We present an agent-based model (ABM) of a financial market with n 1 risky assets, whose price dynamics result from the interaction between rational fundamentalists and trend following imitative noise traders. The interactions and opinion formation of the noise traders are described by an...

As a colossal developing economy, irrational, and inefficient trades broadly exist in China's stock market and are intensified by the once-in-a-century COVID-19 pandemic. This atypical but prominent event enhances systemic risk and requires a more effective analysis tool that adapts to the...

This paper examines the degree of persistence in the volatility of financial time series using a Long Memory Stochastic Volatility (LMSV) model. Specifically, it employs a Gaussian semiparametric (or local Whittle) estimator of the memory parameter, based on the frequency domain, proposed by...

When analysing the volatility related to high frequency financial data, mostly non-parametric approaches based on realised or bipower variation are applied. This article instead starts from a continuous time diffusion model and derives a parametric analog at high frequency for it, allowing...

Simulations of agent-based models have shown that the stylized facts (unit-root, fat tails and volatility clustering) of financial markets have a possible explanation in the interactions among agents. However, the complexity, originating from the presence of non-linearity and interactions, often...

Simulations of agent-based models have shown that the stylized facts (unit-root, fat tails and volatility clustering) of financial markets have a possible explanation in the interactions among agents. However, the complexity, originating from the presence of non-linearity and interactions, often...