This paper examines the time-varying conditional correlations between seventeen metal future markets and Malaysian Islāmic bonds. We apply twelve six variate dynamic conditional correlation (DCC) FIGARCH models in order to capture potential contagion effects between the markets for the period...

In this study, we provide a simple one period mean-field-games setting for the joint optimal trading problem for electricity producers at the electricity markets. Based on the Markowitz mean-variance approach from stock trading, we consider a decision problem of an electricity provider when...

Investors sometimes have strong convictions that a distinctive economic regime will prevail in the period ahead and therefore would like to form a portfolio that reflects the expected returns, standard deviations, and correlations of assets during such a regime. To do so, they typically isolate...

In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transformer (GPT) models is a challenge due to model "hallucinations", necessitating careful verification and validation...

Exchanges, including Nasdaq’s Extended Trading Close introduced in 2022, have implemented the after-hour fixed-price trading (hereinafter referred to as the fixed-price session) to enhance liquidity. Although the fixed trading price eliminates temporary price impact, execution risk remains....

We propose a price impact model where changes in prices are purely driven by the order flow in the market. The stochastic price impact of market orders and the arrival rates of limit and market orders are functions of the market liquidity process which reflects the balance of the demand and...

Time horizon dimensions are added to asset pricing theory. Single period, static, arbitrage pricing theory (APT) describes single period risk with long horizon contributions in the frequency domain. Mean-reversion risks correspond to horizon variances. Mean-reversion risk is measured using the...

We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL...

We propose a class of execution algorithms that consists of a strategic layer and a speculative layer. The strategic layer is an optimal trading schedule that encodes the trader's objective, her tolerance to risk, and the impact of her own trades in the market. The schedule of the strategic...

In this article, we examine the practice of evaluating trading and portfolio performance using accounting P&L, which values positions based on current market prices. We demonstrate that this method is biased due to the price impact from trading, and can give the impression of profits when there...