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14 the second holds that an increase in the number of traders without changes in the information intensity reduces volatility. That Eq. (8) is the case is generally suggested by Table 1, whereas Eq. (9) is suggested by Tauchen and Pitts (1983). However, it should be noted that the empirical results of Jorion (1996) and Bjønnes et al. (2005) do not support the hypothesis that an increase in the number of traders reduces volatility. The most commonly used indicators of information arrival are selected samples from the news-screens of Reuters or Telerate, quoting frequency, the number of transacted contracts and transaction volume. The former is laborious to construct and at any rate not exhaustive with respect to the range of information events that might induce price revision, and the latter two are not readily available in foreign exchange markets. So quote frequency is our indicator of information arrival. More precisely, before 1 January 1999 our quote series consists of the number of BID NOK/DEM quotes per week, and after 1 January 1999 it consists of the number of BID NOK/EUR quotes per week. We denote the log of the number of quotes in week t by qt, but it should be noted that we have adjusted the series for two changes in the underlying data collection methodology, see the data Appendix for details. Graphs of q t and Δq t are contained in Fig. 2. In empirical analysis it is common to distinguish between “expected” and “unexpected” activity, see amongst others Bessembinder and Seguin (1992), Jorion (1996) and Bjønnes et al. (2005). Expected activity is supposed to reflect “normal” or “everyday” quoting or trading activity by traders, and should thus be negatively associated with volatility according to Eq. (9) since this essentially reflects the number of active traders. Unexpected activity on the other hand refers to changes in the rate at which relevant information arrives to the market and should increase volatility. The strategy that is used in order to obtain the expected and unexpected components is to interpret the fitted values of an ARMA–GARCH model as the expected component and the residual as the unexpected. In our case an ARMA(1,1) specification of Δqt with a GARCH(1,1) structure on the error terms suffices in order to obtain uncorrelated standardised residuals and uncorrelated squared standardised residuals. The model and estimation output is contained in Table 3. The expected values are then computed by generating fitted values of qt (not of Δq t) and are denoted bqt. The unexpected values are defined as qt bqt. It has been argued that such a strategy might result in a so-called “generated regressor bias”-see for example Pagan (1984), so we opt for an alternative strategy which yields virtually identical results. As it turns out using q t directly instead of bqt , and Δq t instead of the residual, has virtually no effect on the estimates in Section 3. The reason can be deduced by looking at the bottom graph of Fig. 2. For statistical purposes q t is virtually identical to bqt, and Δq t is virtually identical to the residual (the sample correlations are 0.85 and 0.94, respectively). Summarised, then, we use q t as our measure of the number of active traders and Δq t as our measure of changes in the rate at which information arrives to the market. Both variables serve as explanatory variables in the modelling of volatility in Section 3. 2.3 Other impact variables L. Bauwens et al. Other economic variables may also influence the level of volatility and should be controlled for in empirical models. In line with the conventions introduced above
Exchange rate volatility and the mixture of distribution hypothesis 15 Fig. 2 The log of weekly number of BID NOK/EUR quotes (BID NOK/DEM before 1999) in the upper graph, the log-difference of weekly quoting in the middle graph, and scatter plots of qt vs. bqt and Δq t vs. residual in the bottom graph lower-case means the log-transformation is applied, and upper-case means it is not. The only exceptions are the interest-rate variables, a Russian moratorium dummy id t equal to 1 in one of the weeks following the Russian moratorium (the week containing Friday 28 August 1998 to be more precise) and 0 elsewhere, and a step dummy sd t equal to 0 before 1997 and 1 after.
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 19: Exchange rate volatility and the mi
Page 35: Exchange rate volatility and the mi
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
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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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200 W. B. Omrane, H. V. Oppens anal
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202 W. B. Omrane, H. V. Oppens of s
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204 The extrema detection method ba
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206 price 0.8565 0.8575 0.8585 0.85
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208 We distinguish three possible c
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210 originates. The most active tra
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212 Table 2 Predictability of the c
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214 6 Conclusion Using 5-min euro/d
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216 where σk is the standard devia
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218 If we meet a particular case su
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220 C.5. Triple bottom (TB) TB is c
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222 References W. B. Omrane, H. V.
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Juan M. Rodríguez-Poo · David Ver
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Semiparametric estimation for finan
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254 between price changes and durat
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256 simply aggregated. Even after a
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258 is to make use of the fact that
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260 together with the restrictions
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Table 3 Estimated probabilities of
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264 A. S. Tay, C. Ting More interes
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Table 4 Estimated probabilities of
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268 Acknowledgements Tay gratefully
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270 This paper explores the effect
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272 contrast, price responses to po
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274 Fig. 1 Continuous line is the T
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276 the β’s can form a convex sh
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278 fixing some intervals around th
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280 . That is for a given absolute
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282 30 25 20 15 10 5 CC UNEMW ISM U
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Appendix Table A1 Consumer confiden
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Table A3 Non-farm payrolls • ✓
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Table A5 Weekly unemployment claims
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290 Table A8 Retail sales • ✓ 1
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292 Table A12 GDP, BI, TB and PI Re
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294 V. Voev with the problem of how
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296 V. Voev 2.1 A sample covariance
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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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