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280 . That is for a given absolute value of forecast error, the bond sell-off is more violent than a bond rally. And it holds for all the economic environments (i.e. and vs. and ) except in an economic expansion ( vs. ) where it seems that negative forecast errors (i.e. a weaker confidence than expected) generate larger bond moves than a positive error (see Fig. 4). This is an illustration that when CC comes ‘against the trend’, it tends to get more publicity in the bond market. Also, when the economy is contracting, positive forecast errors tend to impact on the market more than negative ones. 5.2 General results 5.2.1 Tables A2 to A5 All the signs, except in some pathological cases, are intuitively correct. ISM and Non-farm payrolls higher than expected yield bond market sell-offs. And UNEM and UNEMW higher than expected yields bond market rallies. Additionally, asymmetries and results are robust across divisions of the economic cycle. NFP, UNEM and UNEMW represent the labour market. But NFP is the variable that has the stronger effect. NPF has tendency to exacerbate the peaks and bottoms of the business cycle (see , , , ). By contrast, UNEM has a lower and different effect. It has a tendency to accelerate the bond trend (see , ). Yet, as they are both released at the same time we would expect similar effects on TY. The reasons for this different reaction are: 1) UNEM is not seasonally adjusted by the US Census Bureau whilst the Bureau of Labour Statistics adjusts NFP. We could adjust UNEM by seasonality but it is not what the market observes. 2) The NFP survey has a larger sample size (390,000 establishments) than UNEM (60,000 households) and 3) in 1994 the questionnaire and the collection method for UNEM changed, introducing some disturbances on the sample. Regarding UNEMW, it is the only weekly released fundamental. It is not based on a survey but on a complete register announced every week. It has therefore a short term ongoing view of the labour market and can provide the first signals of some future change in the economy, i.e. it is a sort of leading indicator of the state of the economy. But, although it can be very useful for discover ongoing hidden problems, it is very erratic. 5.2.2 Tables A6–A11 D. Veredas These fundamentals have a smaller effect on the bond future than the previous ones. Nevertheless, sings (not reported here) are intuitively correct and results are robust to all economic environments. This is especially true for PPI, CPI, RS and IP. By contrast, the effects of HS and DG are very limited. They last no longer than half an hour with some rebound in some cases after one hour and a half. PPI and CPI deserve a special comment. These two fundamentals are inflation measures but PPI has a weak impact in TY, contrary to CPI. PPI is not significant because ISM, released slightly earlier, is informative enough about the manufacturing sector. Traders still keep in mind the ISM results as a benchmark of the manufacturing
Macroeconomic surprises and short-term behaviour in bond futures 281 and industrial sector but wait for the CPI before gaining a precise idea of the inflation. 5.2.3 Table A12 Even in the less exhaustive case, PI, GDP, BI and TB do not have any effect on TY except, very vaguely, PI. The statistical significance of the coefficients is below acceptance and we will not push the analysis further. In summary, the market also seems to have a general bias when it approaches certain numbers. We call a Bearish Bias a situation where the expected sell-off is larger in magnitude than the expected rally for the same absolute value of the forecast error and the opposite sign. Also the Bullish Bias is where the expected rally is larger in magnitude than the expected sell-off for the same absolute value of the forecast error and the opposite sign. Across the sample we draw the conclusions that there is a Bearish Bias for news coming from CC, CPI, and IP, and a Bullish Bias for news coming from UNEM, ISM, NFP, RS and UNEMW. Table 2 summarizes the effect of all the fundamentals in TY for each stage of the business cycle. It shows the number of significant coefficients for each phase of the business cycle. The left side of the Table (first four columns) indicates that the market reacts more when the economy is in the top of the cycle and when it is contracting. In other words, the market feels that the business cycle is at the top and therefore sooner or later the cycle will start its downward trend, but they do not know when. After the first signals of weakness, the economy starts to contract and the market, which can feel the turn in the cycle, eagers for further signals that confirm the negative trend. Any news therefore has an effect. By contrast, when the economy is expanding, the market is confident about its strength and so news, of any kind, affects the bond future much less. The right and middle parts of the Table give a more refined history. Last four columns show that the market responds more strongly to bad news in good times than in bad times. This confirms the theories of behavioural finance and Veronesi (1999), as well as Conrad et al. (2002) findings. The market responds strongly when the economy is contracting. This is again a confirmation that when the cycle is downward, TY reacts to bad news more than when the economy is growing. Results in the middle columns are not so enlightening. In particular, good news does not have the largest impact in the bottom of the cycle, but when the economy is contracting. It may be an indication that, when the business cycle is downward, the market is uneasy and afraid that the negative trend may continue. In this context, any forecasting error, positive or negative, introduces more uncertainty to the market and in turn induces a reaction in prices. This result nicely dovetails with the results in the left columns. Conversely, when the economy is expanding, the effect of positive news is smaller than in any other phase of the cycle. This again dovetails with the equivalent result in the left columns. In summary, bad news has a stronger effect in good times than in bad times and good news has little effect in bad times. And when the economic phase is downward, any news has a strong effect but when the phase is upward, news has barely any effect.
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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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Juan M. Rodríguez-Poo · David Ver
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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
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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
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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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