STRATEGIC INTERACTIONS AND NEGATIVE OIL PRICES

This paper argues on theoretical grounds that the negative oil prices event on April 20, 2020, was mainly due to the strategic interactions among some active traders on both sides of the futures contract. We present a three-player game of futures trading in which a continuum range of negative price can be supported as (strong) Nash equilibrium, yet none of those constitutes an [Formula: see text]-equilibrium originally developed by Ma (2009). We further propose the notion of coalition-with-side-payment as a solution concept for the environment where strategic interactions and transfer payments among players are allowed. Our model captures the mechanism underlying futures price manipulation, and its predictions largely agree with the observations on that day, which are beyond the scope of demand–supply and physical delivery narratives.

Deep Learning Models for Predicting Monthly TAIEX to Support Making Decisions in Index Futures Trading

Futures markets offer investors many attractive advantages, including high leverage, high liquidity, fair, and fast returns. Highly leveraged positions and big contract sizes, on the other hand, expose investors to the risk of massive losses from even minor market changes. Among the numerous stock market forecasting tools, deep learning has recently emerged as a favorite tool in the research community. This study presents an approach for applying deep learning models to predict the monthly average of the Taiwan Capitalization Weighted Stock Index (TAIEX) to support decision-making in trading Mini-TAIEX futures (MTX). We inspected many global financial and economic factors to find the most valuable predictor variables for the TAIEX, and we examined three different deep learning architectures for building prediction models. A simulation on trading MTX was then performed with a simple trading strategy and two different stop-loss strategies to show the effectiveness of the models. We found that the Temporal Convolutional Network (TCN) performed better than other models, including the two baselines, i.e., linear regression and extreme gradient boosting. Moreover, stop-loss strategies are necessary, and a simple one could be sufficient to reduce a severe loss effectively.

URGENSI PENGATURAN UNDANG-UNDANG PASAR FISIK ASET KRIPTO (CRYPTO ASSET)

Crypto Assets is a new alternative investment concept in Indonesia. The legal basis for regulating crypto assets currently in force in Indonesia cannot accommodate the development of the Crypto assets concept which continues to undergo significant changes. The physical market for crypto assets is incompatible when regulated by the provisions of Law Number 32 of 1997 on commodity futures trading and its amendments, namely Law Number 10 of 2011 because the physical market has conceptual differences with the provisions of the futures market in general. The object traded in the physical market is the commodity, while in the commodity futures market the object is futures contracts (and their derivatives) for commodities traded in the physical market. The scope of the commodity futures market as regulated in Article 1 of the Commodity Futures Trading Law does not accommodate commodity trading in the physical market. The urgency of regulating the physical market for crypto assets with a separate law is the implementation of the principle of legal certainty and protection of crypto asset investors. The method used in writing this journal is normative research using books, journal references, and laws and regulations that are relevant to the legal issues in this study. The results of this study indicate that the regulation of the physical law on crypto assets is needed because crypto assets should be regulated into two separate arrangements so that it is not appropriate if the regulation regarding crypto assets is only accommodated by the Commodity Futures Trading Law.

Outsourcing Compliance Functions and Fraud Detection

This chapter addresses the compliance function for hedge funds and considers areas where violations can occur. It surveys regulation in place in the US and internationally, looking in particular at the Securities and Exchange Commission and the Commodity Futures Trading Commission, and highlights key issues. Primarily, the chapter focuses on the outsourced nature of many of the agents responsible for providing assurance of the honesty of hedge fund operations, including trading, and reporting, registration, and discussing how compliance is made effective. The chapter concludes by considering the risks that require further research.

OPTIMAL DYNAMIC FUTURES PORTFOLIO UNDER A MULTIFACTOR GAUSSIAN FRAMEWORK

We study the problem of dynamically trading futures in continuous time under a multifactor Gaussian framework. We present a utility maximization approach to determine the optimal futures trading strategy. This leads to the explicit solution to the Hamilton–Jacobi–Bellman (HJB) equations. We apply our stochastic framework to two-factor models, namely, the Schwartz model and Central Tendency Ornstein–Uhlenbeck (CTOU) model. We also develop a multiscale CTOU model, which has a fast mean-reverting and a slow mean-reverting factor in the spot asset price dynamics. Numerical examples are provided to illustrate the investor’s optimal positions for different futures portfolios.

IS BITCOIN IMMUNE TO THE COVID-19 PANDEMIC?

This study examines how Bitcoin’s trading characteristics react to the COVID-19 pandemic, using detailed futures trading data from the Chicago Mercantile Exchange. The results show that volume-weighted Bitcoin futures return responds positively to the spikes of public interest. Meanwhile, the surges of pandemic information do not harm market quality. Volume, bid-ask spread, and trading frequency remain stable, indicating that the positive price reaction is not a result of a few small uninformed trades. Bitcoin's conditional beta on the S&P 500 index drops to near zero, while the conditional beta on gold more than doubles. These results indicate that traders have been using Bitcoin as a safe-haven asset after the pandemic outbreak.