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274 Fig. 1 Continuous line is the TY future (scale in the right axis). Piecewise line is ISM (scale in the left axis) gradually accumulate positions in the direction of the trend whilst hedgers are counter-trend followers. Trends by themselves tend to last in time and the cor- rection to our equation coming from this momentum is represented by α 3. ð1 LÞTYt ¼ Xp i¼0 i;C;SðNÞðNt i E½Nt iŠÞ þ "t; (2) where i is the time measured since the moment of the release, C is the variable representing where we are in the business cycle and taking values ‘Top, Bottom, Expansion, Contraction’, and S(N) is the sign of the forecast error N−E(N). The fact that parameters depend on the moment of the business cycle and the sign of the forecasting errors, will permit us to discover hidden relations that otherwise are not possible to discern. For example, in an analysis of non-farms payrolls for β independent of C and S(N) we find a very strong relation, as expected. However, if β depends on the business cycle, non-farms payrolls affect TY only when the cycle is in the top and the bottom range and matters little when the cycle is in expansion. 3 Methodology D. Veredas We can simplify model (1) above in the following way. First we notice that the ISM and the TY price are inversely correlated: −0.71 (see also Fig. 1). TY moves up when ISM goes down, peaks close to when ISM bottoms then sells off when ISM starts recovering again, until it reaches a top. So the price trend and the macroeconomic trend are strongly related and, in a way, represent the same explanatory variable. In other words, we can assume that α1=−α 2. We therefore decide to divide the business cycle into different phases, explained below, and estimate the following equation: Given the previous arguments, our interest is to model the effect of news arrivals in the bond future market not only contemporaneously but also through time and depending on the momentum of the business cycle and the sign of the forecast error. We want to answer questions like: For how long does employment news
Macroeconomic surprises and short-term behaviour in bond futures 275 affect the bond future market? Or how does the effect of a CPI release in the bond future market change throughout the day? Under the econometric viewpoint, several alternatives are available. We could consider that the news arrivals and the bond future price are pure stationary time series and hence, in a univariate context, build a transfer function model, where the transfer function is the ratio of two finite polynomials yielding to an infinite polynomial and hence an infinite response. In a more simplistic context, we could consider a standard regression model with the news arrival and all its lags as exogenous variables and returns of TY as endogenous variables, yielding a model similar to (2) ð1LÞTYt ¼ Xp i¼0 iðNtiENt ½ iŠÞþ"t: (3) Here we assume that just one news item, i.e. one macroeconomic number, arrives to the market. Notice that p should not be fixed and should vary through the macroeconomic number, i.e. the shock effect does not last for the same length of time for all macroeconomic numbers. The differences in p through fundamentals will be tested empirically. This model, although very simple, is not adequate for several reasons. First, we would like to have a smooth effect of the news shock through time and, hence, some smoothness constraints between parameters instead of allowing them to vary freely. Second, the number of parameters can become large if our sample frequency is high and the effect of the fundamental on the TY stands for long. In order to avoid these problems we use a Polynomial Distributed Lag (PDL) model, also known as Almon’s model (1965). PDL models were introduced for a different reason than the two aforementioned: often when contemporaneous and past values of exogenous variables are introduced, the model may suffer of multicollinearity. This does not happen in our case since the exogenous variables take zero value everywhere except when the news is released. The Almon’s model is based on the assumption that the coefficients are represented by a polynomial of small degree K i ¼ 0 þ i 1 þ i 2 2 þ ::: þ i K K; i ¼ 0;:::;p > K; (4) which can be expressed in matrix terms as β = Hα where 1 0 0 0 1 1 H¼ 1 2 1 4 1 2K 1 3 9 3K . . . . 1 p p2 pK 0 1 B @ C C: C A This specification permits us to calculate the p coefficients estimating only K coefficients. K is an integer number, usually between three and four. The degree of the polynomial will determine its flexibility. For degree zero, all the β’s will be equal and hence they form an horizontal straight line. For degree one, the β’s decrease uniformly. For K=2 the β’s form a concave or convex bell. For K=3 (5)
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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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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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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 282 and 283: 276 the β’s can form a convex sh
Page 284 and 285: 278 fixing some intervals around th
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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
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