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Juan M. Rodríguez-Poo · David Veredas · Antoni Espasa Semiparametric estimation for financial durations Abstract We propose a semiparametric model for the analysis of time series of durations that show autocorrelation and deterministic patterns. Estimation rests on generalized profile likelihood, which allows for joint estimation of the parametric— an ACD type of model—and nonparametric components, providing consistent and asymptotically normal estimators. It is possible to derive the explicit form for the nonparametric estimator, simplifying estimation to a standard maximum likelihood problem. Keywords Generalized profile likelihood · ACD model · Seasonality JEL Classification C14 · C15 · C22 · C32 1 Introduction Modeling financial durations has been a very active area of research since Engle and Russell (1998) introduced the autoregressive conditional duration (ACD) model. Their analysis is justified from an economic and a statistical point of view. Market microstructure theory shows that the time between events in stock markets conveys information that is used by market participants. On the other hand, financial durations are one-dimensional point processes (with time as space) and the analysis of these processes has a long tradition in statistics. Since the ACD model, a plethora of modifications and alternatives have been proposed. Bauwens and Giot (2000) introduce the Log-ACD model, an exponential Juan M. Rodríguez-Poo Departamento de Economía, niversidad de Cantabria, E-39005 Santander, Belgium David Veredas Universite Libre de Bruxelles (ECARES), 50 Ave Jeanne CP114 B1050 Brussels, Belgium David Veredas CORE, Université Catholique de Louvain, Louvain, Belgium Antoni Espasa Departamento de Estadística y Econometría, Universidad Carlos III de Madrid, E-28903 Getafe, Spain
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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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Modelling financial transaction pri
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Table A2 ISM • ✓ 10 min −0.09
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Table A4 Unemployment rate • ✓
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Valeri Voev Dynamic modelling of la
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Dynamic modelling of large-dimensio
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