Riesz transform on manifolds with ends of different volume growth for 1<p<21<p<2

Let $M_1$, $\cdots$, $M_\ell$ be complete, connected and non-collapsed manifolds of the same dimension, where $2\le \ell\in\mathbb{N}$, and suppose that each $M_i$ satisfies a doubling condition and a Gaussian upper bound for the heat kernel. If each manifold $M_i$ has volume growth either bigger than two or equal to two, then we show that the Riesz transform \nabla \L^{-1/2} is bounded on $L^p(M)$ for each $1<p<2$ on the gluing manifold $M=M_1\#M_2\#\cdots \# M_\ell$.Comment: 38p

Securities Transaction Tax and Stock Market Behavior in an Agent-based Financial Market Model

AbstractAs highly related to the investors’ earnings expectations and trading decision-making behavior, securities transaction tax (STT) has long been regarded as a typical regulatory mechanism exploited by policy makers. However, neither theoretical analysis nor empirical studies reach consensus about the role and policy effect of the securities transaction tax. Within the framework of agent-based computational finance, this paper presents a new artificial stock market model with heterogeneous agents, which allows us to assess the impacts of varying STTs on market behavior to come to robust conclusions. First we investigate the dynamics of benchmark market with no tax levied, and then market behaviors with different STTs are thoroughly checked. The results show that a modest transactions tax does contribute to stabilize markets by reducing market volatility, but its negative effects on market efficiency cannot be ignored at the same time. The findings suggest that regulatory authorities should introduce STT discreetly to strike a balance between stability and efficiency

Financial Volatility Forecasting with Range-based Autoregressive Volatility Model

The classical volatility models, such as GARCH, are return-based models, which are constructed with the data of closing prices. It might neglect the important intraday information of the price movement, and will lead to loss of information and efficiency. This study introduces and extends the range-based autoregressive volatility model to make up for these weaknesses. The empirical results consistently show that the new model successfully captures the dynamics of the volatility and gains good performance relative to GARCH model.This paper is forthcoming in Finance Research Letters