This dissertation studies issues related to digital currencies and monetary policy. In particular, it analyzes the determinants of the monetary policy of the European Central Bank (ECB) and examines design aspects of central bank digital currencies (CBDCs), e.g., related to financial stability, monetary policy, and privacy. Almost 15 years ago, the US bank Lehman Brothers went bankrupt. This incident marked the beginning of the global financial crisis — a crisis that shook the monetary system. Central banks worldwide introduced unprecedented monetary policy measures to stimulate inflation and economic activity. Since then, the environment for conducting monetary policy has changed due to the structural effects of the crisis. Assets prices, public debt, and the size of central banks’ balance sheets increased substantially and reached all their all-time highs in the euro area in 2021. Besides these economic effects, the global financial crisis was also the starting point of a monetary revolution. As a reaction to the crisis, Satoshi Nakamoto (Nakamoto, 2008) — a pseudonym whose identity remains unknown — initiated a novel decentralized payment system that operates without banks, central banks, and government interference: Bitcoin was born. Originating from Bitcoin, by December 2021, more than 10,000 such cryptocurrencies with a market capitalization of more than two trillion US dollars emerged (Coinmarketcap, 2021). As a consequence of these monetary innovations and the ongoing digitization of payments, central banks consider issuing own digital currencies, so-called CBDCs. CBDCs promise to combine the advantages of (physical) cash and (digital) bank deposits, e.g., cheap, convenient, and safe payments in the digital realm. However, they could also impair financial stability and undermine data privacy. As CBDCs have not been introduced in advanced economies yet, substantial theory-based analyses are required to study their benefits and risks adequately. Chapter 1 addresses the question, which factors influenced the ECB’s monetary policy in the period from 1999 to 2018. After identifying potential monetary policy determinants based on a literature review and textual analysis of the ECB’s public communication, we use an empirical Bayesian model averaging (BMA) approach to determine key variables that impacted the ECB’s monetary policy decisions before and after the global financial crisis. While in the literature, researchers typically select one model to study monetary policy determinants, using BMA allows accounting for uncertainty about the choice of the respective empirical model. This uncertainty arises, amongst others, from the heterogeneity in the ECB’s decision-making body, the Governing Council. Our analysis considers approximately 33,000 different empirical models that can be constructed from the identified potential relevant determinants. Our results suggest the following: First, in the time period analyzed, the ECB mainly focused on the inflation rate when setting interest rates. Second, economic activity indicators were in the focus of the ECB before the financial crisis. Third, over the last decade, the role of economic activity decreased, thereby supporting the hypothesis that inflation was the main driver of monetary policy decisions in the post-crisis period. This result is supported by findings from textual analysis. These results show that, in recent years, official ECB communication mentioned terms related to inflation more frequently than terms related to economic activity. Fourth, when setting interest rates, central bankers appeared to consider more than one model, favoring the use of averaging techniques for studying monetary policy determinants. Chapter 2 focuses on CBDCs. It studies the effects of CBDCs on the financial sector and monetary policy, and analyzes which measures the central bank can undertake to prevent destabilizing effects for the economy. While CBDCs might offer several benefits for their users, they potentially impose threats to the financial sector. They could disintermediate commercial banks and facilitate bank runs since CBDCs, in contrast to commercial bank money, constitute digital forms of central bank money with marginal risk. Thus, in times of crises, private agents could decide to convert substantial amounts of commercial bank money in CBDC, thereby posing a risk to banks’ liquidity. To analyze these concerns in the absence of any CBDC-specific empirical data, we develop a New Keynesian dynamic stochastic general equilibrium model and simulate a financial crisis in a world with and without CBDC. In particular, we compare the effects of remunerated (interest-bearing) and non-remunerated (non-interest-bearing) CBDCs. We find that CBDCs indeed crowd out bank deposits and negatively affect bank funding. However, this crowding-out effect can be mitigated if the central bank chooses to provide additional central bank funds or to disincentivize large-scale CBDC accumulation via low or potentially even negative interest rates. Thus, our results suggest that a CBDC does not necessarily impair the financial sector if the central bank chooses adequate design and policy measures. Chapter 3 studies the design of CBDCs in more detail and addresses the question of how a CBDC should be designed to ensure a high degree of data privacy while taking legal requirements into account. While physical cash allows anonymous transactions, i.e., transaction data is only observable for the two transaction parties involved and not for third parties, such as banks and central banks, CBDC data is generally recorded in a digital database. Opponents of CBDCs criticize that data access for the central bank could incentivize the misuse of CBDC payment data by public sector entities and could undermine citizens’ data privacy. In Chapter 3, utilizing a design science research approach, we propose a CBDC system that preserves citizens’ payment privacy. Despite being often perceived as a tradeoff, we show that it is feasible to provide anonymity for CBDC payments while also complying with regulations related to anti-money laundering and combating the financing of terrorism. Privacy and compliance are guaranteed by zero-knowledge proofs, cryptographic innovations.