In this paper, we contribute to the literature on international stock market comovement. The novelty of our approach lies in usage of wavelet tools to high-frequency financial market data, which allows us to understand the relationship between stock market returns in a different way. Major part...

Momentum trading strategies are thoroughly described in the academic literature and used in many trading strategies by hedge funds, asset managers, and proprietary traders. Baz et al. (2015) describe a momentum strategy for different asset classes in great detail from a practitioner's point of...

This paper examines the relationship between Indian equity prices and other developed markets, in the time-scale domain, using wavelets based multiscale analysis and cross wavelet analysis. Stock markets are analyzed at different levels of resolution which makes it possible to perform a scale by...

The analysis of co-movements of stock market returns is a fundamental issue in finance. The aim of this paper is to examine the co-movement between Germany and major International Stock Markets in the time-frequency space. Our sample period goes from 01 June 1992 to 26 March 2013 and includes...

In the context of globalization, through a growing process of market liberalization, advanced technology and economic trading bloc, national stock markets have become more interdependent, which limits the international portfolio diversification opportunities. This paper investigates the degree...

The world has become more interconnected over the past few decades. Against this backdrop, economic and financial contagion following adverse shocks can have a severe impact on the global economy. How systemic can the effects of contagion be? What specific transmission channels are involved?...

This study examines the multi-scale impact of financial, newspaper, and Twitter-based uncertainty on bank stock returns in the United States. By combining Principal Component Analysis and Discrete Wavelet Transformation, we estimate directional spillover and causality for different time...

We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870–2016. Machine learning models mostly outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of...

To avert the impending global Cyber-Finance Insurance Crisis based upon large-scale commercial reliance upon quantitative models with inherent model risks, tail risks, and systemic risks in current form, this post-doctoral thesis makes the following key contributions: Develops the first known...