Benefits and Limits of Circuit Breaker: Institutional Design Using Artificial Futures Market

Kobayashi, S.; Hashimoto, Takashi

doi:10.14441/eier.7.355

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…Table 1 lists stylized facts in each case. We used returns for 100 time units' intervals to calculate the statistical values for the stylized facts 8 . In all runs, we can find that both kurtosis and autocorrelation coefficients for square returns with several lags are positive, which means that all runs replicate stylized facts.…”

Section: Verification the Modelmentioning

confidence: 99%

“…Thurner et al showed that as leverage increases price fluctuation becomes heavy tailed and display clustered volatility [7]. Kobayashi and Hashimoto showed that circuit breakers play an important role in controlling price fluctuations, while they also reduce the trading volume [8]. Yeh and Yang investigated the effectiveness of price variation limits and showed that price variation limits help to reduce volatility and price distortion [9].…”

Section: Introductionmentioning

confidence: 99%

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

Mizuta¹,

Izumi²,

Yagi³

et al. 2013

JMF

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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 regulation and the up-tick rule caused the trading prices to be higher than the fundamental value. We also 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 the over-shorts. We also showed the limitation price range should be bigger than a volatility calculated by the limitation time span.

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Section: Verification the Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mizuta¹,

Izumi²,

Yagi³

et al. 2013

JMF

View full text Add to dashboard Cite

show abstract

“…Wei (2015) constructed a stock index futures computational experimental model to explore the optical position limit of Hushen300 stock index futures and found that the market quality would be improved when the position limit is between 100 and 300. Kobayashi et al (2007) and Kobayashi et al (2011) applied U-mart platform to explore the effect of breaker mechanism on the market fluctuation and trading volume. The results showed that though the breaker mechanism could lower the market fluctuation, it would also decrease the total trading volume.…”

Section: Contribution Of This Paper To the Literaturementioning

confidence: 99%

Analysis of the Spot Market’s T+1 Trading System Effects on the Stock Index Futures Market

Xiong

Liu

Cui

et al. 2017

EURASIA J MATH SCI T

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T+0 trading system, or day trading system, which allows the investors to buy and sell shares in one day, is a universal trading system in international markets. By contrast, T+1 trading system, which is implemented in China's stock market, allows investors to sell shares which are bought today only on the next day. T+1 trading system in spot market is a Chinese problem left over by history, and a characteristic in Chinese market. Stock index futures, the financial derivatives based on the stock, are the products of the development of the capital market at certain stage. For the stock index futures are generated based on the stock, they must be closely related. On that basis, what's the impact of the Chinese special T+1 trading system in spot market on the pricing efficiency, market liquidity and market volatility of the stock index futures market? The paper adopted the method based on the agent-based computational finance which's different from the traditional method, and built the agent-based computational crossmarket platform which includes both several stocks and stock index futures based on MASON. The paper simulated the experiment on the platform to look out the effect on the market quality of the stock index futures market when T+1 trading system in spot market turned into T+0 trading system, consisting mainly of the efficiency of price discovery, market liquidity, market volatility and arbitrage investors' order submission behavior. The results showed that, compared with the T+1 trading system in spot market, T+0 trading system improved the efficiency of price discovery and market liquidity of stock index futures market, did not raise market volatility, enhance the enthusiasm of arbitrage investors' order submission behavior. From the perspective of the stock index futures market, we believe that we should restore the T+0 trading system in stock market timely, so as to active the market and improve the efficiency of market information diffusion, to promote the steady and sound environment of the stock index futures market.

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“…Thurner, Farmer, and Geanakoplos () showed that, as leverage increases, price fluctuations become heavy tailed and display clustered volatility. Kobayashi and Hashimoto () showed that circuit breakers play an important role in controlling price fluctuations, while they also reduce the trading volume. Yeh and Yang () investigated the effectiveness of price variation limits and showed that price variation limits help to reduce volatility and price distortion.…”

Section: Introductionmentioning

confidence: 99%

Effects of Price Regulations and Dark Pools on Financial Market Stability: An Investigation by Multiagent Simulations

Mizuta

Kosugi

Kusumoto

et al. 2015

Intell. Sys. Acc. Fin. Mgmt.

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SUMMARYWe built an artificial market model and investigated the impact of large erroneous orders on financial market price formations. Comparing the case of consented large erroneous orders in the short term with that of continuous small erroneous orders in the long term, if amounts of orders are the same, we found that the orders induced almost the same price fall range. We also analysed effects of price variation limits for erroneous orders and found that price variation limits that employ a limitation term shorter than the time erroneous orders exist effectively prevent large price fluctuations. We also investigated effects of up-tick rules, adopting the trigger method that the Japan Financial Services Agency adopted in November 2013.We also investigated whether dark pools that never provide any order books stabilize markets or not using the model including one lit market, which provides all order books to investors, and one dark pool. We found that markets become more stable as the dark pool is increasingly used. We also found that using the dark pool more reduces the market impacts. However, if other investors' usage rates of dark pools become too large, investors must use the dark pool more than other investors to avoid market impacts. When a tick size of a lit market is larger, dark pools are more useful to avoid market impacts. These results suggest that dark pools stabilize markets when the usage rate is under some threshold and negatively affect the market when the usage rate is over that threshold. Our simulation results suggest the threshold might be much larger than the usage rate in present real financial markets.This study is the first to discuss whether or not several concrete and actually adoptable regulations, including those that have never been employed (e.g. price variation limits with various parameters), could prevent large fluctuations of market prices, including those beyond our experience, using artificial market simulations, and to discuss quantitatively how spreading of dark pools beyond our experience could affect market price formations using the artificial market simulations. In short, this study is the first study to comprehensively discuss how regulations and financial systems beyond our experience could affect price formations using the artificial market simulations.

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Benefits and Limits of Circuit Breaker: Institutional Design Using Artificial Futures Market

Cited by 8 publications

References 7 publications

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

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

Analysis of the Spot Market’s T+1 Trading System Effects on the Stock Index Futures Market

Effects of Price Regulations and Dark Pools on Financial Market Stability: An Investigation by Multiagent Simulations

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