Design of Financial Market Regulations against Large Price Fluctuations Using by Artificial Market Simulations

Abstract

<p align="justify"> <span style="font-family:Verdana;">We built an artificial market model and compared effects of price variation limits, short selling regulations and up-tick rules. In the case without the regulations, the price fell to below a fundamental value when an economic crush occurred. On the other hand, in the case with the regulations, this overshooting did not occur. However, the short selling regula</span><span style="font-family:Verdana;">tion and the up-tick rule caused the trading prices to be higher than the fundamental value. We also surveyed an ade</span><span style="font-family:Verdana;">quate 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 the over-shorts. We also showed the limitation price range should be bigger than a volatility calculated by the limitation time span.</span> </p> <p> <span></span> </p

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

“…In the case without unlock (left), however, the market prices continued to increase even after the prices recovered to 10, 000, which was the price before the erroneous orders. This result is consistent with those of previous studies that investigated up-tick rules that are in effect all the time [3]. On the other hand, in the case in which the rule is unlocked by time (right), the rule prevented prices continued to increase after the prices recovered because the up-tick rule was unlocked and was not in effect while prices increased.…”

“…As mentioned in III-B, the characteristics of large erroneous orders are similar when s g is constant. Previous studies showed that price variation limits have the same effectiveness for price variations when ΔP pl /t pl is constant [3], [12]. In Table II, we shade regions satisfying inequality,…”

“…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.…”

“…However, there are many indications that the rule increases turbulence [3]. Therefore, the Japan Financial Services Agency modified the up-tick rule on November 2013 by introducing a "trigger method" in which the rule is applied only when the prices fall below a certain threshold [4].…”

Roadmap and research issues of multiagent social simulation using high-performance computing

“…We are considering extending the market simulations to various applications used for stock market analyses. For example, it is in the interest of market companies to determine ''daily limit'' and ''cut-off'' prices [4]. In this case, the simulation must handle 10-20 goods.…”