Markets constitute a highly dynamically evolving universe, which undergoes through distinct time periods and reacts in diverse conditions and phenomena. Accurate quantification of the integration strength between dynamically evolving markets, as well as their inherent risk, has become a major issue in the context of the recent financial predicament, with the typical approaches relying mainly on the time-varying aspects of market indices. Despite its recognized virtue, incorporation of both temporal and frequency information has still gained limited attention in the framework of market integration, whereas it is more popular in the field of risk quantification. To highlight the importance of accounting for both temporal and frequency variabilities towards designing efficient and robust market integration and risk quantification strategies, this presentation will elaborate on two distinct problems: (1) the design of a novel measure, which better adapts to the time-frequency content of market indices for quantifying the degree of their integration. To this end, advanced statistical signal processing techniques are employed to extract market interrelations not only across time, but also across frequency, thus distinguishing between short and long-term investors; (2) the design of a novel risk quantification framework exploiting the time-evolving energy distribution of returns. Specifically, a time-scale decomposition is applied first on the returns series, followed by a nonlinear combination of the optimal subset of time resolutions for estimating risk at a given trading horizon. Most importantly, our proposed energy-based method can be coupled with the commonly used, quantile-based, risk measures to enhance their performance. Regarding the first problem, the experimental results reveal an improved performance against alternative market integration measures, in terms of typical financial performance metrics. On the other hand, concerning the second problem, our experimental evaluation demonstrates an increased robustness of our proposed method in efficiently controlling under- or over-estimated risk values.