全球股票市场相互依赖、传染和长记忆性的小波分析
A wavelet analysis of inter-dependence, contagion and long memory among global equity markets
摘要：
本研究试图从时频的角度探讨全球股票市场的结构和特征。基于这一框架的分析可以从不同的维度捕捉信息，而不是传统的时域分析，其中金融市场的多尺度结构被清楚地提取出来。在金融时间序列中，由于存在多个时间范围，多尺度特征表现出来。多个时间范围的存在需要对每个时间范围分别进行仔细调查，因为不同时间范围的市场结构并不同质。多个时间范围的存在，具有不同程度的复杂性，需要一个调查金融时间序列从一个异构的市场的角度来看，市场参与者被认为是在不同的投资范围内运作。本文将基于时频的小波变换技术的应用扩展到：i）从不同投资者的角度分析全球股票市场的相互依赖性，特别关注印度股票市场，ii）调查金融危机对印度股票市场的传染效应，如果有的话，研究全球股票市场的分形和标度特性，并使用基于小波的长记忆方法分析印度股票市场的有效性。
英文摘要：
This study attempts to investigate into the structure and features of global equity markets from a time-frequency perspective. An analysis grounded on this framework allows one to capture information from a different dimension, as opposed to the traditional time domain analyses, where multiscale structures of financial markets are clearly extracted. In financial time series, multiscale features manifest themselves due to presence of multiple time horizons. The existence of multiple time horizons necessitates a careful investigation of each time horizon separately as market structures are not homogenous across different time horizons. The presence of multiple time horizons, with varying levels of complexity, requires one to investigate financial time series from a heterogeneous market perspective where market players are said to operate at different investment horizons. This thesis extends the application of time-frequency based wavelet techniques to: i) analyse the interdependence of global equity markets from a heterogeneous investor perspective with a special focus on the Indian stock market, ii) investigate the contagion effect, if any, of financial crises on Indian stock market, and iii) to study fractality and scaling properties of global equity markets and analyse the efficiency of Indian stock markets using wavelet based long memory methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
