3,123 research outputs found
Facilitation and Internalization Optimal Strategy in a Multilateral Trading Context
This paper studies four trading algorithms of a professional trader at a
multilateral trading facility, observing a realistic two-sided limit order book
whose dynamics are driven by the order book events. The identity of the trader
can be either internalizing or regular, either a hedge fund or a brokery
agency. The speed and cost of trading can be balanced by properly choosing
active strategies on the displayed orders in the book and passive strategies on
the hidden orders within the spread. We shall show that the price switching
algorithms provide lower and upper bounds of the mixed trading algorithms.
Especially, when the internalization premium is zero, an internalizing trader's
optimal mixed trading strategy can be achieved among the set of price switching
strategies. For both an internalizing trader and a regular trader, the optimal
price switching strategy exists and is expressed in terms of the value
function. A parallelizable algorithm to numerically compute the value function
and optimal price switching strategy for the discretized state process is
provided.Comment: 40 pages; 7 figures; 1 tabl
The Thick Market Effect on Local Unemployment Rate Fluctuations
This paper studies how the thick market effect influences local unemployment rate fluctuations. The paper presents a model to demonstrate that the average matching quality improves as the number of workers and firms increases. Unemployed workers accumulate in a city until the local labor market reaches a critical minimum size, which leads to cyclical fluctuations in the local unemployment rates. Since larger cities attain the critical market size more frequently, they have shorter unemployment cycles, lower peak unemployment rates, and lower mean unemployment rates. Our empirical tests are consisten with the predictions of the model. In particular, we find that an increase of two standard deviations in city size shortens the unemployment cycles by about 0.72 months, lowers the peak unemployment rates by 0.33 percentage points, and lowers the mean unemployment rates by 0.16 percentage points.
Information, no-arbitrage and completeness for asset price models with a change point
We consider a general class of continuous asset price models where the drift
and the volatility functions, as well as the driving Brownian motions, change
at a random time . Under minimal assumptions on the random time and on
the driving Brownian motions, we study the behavior of the model in all the
filtrations which naturally arise in this setting, establishing martingale
representation results and characterizing the validity of the NA1 and NFLVR
no-arbitrage conditions.Comment: 21 page
Providing Efficient Privacy-Aware Incentives for Mobile Sensing
Abstract—Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively promote user participation, both incentive and privacy issues should be addressed. Existing work on privacy-aware incentive is limited to special scenario of mobile sensing where each sensing task needs only one data report from each user, and thus not appropriate for generic scenarios in which sensing tasks may require multiple reports from each user (e.g., in environmental monitoring applications). In this paper, we propose a privacy-aware incentive scheme for general mobile sensing, which allows each sensing task to collect one or multiple reports from each user as needed. Besides being more flexible in task management, our scheme has much lower computation and communication cost compared to the existing solution. Evaluations show that, when each node only contributes data for a small fraction of sensing tasks (e.g, due to the incapability or disqualification to generate sensing data for other tasks), our scheme runs at least one order of magnitude faster. I
A low-complexity iterative channel estimation and detection technique for doubly selective channels
In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key is a segment-by-segment frequency domain equalization (FDE) strategy under the assumption that channel is approximately static within a short segment. Guard gaps (for cyclic prefixing or zero padding) are not required between adjacent segments, which avoids the power and spectral overheads due to the use of cyclic prefix (CP) in the conventional FDE technique. A low-complexity bi-directional channel estimation algorithm is also developed to exploit correlation information of time-varying channels. Simulation results are provided to demonstrate the efficiency of the proposed algorithms. © 2008 IEEE
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