Given the potentially severe financial consequences due to climate change, understanding how climate risks contribute to firms’ credit risk is essential. Building on a Merton-type model, we propose a new model that introduces a random growth adjustment factor in the firm value dynamics to reflect the depreciation due to climate risks. We also review the current state of the literature on how structural models of credit risk are employed to model the impact of climate risk on financial markets. Motivated by the theoretical models, we utilize the information contained in the spreads of Credit Default Swap (CDS) contracts to construct a market-implied, forward-looking carbon risk (CR) factor. We examine empirically how the scope and speed of economic transformation vary across jurisdictions, sectors, and over time. Explicit carbon emission pricing enables lenders to sharpen their assessments. The breadth of the regulation intensifies financial repercussions from carbon risk. The impact differs significantly across industries, indicating that the market identifies which sectors are better poised for a transition to a low-carbon economy. Lenders expect that adjustments in carbon regulations in Europe will cause relatively higher policy-related costs in the near future.

This survey article reviews the current state of literature on how structural models of credit risk are employed to model the impact of climate risk on financial markets. We discuss how the two prominent types of climate risk, physical and transition risk, are captured by the seminal Merton model and its well-known extensions. Theoretical and practical advantages and drawbacks are worked out and an outlook on possible model improvements is provided.

Using Credit Default Swap spreads, we construct a forward-looking, market-implied carbon risk factor and show that carbon risk affects firms’ credit spread. The effect is larger for European than North American firms and varies substantially across industries, suggesting the market recognises where and which sectors are better positioned for a transition to a low-carbon economy. Moreover, lenders demand more credit protection for those borrowers perceived to be more exposed to carbon risk when market-wide concern about climate change risk is elevated. Finally, lenders expect that adjustments in carbon regulations in Europe will cause relatively larger policy-related costs in the near future.

We examine if the trading activity on the German intraday electricity market is linked to fundamental as well as market-induced factors. Thus, we propose a novel point process model in which the intensity process of order arrivals consists of a self-exciting term and additional exogenous factors, such as the production of renewable en- ergy or the activated volume on the balancing market. The model parameters are estimated by a maximum like- lihood approach that explicitly accounts for such factor processes. By comparing the proposed model to several nested models, we investigate whether adding the exogenous factors significantly increases the accuracy of the model fit. We find that intensity processes that only take into account exogenous factors are improved if we add a self-exciting term. On the other hand, to capture the market dynamics correctly, pure self-exciting models need to be extended such that they additionally account for exogenous impacts.

We use point processes to analyze market order arrivals on the intraday market for hourly electricity deliveries in Germany in the second quarter of 2015. As we distinguish between buys and sells, we work in a multivariate setting. We model the arrivals with a Hawkes process whose baseline intensity comprises either only an exponentially increasing component or a constant in addition to the exponentially increasing component, and whose excitation decays exponentially. Our goodness-of-fit tests indicate that the models where the intensity of each market order type is excited at least by events of the same type are the most promising ones. Based on the Akaike information criterion, the model without a constant in the baseline intensity and only self-excitation is selected in almost 50% of the cases on both market sides. The typical jump size of intensities in case of the arrival of a market order of the same type is quite large, yet rather short lived. Diurnal patterns in the parameters of the baseline intensity and the branching ratio of self-excitation are observable. Contemporaneous relationships between different parameters such as the jump size and decay rate of self and cross-excitation are found.

Existing research indicates that on the intraday market for power deliveries in Germany market orders tend to arrive in clusters. To capture such clustering, point processes with an intensity depending on past events, so-called Hawkes processes, appear to be promising. We consider the question whether there is a temporal structure prevalent in the parameters of Hawkes processes estimated for adjacent delivery hours. First we model a diurnal seasonality pattern found in the data and provide an economic intepretation for it. For the remaining decomposed series, we then propose simple (vector) autoregressive models to describe the serial structure. To evaluate our model we conduct a forecasting study. Testing against a benchmark model and a model without any serial structure, we find evidence for our proposed model. Our study reveals that capturing the serial structure in the parameters proves to be useful in understanding the underlying market microstructure.

Emissions Trading Systems (ETSs) with fixed caps lack provisions to address systematic imbalances in the supply and demand of permits due to changes in the state of the regulated economy. We propose a mechanism which adjusts the allocation of permits based on the current bank of permits. The mechanism spans the spectrum between a pure quantity instrument and a pure price instrument. We solve the firms' emissions control problem and obtain an explicit dependency between the key policy stringency parameter – the adjustment rate – and the firms' abatement and trading strategies. We present an analytical tool for selecting the optimal adjustment rate under both risk-neutrality and risk-aversion, which provides an analytical basis for the regulator's choice of a responsive ETS policy.

Paper available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2114177

Paper available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2114196

Working Paper, available at ssrn.com/abstract=2065723

Paper available at:

www.sciencedirect.com/science/article/pii/S0095069610001051

Paper available at:

papers.ssrn.com/sol3/papers.cfm

Paper available at:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1546105

Working Paper available at:

papers.ssrn.com/sol3/papers.cfm

Paper available at:

http://web.mit.edu/ceepr/www/publications/workingpapers/2009-018.pdf