recent developments in high frequency financial ... - Index of

More documents

Recommendations

Info

4 L. Bauwens et al. but provides also an answer to practitioners’ concerns relative to the measurement of market risk. Hall and Hautsch study the determinants of order aggressiveness and traders’ order submission strategy in an open limit order book market. Applying an order classification scheme, they model the most aggressive market orders, limit orders as well as cancellations on both sides of the market employing a six-dimensional autoregressive conditional intensity model. Using order book data from the Australian Stock Exchange, they find that market depth, the queued volume, the bid-ask spread, recent volatility, as well as recent changes in both the order flow and the price play an important role in explaining the determinants of order aggressiveness. Overall, their empirical results broadly confirm theoretical predictions on limit order book trading. Liesenfeld, Nolte, and Pohlmeier develop a dynamic model to capture the fundamental properties of financial prices at the transaction level. They decompose the price in discrete components—direction and size of price changes—and, using autoregressive multinomial models, they show that the model is well suited to test some theoretical implications of market microstructure theory on the relationship between price movements and other marks of the trading process. Intradaily financial data is characterized by its dynamic behavior as well by deterministic seasonal patterns that are due to the market structure. Volatility is known to be larger at the opening and closing than during the lunch time. Similarly for financial durations: they are shorter at the opening and closing, indicating higher activity at these times of the day. Any econometric model should therefore incorporate these features. Rodriguez-Poo, Veredas, and Espasa propose a semiparametric model for financial durations. The dynamics are specified parametrically, with an ACD type of model, while seasonality is left unspecified and hence nonparametric. 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. Tay and Ting carry out an empirical analysis using high frequency data and more specifically estimate the distribution of price changes conditional on trade volume and duration between trades. Their main empirical finding is that even when controlling for the trade volume level, duration has an effect on the distribution of price changes, and the higher the conditioning volume level, the higher the impact of duration on price changes. The authors find significant positive (negative) skewness in the distribution of price changes in buyer (respectively seller)—initiated trades, and see this finding as support of the Diamond and Verrecchia (1987) analysis of the probability of large price falls with high levels of duration. 2 The analysis is carried out using up-to-date techniques for the nonparametric estimation of conditional distributions, and outlines a descriptive procedure that can be useful in choosing the specification of the relationship between duration, volume, and prices when performing a parametric investigation. 2 Diamond, D.W. and Verrecchia, R.E. (1987), “Constraints on Short-Selling and Asset Price Adjustment to Private Information”, Journal of Financial Economics, 18, 277–311.
Editor’s introduction: recent developments in high frequency financial econometrics 5 News is the driving force of price movements in financial markets. Veredas analyses the effect of macroeconomic news on the price of the USA 10-year treasury bond future. Considering 15 fundamentals, he investigates the effect of their forecasting errors conditional upon their sign and the momentum of the business cycle. The results show that traders react when the forecasting error differs from zero. The reaction to a positive or negative forecasting error is different and, most importantly, the reaction varies significantly depending on the momentum of the economic cycle. Moreover, the time of the release matters: the closer it is to the covering period, the more effect it has on the bond future. Modeling and forecasting the covariance of a large number of financial return series has always been a challenge due to the so-called “curse of dimensionality.” For example, the multivariate GARCH models are heavily parameterized or the dynamics of conditional variances and covariances must be restricted to reduce the number of parameters, e.g., through factor structures. As an alternative, the sample covariance matrix has often been used, based on rolling windows, e.g., a monthly covariance is estimated from monthly returns of the last 5 years. Voev compare this approach, and variants of it, with others that use higher frequency data (daily data), the so-called realized covariance matrix. In each approach, there are different ways to define forecasts. For example, the realized covariance matrix for month t may serve to predict the covariance matrix of next month. A more sophisticated forecast is obtained by taking a convex combination of the realized covariance of month t and an equicorrelated covariance matrix, a technique known as “shrinkage.” The previous forecasts are static. Another method consists in modeling the different elements of the realized covariance matrix by using separate univariate time series models to construct forecasts. This raises the difficulty to obtain always a positive definite forecast. Voev measures the deviation of the forecast as a matrix from its target by using the Frobenius norm, where the target or “true” covariance matrix is the realized covariance matrix (observed ex post), and by Diebold–Mariano statistical tests. His main conclusion is that the dynamic models result in the smallest errors in the covariance matrix forecasts for most of the analyzed data series.
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 13 and 14: Luc Bauwens . Dagfinn Rime . Genaro
Page 15 and 16: 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
Page 70 and 71:
64 unitary seller-initiated shock (
Page 72 and 73:
66 6.1 Estimation of the baseline m
Page 74 and 75:
Table 2 The base-line VEC model for
Page 76 and 77:
70 Table 3 Simulation of the base-l
Page 78 and 79:
72 revert towards narrow levels. As
Page 80 and 81:
Table 5 Impulse-response functions
Page 82 and 83:
76 We also show that NYSE buyer-ini
Page 84 and 85:
78 As 0 < a m < 1; L ð Þ ¼ 1 a m
Page 86 and 87:
80 NYSE 2000 Stocks AOL America Onl
Page 88 and 89:
82 A. Escribano and R. Pascual Madh
Page 90 and 91:
84 1 Introduction S. Frey, J. Gramm
Page 92 and 93:
86 develops the empirical methodolo
Page 94 and 95:
Table 1 Sample descriptives Company
Page 96 and 97:
90 comparability across stocks, we
Page 98 and 99:
92 subtracting the deviations from
Page 100 and 101:
94 market order distribution that c
Page 102 and 103:
96 Table 2 First stage GMM results
Page 104 and 105:
Table 4 First stage GMM results bas
Page 106 and 107:
100 S. Frey, J. Grammig quotes on e
Page 108 and 109:
102 S. Frey, J. Grammig approximate
Page 110 and 111:
104 To obtain the estimates in the
Page 112 and 113:
106 Hence, using the liquidity stat
Page 114 and 115:
108 In the main text we discuss the
Page 117 and 118:
Pierre Giot . Joachim Grammig How l
Page 119 and 120:
How large is liquidity risk in an a
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137:
Page 140 and 141:
134 A. D. Hall, N. Hautsch In this
Page 142 and 143:
136 includes order book variables,
Page 144 and 145:
138 An important determinant of liq
Page 146 and 147:
140 The innovation term ei is compu
Page 148 and 149:
Table 1 Order book characteristics
Page 150 and 151:
144 data covering the normal tradin
Page 152 and 153:
146 Table 3 Descriptive statistics
Page 154 and 155:
Table 4 Fully specified ACI models
Page 156 and 157:
Table 4 (continued) BHP NAB NCP TLS
Page 158 and 159:
Table 5 ACI models without dynamics
Page 160 and 161:
Page 162 and 163:
Table 6 ACI models without covariat
Page 164 and 165:
Page 166 and 167:
160 specification which includes or
Page 168 and 169:
162 5.2.5 The impact of the bid-ask
Page 170 and 171:
164 contrast to those of Pascual an
Page 173 and 174:
Roman Liesenfeld . Ingmar Nolte . W
Page 175 and 176:
Modelling financial transaction pri
Page 177 and 178:
Page 179 and 180:
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
Page 191 and 192:
Page 193 and 194:
Page 195 and 196:
Page 197 and 198:
Page 199 and 200:
Page 201 and 202:
Page 203:
Page 206 and 207:
200 W. B. Omrane, H. V. Oppens anal
Page 208 and 209:
202 W. B. Omrane, H. V. Oppens of s
Page 210 and 211:
204 The extrema detection method ba
Page 212 and 213:
206 price 0.8565 0.8575 0.8585 0.85
Page 214 and 215:
208 We distinguish three possible c
Page 216 and 217:
210 originates. The most active tra
Page 218 and 219:
212 Table 2 Predictability of the c
Page 220 and 221:
214 6 Conclusion Using 5-min euro/d
Page 222 and 223:
216 where σk is the standard devia
Page 224 and 225:
218 If we meet a particular case su
Page 226 and 227:
220 C.5. Triple bottom (TB) TB is c
Page 228 and 229:
222 References W. B. Omrane, H. V.
Page 231 and 232:
Juan M. Rodríguez-Poo · David Ver
Page 233 and 234:
Semiparametric estimation for finan
Page 235 and 236:
Page 237 and 238:
Page 239 and 240:
Page 241 and 242:
Page 243 and 244:
Page 245 and 246:
Page 247 and 248:
Page 249 and 250:
Page 251 and 252:
Page 253 and 254:
Page 255 and 256:
Page 257:
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 280 and 281:
274 Fig. 1 Continuous line is the T
Page 282 and 283:
276 the β’s can form a convex sh
Page 284 and 285:
278 fixing some intervals around th
Page 286 and 287:
280 . That is for a given absolute
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
show all

recent developments in high frequency financial ... - Index of

Create successful ePaper yourself

Delete template?

Save as template?