Market Experiments with Multiple Assets: A Survey

2021 ◽  
Author(s):  
John Duffy ◽  
Jean Paul Rabanal ◽  
Olga Rud
Keyword(s):  
Multiple Assets ◽  
Market Experiments

A new bootstrap test for multiple assets joint risk testing

2017 ◽  
Vol 19 (4) ◽  
pp. 1-22 ◽  
Author(s):  
David Ardia ◽  
Lukasz Gatarek ◽  
Lennart Hoogerheide
Keyword(s):  
Bootstrap Test ◽  
Multiple Assets

scholarly journals Government Debt Management: The Long and the Short of It

2018 ◽  
Vol 86 (6) ◽  
pp. 2554-2604 ◽  
Author(s):  
Elisa Faraglia ◽  
Albert Marcet ◽  
Rigas Oikonomou ◽  
Andrew Scott
Keyword(s):  
Transaction Costs ◽  
Incomplete Markets ◽  
Dominant Role ◽  
Global Solutions ◽  
Debt Management ◽  
Computational Method ◽  
Complete Markets ◽  
Market Models ◽  
Multiple Assets ◽  
Over Time

Abstract Standard optimal Debt Management (DM) models prescribe a dominant role for long bonds and advocate against issuing short bonds. They require very large positions in order to complete markets and assume each period that governments repurchase all outstanding bonds and reissue (r/r) new ones. These features of DM are inconsistent with U.S. data. We introduce incomplete markets via small transaction costs which serves to make optimal DM more closely resemble the data : r/r are negligible, short bond issuance substantial and persistent and short and long bonds positively co-vary. Intuitively, long bonds help smooth taxes over states and short bonds over time. Solving incomplete market models with multiple assets is challenging so a further contribution of this article is introducing a novel computational method to find global solutions.

scholarly journals Relation-Aware Transformer for Portfolio Policy Learning

2020 ◽  
Author(s):  
Ke Xu ◽  
Yifan Zhang ◽  
Deheng Ye ◽  
Peilin Zhao ◽  
Mingkui Tan
Keyword(s):  
Portfolio Selection ◽  
State Of The Art ◽  
Asset Price ◽  
Sequential Patterns ◽  
Portfolio Decisions ◽  
Price Series ◽  
Crypto Currency ◽  
Key Issues ◽  
Multiple Assets ◽  
Portfolio Policy

Portfolio selection is an important yet challenging task in AI for FinTech. One of the key issues is how to represent the non-stationary price series of assets in a portfolio, which is important for portfolio decisions. The existing methods, however, fall short of capturing: 1) the complicated sequential patterns for asset price series and 2) the price correlations among multiple assets. In this paper, under a deep reinforcement learning paradigm for portfolio selection, we propose a novel Relation-aware Transformer (RAT) to handle these aspects. Specifically, being equipped with our newly developed attention modules, RAT is structurally innovated to capture both sequential patterns and asset correlations for portfolio selection. Based on the extracted sequential features, RAT is able to make profitable portfolio decisions regarding each asset via a newly devised leverage operation. Extensive experiments on real-world crypto-currency and stock datasets verify the state-of-the-art performance of RAT.

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