FOR 5583 - TP3: Regime switches and optimal asset allocation in climate finance and insurance

Regulatory measures shape investment opportunities in financial markets. Regulatory uncertainty can arise from changes in regulatory rules which occur at discrete points in time, making regime switching (RS) models a natural candidate for the analysis. Examples of such regime switches are the taxation of energy according to its carbon content (carbon tax), environmental, social, and governance (ESG) ratings, or the introduction of new regulatory rules in banking and insurance. Although the application of RS models in the context of asset allocation problems has been widely analyzed in the literature, the specific use of RS models to analyze the implications of regulatory uncertainty for both asset allocation and the efficiency of the objective of the regulator in climate finance and insurance is scarce.

Bubbles in financial markets

Agricultural Commodity Markets

The Chair of Monetary Economics has recently started to focus on price dynamics in agricultural markets. The chair publishes and holds talks to empirical results concerning speculative bubbles, price discovery and the relevance of financial investors in agricultural commodity markets. International cooperations are used to investigate markets that have been neglected in scientific studies, such as European, Brazilian and New Zealand futures markets. Although these markets are relatively young, their importance for producers, processors and financial investors is growing. The specific characteristics of the price dynamics in particular challenge the application of empirical methods. A further core of the research is the transfer of the empirical results to practical use.

FOR 5583 - TP4: Measuring and modelling climate policy uncertainty

Transitioning to a decarbonized economy is at the top of political agendas worldwide. Countries introduce new regulations to fight climate change risk. lmportant examples are market-based instruments such as cap-and-trade systems for greenhouse gases, like the European Union Emission Trading System. Prices in these markets are driven not only by underlying fundamentals but also in a non-trivial way by policy decisions. However, there is huge uncertainty not only with regard to the climate change and the economic consequences thereof (climate risk), but also with regard to the regulatory rules and the timing of such policies (climate policy uncertainty). n this context, however, existing pricing methods mostly abstract from the induced policy uncertainty.

FOR 5583 - TP2: Identification, measurement and pricing of spillover risks and uncertainty in networks

Shocks that hit one firm in the economy usually also effect other firms e.g. via customer-supplier relations or joint ownership. This also holds true for regulatory measures targeted at some sectors like tariffs, CO2 taxes or emission limits. Regulatory uncertainty arising in one part of the economy will thus propagate to other parts, too. Network models of the economy account for these relations. In these models, the firms are the nodes of the network, and the input-output-linkages between the firms define the links between the nodes. Shocks in the network propagate along these links. We want to use these network models of the economy to study the implications of regulatory measures and regulatory uncertainty on asset prices.

Professurvertretung von Prof. Dr. Nicole Branger