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292 Table A12 GDP, BI, TB and PI References • PI GDP BI TB • ✓ 10 min □ 20 min 30 min ▪ 40 min ▪ 50 min ▪ 60 min □ 70 min 80 min 90 min 100 min □ 110 min □ 120 min See legend in Tables A1 and A6 D. Veredas Almon S (1965) The distributed lag between capital appropiations and expenditures. Econometrica 33:178–196 Andersen TG, Bollerslev T (1998) Deutsche mark-dollar volatility: intraday activity patterns, macroeconomic announcements, and longer run dependencies. J Finance LIII:219–265 Andersen TG, Bollerslev T, Diebold FX, Vega C (2003) Micro effects and macro announcements: real-time price discovery in foreign exchange. Am Econ Rev 93:38-62 Berkman NG (1978) On the significance of weekly changes in M1. New England economic review May–June 5–22 Conrad J, Cornell B, Landsman WR (2002) When is bad news really bad news? J Finance 57: 2507–2533 DeGennaro RP, Shrieves RE (1997) Public information releases, private information arrival and volatility in the foreign exchange market. J Empir Finance 4:295-315 Flemming MJ, Remolona EM (1999) What moves the bond market? J Portf Manage 25:28–38 Goodhart CAE, Hall SG, Henry SGB, Pesaran B (1993) News effects in a high frequency model of the sterling–dollar exchange rate. J Appl Econ 8:1–13 Grossman J (1981) The rationality of money supply expectations and the short-run response of interest rates to monetary surprises. J Money Credit Bank 13:409–424 Hautsch N, Hess D (2002) The processing of non-anticipated information in financial markets. Analyzing the impact of surprises in the employment report. European Finance Review 6: 133–161 Hess D (2004) Determinants of the relative price impact of unanticipated information in US macroeconomic releases. J Futures Mark 24:609–630 Kanehman D, Tversky A (1979) Prospect theory: an analysis of decision under uncertainty. Econometrica 47:263–291 Li L, Engle RF (1998) Macroeconomic announcements and volatility of treasury futures. UCSD Discussion Paper 98–27 McQueen G, Roley VV (1993) Stock prices, news and business conditions. Rev Financ Stud 6:683–707 Niemira MP, Zukowsky GF (1998) Trading the fundamentals. McGraw Hill, New York Roley VV (1983) The response of short-term interest rates to weekly money announcements. J Money Credit Bank 15:344–354 Veronesi P (1999) Stock market overreaction to bad news in good time: a rational expectations equilibrium model. Rev Financ Stud 12:975–1007
Valeri Voev Dynamic modelling of large-dimensional covariance matrices Abstract Modelling and forecasting the covariance of financial return series has always been a challenge due to the so-called ‘curse of dimensionality’. This paper proposes a methodology that is applicable in large-dimensional cases and is based on a time series of realized covariance matrices. Some solutions are also presented to the problem of non-positive definite forecasts. This methodology is then compared to some traditional models on the basis of its forecasting performance employing Diebold–Mariano tests. We show that our approach is better suited to capture the dynamic features of volatilities and covolatilities compared to the sample covariance based models. 1 Introduction Modelling and forecasting the variances and covariances of asset returns is crucial for financial management and portfolio selection and re-balancing. Recently, this branch of the econometric literature has grown at a very fast pace. One of the simplest methods used is the sample covariance matrix.Astylized fact, however, is that there is a serial dependence in the second moments of returns. Thus, more sophisticated models had to be developed which incorporate this property, as well as other well-known features of financial return distributions such as leptokurtosis or the so-called ‘leverage effect’. This led to the development of the univariate GARCH processes and their extension—the multivariate GARCH (MGARCH) models (for a comprehensive review see Bauwens et al. (2006)), which include also the modelling of covariances. One of the most severe drawbacks of the MGARCH models, however, is the difficulty of handling dimensions higher than 4 or 5 (or with very restrictive assumptions). Another more practically oriented field of research deals Valeri Voev (�) University of Konstanz, CoFE, Konstanz, Germany PBox D-124, University of Konstanz, 78457 Konstanz, Germany E-mail: valeri.voev@uni-konstanz.de
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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 262 and 263: 256 simply aggregated. Even after a
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Page 270 and 271: 264 A. S. Tay, C. Ting More interes
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Page 274 and 275: 268 Acknowledgements Tay gratefully
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Page 278 and 279: 272 contrast, price responses to po
Page 280 and 281: 274 Fig. 1 Continuous line is the T
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Page 290 and 291: Appendix Table A1 Consumer confiden
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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
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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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