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Using a novel regulatory dataset of fully identified derivatives transactions, this paper provides the first comprehensive analysis of the structure of the euro area interest rate swap (IRS) market after the start of the mandatory clearing obligation. Our dataset contains 1.7 million bilateral IRS transactions of banks and non-banks. Our key results are as follows:
1) The euro area IRS market is highly standardised and concentrated around the group of the G16 Dealers but also around a significant group of core “intermediaries"(and major CCPs).
2) Banks are active in all segments of the IRS euro market, whereas non-banks are often specialised.
3) When using relative net exposures as a proxy for the “flow of risk" in the IRS market, we find that risk absorption takes place in the core as well as the periphery of the network but in absolute terms the risk absorption is largely at the core.
4) Among the Basel III capital and liquidity ratios, the leverage ratio plays a key role in determining a bank's IRS trading activity.
When a spot market monopolist participates in a derivatives market, she has an incentive to deviate from the spot market monopoly optimum to make her derivatives market position more profitable. When contracts can only be written contingent on the spot price, a risk-averse monopolist chooses to participate in the derivatives market to hedge her risk, and she reduces expected profits by doing so. However, eliminating all risk is impossible. These results are independent of the shape of the demand function, the distribution of demand shocks, the nature of preferences or the set of derivatives contracts.
We take a simple time-series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time-series modeling reveals conditional mean dynamics, and crucially, strong conditional variance dynamics, in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. As we argue, it also holds promise for producing the long-horizon predictive densities crucial for pricing weather derivatives, so that additional inquiry into time-series weather forecasting methods will likely prove useful in weather derivatives contexts.