Petroleum markets are undergoing rapid financialization and integration, leading to increased volatility and exposing participants to potentially much greater risks. This thesis addresses the explicit modeling of petroleum price volatility in a multivariate framework and analyzes the relative merits of multivariate models to describe change in the context of petroleum markets risk. The focus of this thesis will be on explaining the dynamic interdependencies in petroleum markets and further demonstrate whether the existence of such interdependencies prompt for the need to assess risk differently, by which this thesis contributes to the existing economic or econometric theories in three aspects. The first empirical part examines the importance of volatility spillovers and asymmetry in petroleum markets and their influence on optimal hedging strategy. To address in a realistic way the dynamic conditional correlation of petroleum spot and futures markets, we develop a new theoretical framework by accounting for the effect of time-varying conditional correlations in the conditional volatility processes of the VARMA-AGARCH model in what is termed the VARMA-AGARCH-DCC model. Results demonstrate that the proposed model is the best for OHR calculation in terms of the variance of portfolio reduction and tail risk analysis. The second empirical part, for the first time in the literature of energy economics, examines the volatility and correlation interdependence between oil market and China stock market at the sector-level. Results indicate that oil price fluctuations constitute a systematic asset price risk at the sector level and information content embedded in oil market volatility is an effective and valuable variable for constructing an optimal oil-stock holding. Finally, the third empirical part, for the first time in the literature of energy economics, investigates the volatility transmission mechanism among three benchmark oil markets and quantifies the size and persistence of these connections through employing the Volatility Impulse Response Function (VIRF) methodology. Results suggest markedly different responsiveness to historical events and volatility/correlation dynamics across crude oil benchmark markets. Overall, the findings of this thesis have important implications for crude oil market trading and risk management, as well as stock market investors, by providing valuable information on the oil price volatility dynamics and will help market participants develop efficient risk measurement schemes and devise sound risk management strategies.