The industrial organization of execution, clearing and settlement in financial markets

The execution, clearing, and settlement of financial transactions are all subject to substantial scale and scope economies which make each of these complementary functions a natural monopoly. Integration of trade, execution, and settlement in an exchange improves efficiency by economizing on transactions costs. When scope economies in clearing are more extensive than those in execution, integration is more costly, and efficient organization involves a trade-off of scope economies and transactions costs. A properly organized clearing cooperative can eliminate double marginalization problems and exploit scope economies, but can result in opportunism and underinvestment. Moreover, a clearing cooperative may exercise market power. Vertical integration and tying can foreclose entry, but foreclosure can be efficient because market power rents attract excessive entry. Integration of trading and post-trade services is the modal form of organization in financial markets, which is consistent with the hypothesis that transactional efficiencies explain organizational arrangements in these markets

Densely Entangled Financial Systems

In [1] Zawadoski introduces a banking network model in which the asset and counter-party risks are treated separately and the banks hedge their assets risks by appropriate OTC contracts. In his model, each bank has only two counter-party neighbors, a bank fails due to the counter-party risk only if at least one of its two neighbors default, and such a counter-party risk is a low probability event. Informally, the author shows that the banks will hedge their asset risks by appropriate OTC contracts, and, though it may be socially optimal to insure against counter-party risk, in equilibrium banks will {\em not} choose to insure this low probability event. In this paper, we consider the above model for more general network topologies, namely when each node has exactly 2r counter-party neighbors for some integer r>0. We extend the analysis of [1] to show that as the number of counter-party neighbors increase the probability of counter-party risk also increases, and in particular the socially optimal solution becomes privately sustainable when each bank hedges its risk to at least n/2 banks, where n is the number of banks in the network, i.e., when 2r is at least n/2, banks not only hedge their asset risk but also hedge its counter-party risk.Comment: to appear in Network Models in Economics and Finance, V. Kalyagin, P. M. Pardalos and T. M. Rassias (editors), Springer Optimization and Its Applications series, Springer, 201

A model for hedging load and price risk in the Texas electricity market

Energy companies with commitments to meet customers’ daily electricity demands face the problem of hedging load and price risk. We propose a joint model for load and price dynamics, which is motivated by the goal of facilitating optimal hedging decisions, while also intuitively capturing the key features of the electricity market. Driven by three stochastic factors including the load process, our power price model allows for the calculation of closed-form pricing formulas for forwards and some options, products often used for hedging purposes. Making use of these results, we illustrate in a simple example the hedging benefit of these instruments, while also evaluating the performance of the model when fitted to the Texas electricity market

ADAPT: a price-stabilizing compliance policy for renewable energy certificates: the case of SREC markets

Currently most Renewable Energy Certificate (REC) markets are defined based on targets which create an artificial step demand function resembling a cliff. This target policy produces volatile prices which can make investing in renewables a risky proposition. In this paper, we propose an alternative policy called Adjustable Dynamic Assignment of Penalties and Targets (ADAPT) which uses a sloped compliance penalty and a self-regulating requirement schedule, both designed to stabilize REC prices, helping to alleviate a common weakness of environmental markets. To capture market behavior, we model the market as a stochastic dynamic programming problem to understand how the market might balance the decision to use a REC now versus holding it for future periods (in the face of uncertain new supply). Then, we present and prove some of the properties of this market, and finally we show that this mechanism reduces the volatility of REC prices which should stabilize the market and encourage long-term investment in renewables

A non-parametric structural hybrid modeling approach for electricity prices

We develop a stochastic model of zonal/regional electricity prices, designed to reflect information in fuel forward curves and aggregated capacity and load as well as zonal or regional price spreads. We use a nonparametric model of the supply stack that captures heat rates and fuel prices for all generators in the market operator territory, combined with an adjustment term to approximate congestion and other zone-specific behavior. The approach requires minimal calibration effort, is readily adaptable to changing market conditions and regulations, and retains sufficient tractability for the purpose of forward price calibration. The model is illustrated for the spot and forward electricity prices of the PS zone in the PJM market, and the set of time-dependent risk premiums are inferred and analyzed

SMART-SREC: a stochastic model of the New Jersey solar renewable energy certificate market

Markets for solar renewable energy certificates (SRECs) are gaining in promi- nence in many states, stimulating growth of the U.S. solar industry. However, SREC market prices have been extremely volatile, causing high risk to participants and potentially less investment in solar power generation. Such concerns necessitate the development of realis- tic, flexible and tractable models of SREC prices that capture the behavior of participants given the rules that govern the market. We propose an original stochastic model called SMART-SREC to fill this role, building on established ideas from the carbon pricing liter- ature, and including a feedback mechanism for generation response to prices. We calibrate the model to the New Jersey market and backtest it, analyzing parameter sensitivity and demonstrating its ability to reproduce historical dynamics. Finally, we run simulations to investigate the role and impact of regulatory parameters, thus providing insight into the crucial role played by market design