標題: | Empirical scaling laws and the aggregation of non-stationary data |
作者: | Chang, Lo-Bin Geman, Stuart 應用數學系 Department of Applied Mathematics |
關鍵字: | Random-walk models;Self-similarity;Stochastic volatility;Market time |
公開日期: | 15-十月-2013 |
摘要: | Widely cited evidence for scaling (self-similarity) of the returns of stocks and other securities is inconsistent with virtually all currently-used models for price movements. In particular, state-of-the-art models provide for ubiquitous, irregular, and often times high-frequency fluctuations in volatility ("stochastic volatility"), both intraday and across the days, weeks, and years over which data is aggregated in demonstrations of self-similarity of returns. Stochastic volatility renders these models, which are based on variants and generalizations of random walks, incompatible with self-similarity. We show here that empirical evidence for self-similarity does not actually contradict the analytic lack of self-similarity in these models. The resolution of the mismatch between models and data can be traced to a statistical consequence of aggregating large amounts of non-stationary data. (C) 2013 The Authors. Published by Elsevier B.V. All rights reserved. |
URI: | http://dx.doi.org/10.1016/j.physa.2013.06.049 http://hdl.handle.net/11536/22680 |
ISSN: | 0378-4371 |
DOI: | 10.1016/j.physa.2013.06.049 |
期刊: | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS |
Volume: | 392 |
Issue: | 20 |
起始頁: | 5046 |
結束頁: | 5052 |
顯示於類別: | 期刊論文 |