We study misaligned prices for logically related contracts in prediction markets. First, we uncover persistent arbitrage opportunities for risk-neutral investors between identical contracts on different exchanges. Examining the impact of several thousand dollars of transactions on the exchanges...

Understanding the microstructure of the financial market requires the processing of a vast amount of data related to individual trades, and sometimes even multiple levels of quotes. Analyzing such a large volume of data requires tremendous computing power that is not easily available to...

-

This paper proposes novel lattice algorithms to compute tail conditional expectation of European calls and puts in linear time. We incorporate the technique of prefix-sum into tilting, trinomial, and extrapolation algorithms as well as some syntheses of these algorithms. Furthermore, we...

We study misaligned prices for logically related contracts in prediction markets. First, we uncover persistent arbitrage opportunities for risk-neutral investors between identical contracts on different exchanges. Examining the impact of several thousand dollars of transactions on the exchanges...

We introduce Stochastic Flow Diagrams (SFDs), a new mathematical approach to represent complex dynamic systems into a single weighted digraph. This topological representation provides a way to visualize what otherwise would be a morass of equations in differences. SFDs model the propagation and...

A large portion of Macroeconomic and Financial research is built upon classical applications of Linear Algebra (such as regression analysis) and Stochastic Calculus (such as valuation models). As a result, most Macroeconomic and Financial research has inherited a focus on geometric locations...

This work’s main purpose is to understand the price dynamics in a generic limit order market, and illustrate a dynamical trading mechanism that can be applied to explore its market microstructure. First and foremost, we capture the iterative nature of the limit order market, and quantitatively...

We study the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping the optimal portfolio selection problem into a generalized eigenvalue problem, we propose a new optimization approach based on the use of simulated annealing. This new...

-