Abstract
When financial market conditions change, traders adopt different strategies. The traders’ collective behaviour may cause significant changes in the statistical properties of price movements. When this happens, the market is said to have gone through “regime changes”. The purpose of this paper is to characterise what is a “normal market regime” as well as what is an “abnormal market regime”, under observations in Directional Changes (DC). Our study starts with historical data from 10 financial markets. For each market, we focus on a period of time in which significant events could have triggered regime changes. The observations of regime changes in these markets are then positioned in a designed two-dimensional indicator space based on DC. Our results suggest that the normal regimes from different markets share similar statistical characteristics. In other words, with our observations, it is possible to distinguish normal regimes from abnormal regimes. This is significant, because, for the first time, we can tell whether a market is in a normal regime by observing the DC indicators in the market. This opens the door for future work to be able to dynamically monitor the market for regime change.
Highlights
Prices in financial markets are records of transactions between market participants
To relate the results found in different markets, we normalise the values of the Directional Change (DC)-indicators before positioning them into the indicator space
We have explained that under a given threshold, DC summarised a dataset into trends
Summary
Prices in financial markets are records of transactions between market participants. When significant political and economic events take place, traders may have to adopt different trading strategies to counteract them. When that happens, their collective behaviour could change significantly—researchers call such changes in the market “regime changes”. Regime changes can be seen as a reflection of significant changes in the statistical properties of price movements in the financial markets. A common approach to detect what is regime change is to analyse the statistical properties of time-series [1]. When volatility has changed significantly over a period of time, we may conclude that regime changes have taken place
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