Algorithmic market making in dealer markets with hedging and market impact

In dealer markets, dealers provide prices at which they agree to buy and sell the assets and securities they have in their scope. With ever increasing trading volume, this quoting task has to be done algorithmically in most markets such as foreign exchange (FX) markets or corporate bond markets. Over the last 10 years, many mathematical models have been designed that can be the basis of quoting algorithms in dealer markets. Nevertheless, in most (if not all) models, the dealer is a pure internalizer, setting quotes and waiting for clients. However, on many dealer markets, dealers also have access to an interdealer market or even public trading venues where they can hedge part of their inventory. In this paper, we propose a model taking this possibility into account therefore allowing dealers to externalize part of their risk. The model displays an important feature well known to practitioners that within a certain inventory range, the dealer internalizes the flow by appropriately adjusting the quotes and starts externalizing outside of that range. The larger the franchise, the wider is the inventory range suitable for pure internalization. The model is illustrated numerically with realistic parameters for USDCNH spot market.     

Compromising as an equal loss principle

Review of Economic Design - A social choice rule aggregates the preferences of a group of individuals over a set of alternatives into a collective choice. The literature admits several social...     

Health subsidies,prevention and welfare

People value healthy ageing but may underinvest in health-improving preventive care. This arises when they ignore the beneficial effects of healthy ageing on public health expenditures and hence on the tax burden of future generations. This health externality justifies public intervention. We build an overlapping generations model with a government subsidizing investment in health by the young generation and paying the health care costs of the old generation. We find that the welfare-maximizing subsidy rate depends positively on the health externality and the size of health care costs, and negatively on the discount factor. The subsidy rate should therefore be high when prevention is cost-effective and when the population is careless about the future. Moreover, the welfare-maximizing subsidy rate is lower than the health-maximizing rate but higher than the capital-maximizing rate. This underlines the trade-off for a policy maker between health and economy.