.png){kind=icon}
返回
Forecasting multifractal volatility
.png){kind=icon}
OA
AI
DOI：10.1016/S0304-4076(01)00069-0.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multifractal. The process captures the thick tails, volatility persistence, and moment scaling exhibited by many financial time series. It can be interpreted as a stochastic volatility model with multiple frequencies and a Markov latent state. We assume for simplicity that the forecaster knows the true generating process with certainty but only observes past returns. The challenge in this environment is long memory and the corresponding infinite dimension of the state space. We introduce a discretized version of the model that has a finite state space and an analytical solution to the conditioning problem. As the grid step size goes to zero, the discretized model weakly converges to the continuous-time process, implying the consistency of the density forecasts. (C) 2001 Published by Elsevier Science S.A.
Keyword:
forecasting
long memory
multiple frequencies
stochastic volatility
weak convergence
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
5.2K
被引数:
3.0W
机构
暂无机构信息
引用论文
分享收藏Altered Default Network Resting-State Functional Connectivity in Adolescents with Internet Gaming Addiction.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏 没有更多内容
没有更多内容