Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization
We propose a new method for finding statistical arbitrages that can contain more assets than just the traditional pair. We formulate the problem as seeking a portfolio with the highest volatility, subject to its price remaining in a band and a leverage limit. This optimization problem is not convex, but can be approximately solved using the convex-concave procedure, a specific sequential convex programming method. We show how the method generalizes to finding moving-band statistical arbitrages, where the price band midpoint varies over time.
Code (1)
Similar Papers 제목 키워드 기반
A Markowitz Approach to Managing a Dynamic Basket of Moving-Band Statistical Arbitrages
We consider the problem of managing a portfolio of moving-band statistical arbitrages (MBSAs), inspired by the Markowitz optimization framework. We show how to manage a dynamic basket of MBSAs, and illustrate the method …
Cyclic Arbitrage in Decentralized Exchanges
Decentralized Exchanges (DEXes) enable users to create markets for exchanging any pair of cryptocurrencies. The direct exchange rate of two tokens may not match the cross-exchange rate in the market, and such price discr…
Maximum Implied Variance Slope -- Practical Aspects
In the Black-Scholes model, the absence of arbitrages imposes necessary constraints on the slope of the implied variance in terms of log-moneyness, asymptotically for large log-moneyness. The constraints are used for exa…
EVaR-Optimal Arm Identification in Bandits
We study the fixed-confidence best arm identification (BAI) problem within the multi-armed bandit (MAB) framework under the Entropic Value-at-Risk (EVaR) criterion. Our analysis considers a nonparametric setting, allowin…
Bandit Convex Optimization: Towards Tight Bounds
Bandit Convex Optimization (BCO) is a fundamental framework for decision making under uncertainty, which generalizes many problems from the realm of online and statistical learning. While the special case of linear cost …
Decision MakingDecision Making Under UncertaintyOpen-Ended Question Answering