Abstract
In this paper, we propose a new model of security price dynamics in order to explain the stylized facts of the pricing process such as power law distribution, volatility clustering, jumps, and structural changes. We assume that there are two types of agents in the financial market: speculators and fundamental investors. Speculators use past prices to predict future prices and only buy assets whose prices are expected to rise. Fundamental investors attach a certain value to each asset and buy when the asset is undervalued by the market. When the expectations of agents are exogenously driven, that is, entirely shaped by exogenous news, then they can be modeled as following a random walk. We assume that the information related to the two types of agents in the model will arrive randomly with a certain probability distribution and change the viewpoint of the agents according to a certain percentage. Our simulated results show that this model can simulate well the random walk of asset prices and explain the power-law tail distribution of returns, volatility clustering, jumps, and structural changes of asset prices.
Highlights
Many empirical studies point out the fact that there are typically power-law tails, volatility clustering, jumps, and structural changes in time series of financial asset prices
We propose a new model of security price dynamics in order to explain the stylized facts of the pricing process such as power law distribution, volatility clustering, jumps, and structural changes
In this paper, based on the typical characteristics of the power-law distribution, volatility clustering, jumps, and structural changes in China’s stock market, we improve and expand the model given by Inoua [11] and propose a price generating mechanism model with exogenous information impact
Summary
Many empirical studies point out the fact that there are typically power-law tails, volatility clustering, jumps, and structural changes in time series of financial asset prices. Gabaix et al [1] summarized many research results and found heavy-tailed long-range distributions with characteristic power-law exponents, the so-called inverse cubic law They pointed out that this is rather “universal” for financial markets in most countries, with time intervals ranging from one minute to one month, across different sizes of stocks, different time periods, and different stock market indices.
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