Optimal Convergence Trading with Unobservable Pricing Errors
We study a dynamic portfolio optimization problem related to convergence trading, which is an investment strategy that exploits temporary mispricing by simultaneously buying relatively underpriced assets and selling short relatively overpriced ones with the expectation that their prices converge in the future. We build on the model of Liu and Timmermann (2013) and extend it by incorporating unobservable Markov-modulated pricing errors into the price dynamics of two co-integrated assets. We characterize the optimal portfolio strategies in full and partial information settings both under the assumption of unrestricted and beta-neutral strategies. By using the innovations approach, we provide the filtering equation that is essential for solving the optimization problem under partial information. Finally, in order to illustrate the model capabilities, we provide an example with a two-state Markov chain.
Code (0)
등록된 구현이 없습니다.
Tasks
Portfolio OptimizationSimilar Papers 제목 키워드 기반
Dynamic Monopoly Pricing With Multiple Varieties: Trading Up
This paper studies dynamic monopoly pricing for a class of settings that includes multiple durable, multiple rental, or a mix of varieties. We show that the driving force behind pricing dynamics is the seller's incentive…
On Optimal Pricing Model for Multiple Dealers in a Competitive Market
In this paper, the optimal pricing strategy in Avellande-Stoikov's for a monopolistic dealer is extended to a general situation where multiple dealers are present in a competitive market. The dealers' trading intensities…
Fully Decentralized Peer-to-Peer Community Grid with Dynamic and Congestion Pricing
Peer-to-peer (P2P) electricity markets enable prosumers to minimize their costs, which has been extensively studied in recent research. However, there are several challenges with P2P trading when physical network constra…
Expert Opinions and Logarithmic Utility Maximization for Multivariate Stock Returns with Gaussian Drift
This paper investigates optimal trading strategies in a financial market with multidimensional stock returns where the drift is an unobservable multivariate Ornstein-Uhlenbeck process. Information about the drift is obta…
Online Prompt Pricing based on Combinatorial Multi-Armed Bandit and Hierarchical Stackelberg Game
Generation models have shown promising performance in various tasks, making trading around machine learning models possible. In this paper, we aim at a novel prompt trading scenario, prompt bundle trading (PBT) system, a…