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250 J. M. Rodríguez-Poo et al. (A.5) and (A.1) to (A.4). Condition NP(b) (least favorable curve) is immediate from Lemma 6 of Severini and Wong (1992). This is due to the fact that we assume that the conditional density function belongs to the exponential family. The proof of Eq. (21) is equal to the proof of Eq. (15) except for that (L.2) is replaced by (B.2). References Andersen T, Bollerslev T (1997) Heterogenous information arrivals and return volatility dynamics: unrecovering the long-run in high frequency returns. J Finance 52:975–1005 Andersen T, Bollerslev T (1998) Deustche mark-dollar volatility: intraday activity patterns, macroeconomic announcements, and longer run dependencies. J Finance 53:219–265 Baillie R, Bollerslev T (1990) Intra-day and inter-market volatility in foreign exchange rates. Rev Econ Stud 58:565–585 Bauwens L, Giot P (2000) The logarithmic ACD model: an application to the bid-ask quote process of three NYSE stocks. Annales d’Economie et de Statistique 60:117–149 Bauwens L, Giot P, Grammig J, Veredas D (2004) A comparison of financial duration models via density forecast. Int J Forecast 20:589–604 Bauwens L, Veredas D (2004) The stochastic conditional duration model: a latent factor model for the analysis of financial durations. J Econ 119(2):381–412 Beltratti A, Morana C (1999) Computing value at risk with high frequency data. J Empir Finance 6:431–455 Birgé L, Massart P (1993) Rate of convergence for minimum contrast estimators. Probab Theory Relat Field 97:113–150 Bollerslev T, Domowitz I (1993) Trading patterns and prices in the interbank foreign exchange market. J Finance 48:1421–1443 Bosq D (1998) Nonparametric statistics for stochastic processes. Estimation and prediction, 2nd edition. Lecture Notes in Statistics, 110. Springer-Verlag, New York Chen SX (2000) A beta kernel estimation for density functions. Comput Stat Data Anal 31:131– 145 Chen SX, Shen SX (1998) Sieve extremum estimates for weakly dependent data. Econometrica 66:289–314 Cox DR, Kim TY (1995) Moment bounds for mixing random variables useful in nonparametric function estimation. Stochastic Processes and their Applications 56:151–158 Diebold FX, Gunther TA, Tay AS (1998) Evaluating density forecasts, with applications to financial risk management. Int Econ Rev 39:863–883 Drost FC, Werker BJM (2004) Efficient estimation in semiparametric time series: the ACD model. J Bus Econ Stat 22:40–50 Engle RF, Russell JR (1995) Autoregressive conditional duration: a new model for irregularly spaced data. unpublished manuscript , University of California, San Diego Engle RF, Russell JR (1997) Forecasting the frequency of changes in quoted foreign exchange prices with the ACD model. J Empir Finance 4:187–212 Engle RF, Russell JR (1998) Autoregressive conditional duration: a new approach for irregularly spaced transaction data. Econometrica 66:1127–1162 Engle RF (2000) The econometrics of ultra high frequency data. Econometrica 68:1–22 Eubank RL (1988) Spline smoothing and nonparametric regression. Marcell Decker, New York Fan J, Heckman NE, Wand MP (1995) Local polynomial kernel regression for generalized linear models and quasi-likelihood functions. J Am Stat Assoc 90:141–150 Fernandes M, Grammig J (2001) A family of autoregressive conditional duration models. CORE DP 2001/36, Université catholique de Louvain. Forthcoming J Econ Gerhard F, Haustch N (2002) Volatility estimation on the basis of price intensities. J Empir Finance 9:57–89 Ghysels E, Gouriéroux C, Jasiak J (1997) Stochastic volatility duration models. Working paper 9746. J Econ 119(2):413–435 Gouriéroux C, Monfort A, Trognon A (1984) Pseudo maximum likelihood methods: theory. Econometrica 52:681–700 Grammig J, Maurer KO (2000) Non-monotonic hazard functions and the autoregressive conditional duration model. Econ J 3:16–38
Semiparametric estimation for financial durations 251 Grenander U (1981) Abstract inference. Wiley, New York Kiefer J, Wolfowitz J (1956) Consistency of the maximum likelihood estimator in the presence of infinetely many incidental parameters. Ann Math Stat 28:887–906 McCullagh P, Nelder JA (1983) Generalized linear models. Chapman and Hall, London Meitz M, Terasvirta T (2004) Evaluating models of autoregressive conditional duration. Working Paper Series in Economics and Finance 557, Stockholm School of Economics. Forthcoming in J Bus Econ Stat Severini TA, Staniswalis JG (1994) Quasi-likelihood estimation in semiparametric models. J Am Stat Assoc 89:501–511 Severini TA, Wong WH (1992) Profile likelihood and conditionally parametric models. Ann Stat 20:1768–1802 Shen X, Wong WH (1994) Convergence rates for sieve estimates. Ann Stat 22:580–615 Spierdijk L, Nijman TE, van Soest AHO (2004) Price dynamics and trading volume: a semiparametric approach. TW Memorandum, University of Twente Staniswalis JG (1989) On the kernel estimate of a regression function in likelihood based models. J Am Stat Assoc 84:276–283 Wooldridge JM (1994) Estimation and inference for dependent processes. In: Engle RF, McFadden DL (eds) Handbook of econometrics, vol. 4. Elsevier Science, New York Zhang MY, Russell JR, Tsay RT (2001) A nonlinear autoregressive conditional duration model with applications to financial transaction data. J Econ 104:179–207
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High Frequency Financial Econometri
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Prof. Luc Bauwens CORE Voie du Roma
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vi Contents Intraday stock prices,
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2 L. Bauwens et al. component nicel
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4 L. Bauwens et al. but provides al
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Luc Bauwens . Dagfinn Rime . Genaro
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Exchange rate volatility and the mi
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32 K. Bien et al. Although economet
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34 K. Bien et al. 2.1 Copula functi
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36 K. Bien et al. and x k t ≡ (xk
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38 K. Bien et al. Fig. 3 Multivaria
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40 K. Bien et al. Bivariate model s
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42 K. Bien et al. deviation 0.0099,
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44 K. Bien et al. - Set ˆzt = Âxt
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46 K. Bien et al. % Frequency −0.
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48 K. Bien et al. References Amilon
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50 1 Introduction A. Escribano and
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52 A. Escribano and R. Pascual Jang
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54 A. Escribano and R. Pascual the
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56 The generating processes of mark
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58 with AtðLÞ ¼ 0 B @ 1 ð ÞAab
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60 A. Escribano and R. Pascual cros
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62 Hasbrouck (1991). The system is
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64 unitary seller-initiated shock (
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66 6.1 Estimation of the baseline m
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Table 2 The base-line VEC model for
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70 Table 3 Simulation of the base-l
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72 revert towards narrow levels. As
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Table 5 Impulse-response functions
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76 We also show that NYSE buyer-ini
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78 As 0 < a m < 1; L ð Þ ¼ 1 a m
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80 NYSE 2000 Stocks AOL America Onl
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82 A. Escribano and R. Pascual Madh
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84 1 Introduction S. Frey, J. Gramm
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86 develops the empirical methodolo
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Table 1 Sample descriptives Company
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90 comparability across stocks, we
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92 subtracting the deviations from
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94 market order distribution that c
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96 Table 2 First stage GMM results
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Table 4 First stage GMM results bas
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100 S. Frey, J. Grammig quotes on e
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102 S. Frey, J. Grammig approximate
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104 To obtain the estimates in the
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106 Hence, using the liquidity stat
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108 In the main text we discuss the
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Pierre Giot . Joachim Grammig How l
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How large is liquidity risk in an a
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134 A. D. Hall, N. Hautsch In this
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136 includes order book variables,
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138 An important determinant of liq
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140 The innovation term ei is compu
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Table 1 Order book characteristics
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144 data covering the normal tradin
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146 Table 3 Descriptive statistics
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Table 4 Fully specified ACI models
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Table 4 (continued) BHP NAB NCP TLS
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Table 5 ACI models without dynamics
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Table 6 ACI models without covariat
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160 specification which includes or
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162 5.2.5 The impact of the bid-ask
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164 contrast to those of Pascual an
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Roman Liesenfeld . Ingmar Nolte . W
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Modelling financial transaction pri
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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
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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
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300 V. Voev where �kl(t) is the (
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302 V. Voev where hkl,k ′ l ′ i
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304 V. Voev is 1.9%. From the daily
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306 V. Voev ACF ACF ACF 0.5 0.5 0.5
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308 V. Voev autocorrelated. Indeed,
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310 V. Voev 5 Conclusion Table 2 Ro
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312 V. Voev Engle R (1982) Autoregr
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