There's more to volatility than volume

被引:51
作者
Gillemot, Laszlo
Farmer, J. Doyne
Lillo, Fabrizio
机构
[1] Santa Fe Inst, Santa Fe, NM 87501 USA
[2] Budapest Univ Technol & Econ, H-1111 Budapest, Hungary
[3] Univ Palermo, Dipartimento Fis & Tecnol Relat, I-90128 Palermo, Italy
[4] CNR, INFM, I-90128 Palermo, Italy
关键词
financial markets; volatility; volume; subordinated processes;
D O I
10.1080/14697680600835688
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
it is widely believed that fluctuations in transaction volume, as reflected in the number of transactions and to a lesser extent their size, are the main cause of clustered volatility. Under this view bursts of rapid or slow price diffusion reflect bursts of frequent or less frequent trading, which cause both clustered volatility and heavy tails in price returns. We investigate this hypothesis using tick by tick data from the New York and London Stock Exchanges and show that only a small fraction of volatility fluctuations are explained in this manner. Clustered volatility is still very strong even if price changes are recorded on intervals in which the total transaction volume or number of transactions is held constant. In addition the distribution of price returns conditioned on volume or transaction frequency being held constant is similar to that in real time, making it clear that neither of these are the principal cause of heavy tails in price returns. We analyse recent results of Ane and Geman (2000: J. Finance, 55, 2259-2284) and Gabaix et al. (2003: Nature, 423, 267-270), and discuss the reasons why their conclusions differ from ours. Based on a cross-sectional analysis we show that the long-memory of volatility is dominated by factors other than transaction frequency or total trading volume.
引用
收藏
页码:371 / 384
页数:14
相关论文
共 35 条
[1]   The distribution of realized exchange rate volatility [J].
Andersen, TG ;
Bollerslev, T ;
Diebold, FX ;
Labys, P .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2001, 96 (453) :42-55
[2]   Modeling and forecasting realized volatility [J].
Andersen, TG ;
Bollerslev, T ;
Diebold, FX ;
Labys, P .
ECONOMETRICA, 2003, 71 (02) :579-625
[3]   Order flow, transaction clock, and normality of asset returns [J].
Ané, T ;
Geman, H .
JOURNAL OF FINANCE, 2000, 55 (05) :2259-2284
[4]  
Beran J., 1994, STAT LONG MEMORY PRO
[5]   Fluctuations and response in financial markets: the subtle nature of 'random' price changes [J].
Bouchaud, JP ;
Gefen, Y ;
Potters, M ;
Wyart, M .
QUANTITATIVE FINANCE, 2004, 4 (02) :176-190
[6]  
Breidt F, 1993, MODELING LONG MEMORY
[7]  
Burns ArthurF., 1947, MEASURING BUSINESS C
[8]   SUBORDINATED STOCHASTIC-PROCESS MODEL WITH FINITE VARIANCE FOR SPECULATIVE PRICES [J].
CLARK, PK .
ECONOMETRICA, 1973, 41 (01) :135-155
[9]  
DEO R, 2005, TRACING SOURCE LONG
[10]  
Ding Z., 1993, J EMPIR FINANC, V1, P83, DOI [DOI 10.1016/0927-5398(93)90006-D, 10.1016/0927-5398(93)90006-D]