Topic
Algorithmic trading
About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.
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TL;DR: The seven pilot carbon trading schemes have features in common, but vary considerably in their approach to issues such as the coverage of sectors, allocation of allowances, price uncertainty and market stabilization, potential market power of dominated players, use of offsets, and enforcement and compliance as discussed by the authors.
Abstract: The Chinese central government has approved the seven pilot carbon trading schemes. These seven pilot regions are deliberately selected to be at varying stages of development and are given considerable leeway to design their own schemes. These pilot trading schemes have features in common, but vary considerably in their approach to issues such as the coverage of sectors, allocation of allowances, price uncertainty and market stabilization, potential market power of dominated players, use of offsets, and enforcement and compliance. This article explains why China opts for emissions trading, rather than carbon or environmental taxes at least initially, discusses the key common and varying features of these carbon trading pilots and their first-year performance, draws the lessons learned, discusses the potential pathways for evolution of regional pilot carbon trading schemes into a nationwide carbon trading scheme, and raises fundamental issues that must be addressed in order to make such an emissions trading scheme to work reliably and effectively and with an increasingly expanded coverage and scope.
41 citations
TL;DR: In this paper, the impact of index futures trading on the volatility of the underlying stock market was investigated and it was shown that index futures did not destabilize the spot market and that uninformed traders were the dominant traders in the futures markets.
Abstract: This paper investigates the impact of introducing index futures trading on the volatility of the underlying stock market. We exploit a unique institutional setting in which presumably uninformed individuals are the dominant trader type in the futures markets. This enables us to investigate the destabilization hypothesis more accurately than previous studies do and to provide evidence for or against the inuence of individuals trading in index futures on spot market volatility. To overcome econometric shortcomings of the existing literature we employ a Markov-switching-GARCH approach to endogenously identify distinct volatility regimes. Our empirical evidence for Poland surprisingly suggests that the introduction of index futures trading does not destabilize the spot market.
41 citations
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TL;DR: ABIDES, an Agent-Based Interactive Discrete Event Simulation environment designed from the ground up to support AI agent research in market applications is introduced and its use and configuration is illustrated with sample code, validating the environment with example trading scenarios.
Abstract: We introduce ABIDES, an Agent-Based Interactive Discrete Event Simulation environment. ABIDES is designed from the ground up to support AI agent research in market applications. While simulations are certainly available within trading firms for their own internal use, there are no broadly available high-fidelity market simulation environments. We hope that the availability of such a platform will facilitate AI research in this important area. ABIDES currently enables the simulation of tens of thousands of trading agents interacting with an exchange agent to facilitate transactions. It supports configurable pairwise network latencies between each individual agent as well as the exchange. Our simulator's message-based design is modeled after NASDAQ's published equity trading protocols ITCH and OUCH. We introduce the design of the simulator and illustrate its use and configuration with sample code, validating the environment with example trading scenarios. The utility of ABIDES is illustrated through experiments to develop a market impact model. We close with discussion of future experimental problems it can be used to explore, such as the development of ML-based trading algorithms.
41 citations
TL;DR: In this paper, a new paradigm for the trading of equities is described, which involves the notion of synthetic prices and some idealizations involving the volatility of prices and trading liquidity.
41 citations
TL;DR: The authors developed a pairs trading framework based on a mean-reverting jump-diffusion model and applied it to minute-by-minute data of the S&P 500 oil companies from 1998 to 2015.
Abstract: This paper develops a pairs trading framework based on a mean-reverting jump–diffusion model and applies it to minute-by-minute data of the S&P 500 oil companies from 1998 to 2015. The established ...
41 citations