论文标题
全球股票市场之间相互依存，传染和长期记忆的小波分析
A wavelet analysis of inter-dependence, contagion and long memory among global equity markets
论文作者
论文摘要
这项研究试图从时频的角度研究全球股票市场的结构和特征。基于此框架的分析使人们可以从不同的维度捕获信息，而不是传统的时间域分析，在这种分析中，金融市场的多尺度结构清楚地提取了。在财务时间序列中，多尺度特征由于存在多个时间范围而表现出来。由于市场结构在不同的时间范围内不是同质的，因此，多个时间范围的存在需要仔细研究每次视野。多个时间范围的存在，其复杂程度不同，需要一个人从异质市场的角度调查财务时间序列，据说市场参与者可以在不同的投资视野中运作。本论文将基于时频的小波技术的应用扩展到：i）分析全球股票市场的相互依存关系，从异质投资者的角度进行特殊关注的印度股票市场，ii）调查基于印度股票市场的竞争效应，以及研究全球股票的效率，并分析全球股票的效率，并研究各种财务疾病的兴趣效应，并研究全球股票的效率。 方法。
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.
