Algorithmic trading ... - page 6
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Methods: To study the microscopic origination of the return-volatility correlation in financial systems, we take into account the individual and collective behaviors of investors in real markets, and construct an agent-based model. The agents are linked with each other and trade in groups, and particularly, two novel microscopic mechanisms, i.e., investors' asymmetric trading and herding in bull and bear markets, are introduced. Further, we propose effective methods to determine the key parameters in our model from historical market data.
Results: With the model parameters determined for six representative stock-market indices in the world respectively, we obtain the corresponding leverage or anti-leverage effect from the simulation, and the effect is in agreement with the empirical one on amplitude and duration. At the same time, our model produces other features of the real markets, such as the fat-tail distribution of returns and the long-term correlation of volatilities.
Conclusions: We reveal that for the leverage and anti-leverage effects, both the investors' asymmetric trading and herding are essential generation mechanisms. These two microscopic mechanisms and the methods for the determination of the key parameters can be applied to other complex systems with similar asymmetries.
This paper investigates effects of realistic, non-ideal, decisions of energy users as to whether to participate in an energy trading system proposed for demand-side management of a residential community. The energy trading system adopts a non-cooperative Stackelberg game between a community energy storage (CES) device and users with rooftop photovoltaic panels where the CES operator is the leader and the users are the followers. Participating users determine their optimal energy trading starting time to minimize their personal daily energy costs while subjectively viewing their opponents' actions. Following a non-cooperative game, we study the subjective behavior of users when they decide on energy trading starting time using prospect theory. We show that depending on the decisions of participating-time, the proposed energy trading system has a unique Stackelberg equilibrium at which the CES operator maximizes their revenue while users minimize their personal energy costs attaining a Nash equilibrium. Simulation results confirm that the benefits of the energy trading system are robust to decisions of participating-time that significantly deviate from complete rationality.
The quest for minimising risk and maximising returns has brought us to the advent of Algorithmic Trading (AT). AT is a rule based (automated execution logic based) responsible for executing orders to buy or sell a given asset at a specific time, price and location (market venue). AT has witnessed phenomenal growth in India in last five years. This article examines the impact of AT on market quality and human trading behaviour.
With the rapid development of network and mobile terminal, online trading has become more and more widespread. However, E-commerce transaction systems aren’t completely strong due to the openness of network. Some points of a system is vulnerable in the real world and thus they can be utilized by attackers and cheaters. We focus on E-commerce transaction systems with attacks, and propose a kind of Petri nets called VET-net (Vulnerable E-commerce Transaction nets) to model them. A VET-net considers both normal actions belonging to the related system and malicious actions ones such as tampering with a data. Based on VET-net, this paper proposes the concepts of vulnerable points and vulnerable levels in order to describe the cause and levels of vulnerability. And then it uses the dynamic sling method to locate the vulnerable points. A real example is used to illustrate the effectiveness and rationality of our concepts and method.