Renewable Energy Certificate Trading via Permissioned Blockchain

With the continuous advancement of the green certificate trading mechanism, information verification needs to span multiple departments, which causes the application process cumbersome and human errors. In order to solve problems of cumbersome issuance process of the renewable energy certificate (REC) and the inflexible pricing mechanism, in this paper, a hybrid REC trading system was proposed based on an permissioned blockchain technology (BT), which combined advantages of the BT and the continuous double auction (CDA). The operation process of the system was introduced in detail, and the view change protocol in the Practical Byzantine Fault Tolerance algorithm was revised according to the characteristics of the system to improve the system stability. The continuous double auction rule was also introduced in the system. And corresponding bidding strategies were designed to maximize the revenue of users (buyer and seller) and transaction probability. The simulation experiment proves that the bidding mechanism can flexibly adjust the REC price according to the supply and demand relationship. At the same time, the effectiveness and feasibility of trading rule and bidding strategy were also verified.

Trading Strategies of a Leveraged ETF in a Continuous Double Auction Market Using an Agent-Based Simulation

A leveraged ETF is a fund aimed at achieving a rate of return several times greater than that of the underlying asset such as Nikkei 225 futures. Recently, it has been suggested that rebalancing trades of a leveraged ETF may destabilize the financial markets. An empirical study using an agent-based simulation indicated that a rebalancing trade strategy could affect the price formation of an underlying asset market. However, no leveraged ETF trading method for suppressing the increase in volatility as much as possible has yet been proposed. In this paper, we compare different strategies of trading for a proposed trading model and report the results of our investigation regarding how best to suppress an increase in market volatility. As a result, it was found that as the minimum number of orders in a rebalancing trade increases, the impact on the market price formation decreases.

Research and Application of Continuous Bidirectional Trading Mechanism in Yunnan Electricity Market

To further promote market competition, enrich trading varieties, alleviate information asymmetry, and improve trading efficiency during electricity market reform in China, the continuous bidirectional transaction (CBT) was designed and applied in the Yunnan electricity market (YNEM), which is dominated by medium- and long-term power energy trading. The clearing model for the CBT with the goal of maximum social welfare is proposed in two bidding stages, including call auction (CA) and continuous double auction (CDA). Correspondingly, the integrated two-stage market clearing algorithm is also introduced to ensure the data consistency and business continuity. Finally, the analysis of the practical application shows that the proposed model, algorithm, and various key implementation strategies of the trading platform support the bidding and clearing of the CBT well. In addition, the research and application of CBT may also provide valuable insights for other electricity market construction.

Aggregation mechanisms for crowd predictions

When the information of many individuals is pooled, the resulting aggregate often is a good predictor of unknown quantities or facts. This aggregate predictor frequently outperforms the forecasts of experts or even the best individual forecast included in the aggregation process (“wisdom of crowds”). However, an appropriate aggregation mechanism is considered crucial to reaping the benefits of a “wise crowd”. Of the many possible ways to aggregate individual forecasts, we compare (uncensored and censored) arithmetic and geometric mean and median, continuous double auction market prices and sealed bid-offer call market prices in a controlled experiment. We use an asymmetric information structure, where participants know different sub-sets of the total information needed to exactly calculate the asset value to be estimated. We find that prices from continuous double auction markets clearly outperform all alternative approaches for aggregating dispersed information and that information lets only the best-informed participants generate excess returns.

The Exposure Problem and Market Design

Markets have an exposure problem when getting to the optimal allocation requires a sequence of transactions which if started but not completed leaves at least one trader with losses. We use laboratory experiments to evaluate the effect of the exposure problem on alternative market mechanisms. The continuous double auction performs poorly: efficiency is only 20% when exposure is high and 55% when it is low. A package market effectively eliminates the exposure problem: in low and high-exposure treatments efficiency is 82% and 89%, respectively. Building on stability notions from matching theory we introduce the concept of mechanism stability. A model of trade that combines mechanism stability with noisy best responses and imperfect foresight explains the difference in market performance. Finally, decentralized bargaining with contingent contracts performs well with perfect information and communication but not in the more realistic case when traders’ preferences are privately known.

A Q-Cube Framework of Reinforcement Learning Algorithm for Continuous Double Auction among Microgrids

Decision-making of microgrids in the condition of a dynamic uncertain bidding environment has always been a significant subject of interest in the context of energy markets. The emerging application of reinforcement learning algorithms in energy markets provides solutions to this problem. In this paper, we investigate the potential of applying a Q-learning algorithm into a continuous double auction mechanism. By choosing a global supply and demand relationship as states and considering both bidding price and quantity as actions, a new Q-learning architecture is proposed to better reflect personalized bidding preferences and response to real-time market conditions. The application of battery energy storage system performs an alternative form of demand response by exerting potential capacity. A Q-cube framework is designed to describe the Q-value distribution iteration. Results from a case study on 14 microgrids in Guizhou Province, China indicate that the proposed Q-cube framework is capable of making rational bidding decisions and raising the microgrids’ profits.

Investor Structure and Stock Price Crash Risk in a Continuous Double Auction Market: An Agent-Based Perspective

Multiple studies presume the institutional investors to be informed investors. However, some reports argue that this view is still under debate. In this paper, to avoid the informed investors proxy bias caused by the institutional investors, we construct an agent-based continuous double auction stock market model with both informed and uninformed investors and examine whether stock price crash risk can be affected by the change of investor structure. In particular, we employ four types of investor structures by gradually increasing percentage adjustments of informed investors from 20%, 40%, 60% to 80% within the market. We find that stock clear price and return show significant improved stability coming with the rising weight of informed investors. Beyond that, we recognize the situation that stock clear price falls below 95% confidence interval as crash event and count the number of the stock price crash events within each simulation of each different investor structure. We find that consistent with growing stability of stock clear price and return, stock price crash event number drops dramatically following the higher proportion of informed investors. These findings confirm our hypothesis that the involvement of informed investors contributes to the market stability.