Insurance and financial products, companies and markets are highly complex. An understanding of the inherent upside and downside risk requires suitable tools for a detailed analysis. In addition, several crises in the history of the financial system have shown that powerful regulatory frameworks are indispensable in order to guarantee that products, firms and markets provide benefits to the society. These issues are the focus of this thesis.Classical monetary risk measures are functionals that quantify the downside risk of positions. They facilitate a better understanding of the risks in products, companies and markets, and they are an important basis for regulation – in particular in the context of capital requirements. Risk measures have been studied intensively over the past twenty years. The present thesis focuses on the following aspects:• From a practical point of view, the implementation of risk measures in the context of Monte Carlo simulations is an important issue; for a certain class of risk measures, we design and evaluate their efficient estimation via a stochastic root finding algorithm.• The thesis contributes to the development of risk measures and the evaluation of their merits and disadvantages. Classical risk measures typically evaluate exogenous positions. We investigate feedback from trading and price impact and suggest suitable liquidity-adjusted risk measures. We also consider risk measurement in networks of firms and investigate the issues of optimal capital allocation and optimal risk sharing between entities within a network. We find that firms may hide a substantial portion of their downside risk if they use V@R-based risk measures as a basis for their capital requirements.• We investigate the impact of insurance premium taxation. This tax on many insurance products differs from the standard tax scheme: the value-added tax.• Finally, we focus on a specific functional of the upside and downside risk, the market consistent embedded value and its components within an asset-liability management model; this requires a combination of different valuation approaches and an integration of actuarial and financial perspectives.