||Proper understanding of the dependence between assets is a crucial ingredient for a number of portfolio and risk management tasks. While the research in this area has been lively for decades, the recent financial crisis of 2007-2008 reminded us that we might not understand the dependence properly. This crisis served as catalyst for boosting the demand for models capturing the dependence structures. Reminded by this urgent call, literature is responding by moving to nonlinear dependence models resembling the dependence structures observed in the data. In my dissertation, I contribute to this surge with three papers in financial econometrics, focusing on nonlinear dependence in financial time series from different perspectives. I propose a new empirical model which allows capturing and forecasting the conditional time-varying joint distribution of the oil – stocks pair accurately. Employing a recently proposed conditional diversification benefits measure that considers higher-order moments and nonlinear dependence from tail events, I document decreasing benefits from diversification over the past ten years. The diversification benefits implied by my empirical model are, moreover, strongly varied over time. These findings have important implications for asset allocation, as the benefits of including oil in stock portfolios may not be as large as perceived. Further, I investigate the dependence structure in financial time series using quantile regression framework. I model conditional quantiles of returns using nonlinear quantile regression models based on copula functions. I explore further non-linearities in the data, and propose to use realized measures in the nonlinear quantile regression framework to explain and forecast conditional quantiles of financial returns. The nonlinear quantile regression models are implied by copula specifications and allow us to capture possible nonlinearities, and asymmetries in conditional quantiles of financial returns. Using high frequency data covering most liquid U.S. stocks in seven sectors, I provide ample evidence of asymmetric conditional dependence and different level of dependence characteristic for each industry. Finally, I consider conditional Value-at-Risk estimation under copula quantile regression models. I follow a slightly different approach compared to the current literature, where in the focus is systemic risk, and estimate the risk contribution that an asset has on some other individual asset. This approach allows the study of risk spillovers among assets. The dataset which I use for the model is the same as the one in Chapter 3. I find different risk spillover levels for distinctive industries. Furthermore, in some cases the risk spillover levels within the assets of the same industry are very different. These findings have great potential on portfolio re-balancing policies under stress events.