Financial assets are volatile, and volatility becomes more intense in terms of size and rate of recurrence when markets are uncertain and growing rapidly. The fact that the recurrence rate increased during crisis periods, such as the IT bubble in the early 2000 and the global financial crisis that started in 2007, is a key finding in the literature. Estimating these results requires modeling a time series that can consider volatility clustering. However, the prominent model in finance and economics estimates that the average volatility increases when uncertainty increases. This modeling process needs to consider the asymmetry that financial assets and economic outcomes, such as gross domestic product (GDP) exhibit, which tend to fall drastically in a short period and increase steadily over a long period. To model these different behaviors, one must consider the asymmetric nature of the return, for example, when a stock has extremely low or extremely high returns in a day. 

To model this behavior, I used several methods in settings that could better explain what happens during market periods when there is higher uncertainty. The general finding is that correlations are higher when returns are in the lower quantiles, called the left tails. Thus, financial assets are positively correlated, especially during periods of increased uncertainty. It is not only clustering that one would try to explain, but another issue is the prediction of one asset’s effect on another. The effect of one asset on another asset is called the spillover effect. We tried to distinguish between events that happen during the same time that affect all assets. These events are called systematic risk, and the effects that one asset has on another asset is called systemic risk. Explaining the systemic risk typically has higher priority from a policy perspective, as systemic risk can be a driver for risk transmission from one asset to another, creating a chain of risk or a spiral of risk. Hence, the approaches I used can model that chain of risk and predict risk transmission while controlling for external factors that increase uncertainty. The results of this research show the connection between energy assets and renewable energy stocks in Papers 1 and 2. For instance, we found that there is a possibility of adjusting the European carbon emission cap and that renewable energy stocks positively correlate with energy commodities in the tails. Thus, renewable energy stocks follow a macroeconomic cycle. The findings of Paper 3 show the systemic and systematic nature of cross-country spillovers between emerging and developed financial markets, and that the spillover is time-varying with increasing spillovers in crisis periods. Paper 4 examines the Nordic banking sector. The results show that banks’ spillover to their local markets is due to their systemic importance and the strength of the spillover is related to the bank’s characteristics. In the final Paper, I studied the upside and downside movement asymmetry of stocks and found that betting on upside volatility is better than a portfolio perspective but comes at the cost of increased pricing errors. The empirical findings of this thesis significantly contribute to policymakers and institutional investors in portfolio diversification and risk management.