Journal of Financial Econometrics Rydberg T, Shephard N (2003) Dynamics of trade-by-trade price movements: decomposition and models. J Financ Econ 1:2–25 Stevens W (1950) Fiducial limits of the parameter of a discontinuous distribution. Biometrika 37:117–129 Tauchen GE, Pitts M (1983) The price variability-volume relationship on speculative markets. Econometrica 51:485–505 Zeger S (1988) A regression model for time series of counts. Biometrika 75:621–629
Page 1 and 2:
High Frequency Financial Econometri
Page 3 and 4:
Prof. Luc Bauwens CORE Voie du Roma
Page 5:
vi Contents Intraday stock prices,
Page 8 and 9:
2 L. Bauwens et al. component nicel
Page 10 and 11:
4 L. Bauwens et al. but provides al
Page 13 and 14:
Luc Bauwens . Dagfinn Rime . Genaro
Page 15 and 16:
Exchange rate volatility and the mi
Page 17 and 18:
Page 19 and 20:
Page 21 and 22:
Page 23 and 24:
Page 25 and 26:
Page 27 and 28:
Page 29 and 30:
Page 31 and 32:
Page 33 and 34:
Page 35:
Page 38 and 39:
32 K. Bien et al. Although economet
Page 40 and 41:
34 K. Bien et al. 2.1 Copula functi
Page 42 and 43:
36 K. Bien et al. and x k t ≡ (xk
Page 44 and 45:
38 K. Bien et al. Fig. 3 Multivaria
Page 46 and 47:
40 K. Bien et al. Bivariate model s
Page 48 and 49:
42 K. Bien et al. deviation 0.0099,
Page 50 and 51:
44 K. Bien et al. - Set ˆzt = Âxt
Page 52 and 53:
46 K. Bien et al. % Frequency −0.
Page 54 and 55:
48 K. Bien et al. References Amilon
Page 56 and 57:
50 1 Introduction A. Escribano and
Page 58 and 59:
52 A. Escribano and R. Pascual Jang
Page 60 and 61:
54 A. Escribano and R. Pascual the
Page 62 and 63:
56 The generating processes of mark
Page 64 and 65:
58 with AtðLÞ ¼ 0 B @ 1 ð ÞAab
Page 66 and 67:
60 A. Escribano and R. Pascual cros
Page 68 and 69:
62 Hasbrouck (1991). The system is
Page 70 and 71:
64 unitary seller-initiated shock (
Page 72 and 73:
66 6.1 Estimation of the baseline m
Page 74 and 75:
Table 2 The base-line VEC model for
Page 76 and 77:
70 Table 3 Simulation of the base-l
Page 78 and 79:
72 revert towards narrow levels. As
Page 80 and 81:
Table 5 Impulse-response functions
Page 82 and 83:
76 We also show that NYSE buyer-ini
Page 84 and 85:
78 As 0 < a m < 1; L ð Þ ¼ 1 a m
Page 86 and 87:
80 NYSE 2000 Stocks AOL America Onl
Page 88 and 89:
82 A. Escribano and R. Pascual Madh
Page 90 and 91:
84 1 Introduction S. Frey, J. Gramm
Page 92 and 93:
86 develops the empirical methodolo
Page 94 and 95:
Table 1 Sample descriptives Company
Page 96 and 97:
90 comparability across stocks, we
Page 98 and 99:
92 subtracting the deviations from
Page 100 and 101:
94 market order distribution that c
Page 102 and 103:
96 Table 2 First stage GMM results
Page 104 and 105:
Table 4 First stage GMM results bas
Page 106 and 107:
100 S. Frey, J. Grammig quotes on e
Page 108 and 109:
102 S. Frey, J. Grammig approximate
Page 110 and 111:
104 To obtain the estimates in the
Page 112 and 113:
106 Hence, using the liquidity stat
Page 114 and 115:
108 In the main text we discuss the
Page 117 and 118:
Pierre Giot . Joachim Grammig How l
Page 119 and 120:
How large is liquidity risk in an a
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137:
Page 140 and 141:
134 A. D. Hall, N. Hautsch In this
Page 142 and 143:
136 includes order book variables,
Page 144 and 145:
138 An important determinant of liq
Page 146 and 147:
140 The innovation term ei is compu
Page 148 and 149:
Table 1 Order book characteristics
Page 150 and 151:
144 data covering the normal tradin
Page 152 and 153: 146 Table 3 Descriptive statistics
Page 154 and 155: Table 4 Fully specified ACI models
Page 156 and 157: Table 4 (continued) BHP NAB NCP TLS
Page 158 and 159: Table 5 ACI models without dynamics
Page 162 and 163: Table 6 ACI models without covariat
Page 166 and 167: 160 specification which includes or
Page 168 and 169: 162 5.2.5 The impact of the bid-ask
Page 170 and 171: 164 contrast to those of Pascual an
Page 173 and 174: Roman Liesenfeld . Ingmar Nolte . W
Page 175 and 176: Modelling financial transaction pri
Page 201: Modelling financial transaction pri
Page 206 and 207: 200 W. B. Omrane, H. V. Oppens anal
Page 208 and 209: 202 W. B. Omrane, H. V. Oppens of s
Page 210 and 211: 204 The extrema detection method ba
Page 212 and 213: 206 price 0.8565 0.8575 0.8585 0.85
Page 214 and 215: 208 We distinguish three possible c
Page 216 and 217: 210 originates. The most active tra
Page 218 and 219: 212 Table 2 Predictability of the c
Page 220 and 221: 214 6 Conclusion Using 5-min euro/d
Page 222 and 223: 216 where σk is the standard devia
Page 224 and 225: 218 If we meet a particular case su
Page 226 and 227: 220 C.5. Triple bottom (TB) TB is c
Page 228 and 229: 222 References W. B. Omrane, H. V.
Page 231 and 232: Juan M. Rodríguez-Poo · David Ver
Page 233 and 234: Semiparametric estimation for finan
Page 253 and 254:
Semiparametric estimation for finan
Page 255 and 256:
Page 257:
Page 260 and 261:
254 between price changes and durat
Page 262 and 263:
256 simply aggregated. Even after a
Page 264 and 265:
258 is to make use of the fact that
Page 266 and 267:
260 together with the restrictions
Page 268 and 269:
Table 3 Estimated probabilities of
Page 270 and 271:
264 A. S. Tay, C. Ting More interes
Page 272 and 273:
Table 4 Estimated probabilities of
Page 274 and 275:
268 Acknowledgements Tay gratefully
Page 276 and 277:
270 This paper explores the effect
Page 278 and 279:
272 contrast, price responses to po
Page 280 and 281:
274 Fig. 1 Continuous line is the T
Page 282 and 283:
276 the β’s can form a convex sh
Page 284 and 285:
278 fixing some intervals around th
Page 286 and 287:
280 . That is for a given absolute
Page 288 and 289:
282 30 25 20 15 10 5 CC UNEMW ISM U
Page 290 and 291:
Appendix Table A1 Consumer confiden
Page 292 and 293:
Table A3 Non-farm payrolls • ✓
Page 294 and 295:
Table A5 Weekly unemployment claims
Page 296 and 297:
290 Table A8 Retail sales • ✓ 1
Page 298 and 299:
292 Table A12 GDP, BI, TB and PI Re
Page 300 and 301:
294 V. Voev with the problem of how
Page 302 and 303:
296 V. Voev 2.1 A sample covariance
Page 304 and 305:
298 V. Voev Using the equicorrelate
Page 306 and 307:
300 V. Voev where �kl(t) is the (
Page 308 and 309:
302 V. Voev where hkl,k ′ l ′ i
Page 310 and 311:
304 V. Voev is 1.9%. From the daily
Page 312 and 313:
306 V. Voev ACF ACF ACF 0.5 0.5 0.5
Page 314 and 315:
308 V. Voev autocorrelated. Indeed,
Page 316 and 317:
310 V. Voev 5 Conclusion Table 2 Ro
Page 318:
312 V. Voev Engle R (1982) Autoregr
show all

recent developments in high frequency financial ... - Index of

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?