The effect of exchange rate volatility on the transitional behavior of brokers: A perspective from Knowledge-driven Agent-based modeling with Software Simulation



The advancement in computational capabilities has escalated the use of agent-based simulations for the applications related to natural sciences. It brings a kind of rational expectation and local intelligence to decision support system. This kind of modeling allows us to simulate the behavior and analyze the outcome with acceptable certainty which cannot be performed otherwise in real-world settings. The popular data-driven approaches can be applied if the data is available, however, with unavailability of ground truth, the method fails to produce any outcome. On the other hand, knowledge-driven rules can perform simulation based on the domain knowledge and rules defined for the agent’s behavior which in turn helps the model to reach a certain conclusion. To prove the applicability of simulation in such dynamic environments we consider a case study of the place where buyers and sellers exchange securities. In this paper, we implement an agent-based simulation for exhibiting the behavior of brokers in the suchtrading. Agents in the model determine their speculative investment positions using fundamental and technical trading rules. An agent can change its behavior towards the usage of rules based on the speculations and interactions with other agents. In this model, we have added the exchange volatility rate to the log-linear value model and added one more agent i.e., idle broker. The main aim of this study is to analyze the behavior of agents involve in the trading with unfavorable security conditions and the impactof such conditions on the current value and the transitional behavior of agents i.e., change in the probability of switching of one agent into another. The simulation of the model shows that exchange rate volatility significantly impacts the current value as well as the transitional behavior of the agents.

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