Price variation limits and financial market bubbles: Artificial market simulations with agents' learning process

Abstract

Financial exchanges sometimes employ a "price variation limit", which restrict trades out of certain price ranges within certain time spans to avoid sudden large price fluctuations.We built an artificial market model implementing a learning process to replicate bubbles that has the continues double auction mechanism and investigated price variation limits. We surveyed an adequate limitation price range and an adequate limitation time span for the price variation limit and found a parameters' condition of the price variation limit to prevent bubbles. The price variation limits are expected to be an especially effective way to prevent bubbles, so the model should be able to replicate bubbles. When we gave a bubble-inducing trigger, which is a rapid increment of the fundamental value, a bubble occurred in the case in which the model implemented the learning process and did not occur in the case without the process. We also showed that a hazard rate enables verification of whether the models can replicate a bubble process or not.

Regulations' effectiveness for market turbulence by large erroneous orders using multi agent simulation

“…Mizuta et al [12] added a learning process to the model of Chiarella et al [13] and showed that the learning process is needed to replicate dynamic large fluctuations unstably existing in the short term, such as those caused by large erroneous orders. In this study, we additionally implemented a large-erroneous-order process and some order price regulations to the model of Mizuta et al [12].…”

“…The model of Mizuta et al [12], on which we base our model, was itself based on the model of Chiarella et al [13], which succeed in replicating stylized facts that are the statistical nature of returns stably existing over the long term in almost all financial markets. Mizuta et al [12] added a learning process to the model of Chiarella et al [13] and showed that the learning process is needed to replicate dynamic large fluctuations unstably existing in the short term, such as those caused by large erroneous orders.…”

“…In the actual financial markets that include such large fluctuations, the probability that a run (sequence of observations of the same sign) of negative returns will end should decline with the length of the run q [12], [15], [16]. A hazard rate H(q) is used for the test of large fluctuations.…”

“…An artificial market, which is a kind of a multi agent simulation, will help us to discuss such new regulation effectively [5], [6]. Many studies have investigated the effects of several institutions and regulations using artificial market simulations [3], [7]- [12]. No simulation studies, however, have used artificial markets to investigate effects of price variation limits or up-tick rules adopting the trigger method for large financial market turbulence caused by large erroneous orders.…”

“…No simulation studies, however, have used artificial markets to investigate effects of price variation limits or up-tick rules adopting the trigger method for large financial market turbulence caused by large erroneous orders. Therefore, we built a simple artificial market model on the basis of the model of Mizuta et al [12] and investigated effects of these price regulations in large turbulence caused by large erroneous orders. …”