Searcharxiv⌕ Search

arXiv subjects

Yaozhong Hu

Publications and source records attributed to Yaozhong Hu.

At least 55 records · Page 3Linked to original sources

Nonlinear McKean-Vlasov diffusions under the weak Hormander condition with quantile-dependent coefficients

In this paper, the strong existence and uniqueness for a degenerate finite system of quantile-dependent McKean-Vlasov stochastic differential equations are obtained under a weak Hörmander condition. The approach relies on the apriori bounds for the density of the solution to time inhomogeneous diffusions. The time inhomogeneous Feynman-Fac formula is used to construct a contraction map for this degenerate system.

math.PR↗

Solvability of parabolic Anderson equation with fractional Gaussian noise

This paper provides necessary as well as sufficient conditions on the Hurst parameters so that the continuous time parabolic Anderson model $\frac{\partial u}{\partial t}=\frac{1}{2}\frac{\partial^2 u}{\partial x^2}+u\dot{W}$ on $[0, \infty)\times {\bf R}^d $ with $d\geq 1$ has a unique random field solution, where $W(t, x)$ is a fractional Brownian sheet on $[0, \infty)\times {\bf R}^d$ and formally $\dot W =\frac{\partial^{d+1}}{\partial t \partial x_1 \cdots \partial x_d} W(t, x)$. When the noise $W(t, x)$ is white in time, our condition is both necessary and sufficient when the initial data $u(0, x)$ is bounded between two positive constants. When the noise is fractional in time with Hurst parameter $H_0>1/2$, our sufficient condition, which improves the known results in literature, is different from the necessary one.

math.PR↗

Stochastic Heat Equation with general noise

In this paper, we study a nonlinear one spatial dimensional stochastic heat equations driven by Gaussian noise: $\frac{\partial u }{\partial t}=\frac{\partial^2 u }{\partial x^2}+σ(u )\dot{W} $, where $\dot{W} $ is white in time and has the covariance of a fractional Brownian motion with Hurst parameter $H\in(\frac 14,\frac 12)$. We remove a critical and unnatural condition $σ(0)=0$ previously imposed in a recent paper by Hu, Huang, Lê, Nualart and Tindel. The idea is to work on a weighted space $\mathcal{Z}_{λ,T}^p$ for some power decay weight $λ(x)=c_H(1+|x|^2)^{H-1}$. We obtain the weak existence of solution. With additional decay conditions on $σ$ we obtain the existence of strong solution and the pathwise uniqueness of the strong solution. The reason to introduce the weight function is that the solution $u(t,x)$ may explode as $|x|\rightarrow \infty$ when the "diffusion coefficient" $σ(u)$ does not satisfy $σ(0)=0$ regardless of the initial condition. This motivates us to study the exact asympotics of the solution $u_{\rm add}(t,x)$ as $t$ and $x$ go to infinity when $σ(u)=1$ and when the initial condition $u_0(x)\equiv 0$. In particular, we find the exact growth of $\sup_{|x|\leq L}{|u_{\rm add}(t,x)|}$. Furthermore, we find the sharp growth rate for the Hölder coefficients, namely, $\sup_{|x|\leq L} \frac{| u_{\rm add}(t,x+h)-u_{\rm add}(t,x)|}{|h|^β}$ and $\sup_{|x|\leq L} \frac{| u_{\rm add}(t+τ,x)-u_{\rm add}(t,x)|}{τ^α}$. These results are interesting and fundamental themselves.

math.PR↗

Parameter estimation for threshold Ornstein-Uhlenbeck processes from discrete observations

Assuming that a threshold Ornstein-Uhlenbeck process is observed at discrete time instants, we propose generalized moment estimators to estimate the parameters. Our theoretical basis is the celebrated ergodic theorem. To use this theorem we need to find the explicit form of the invariant measure. With the sampling time step arbitrarily fixed, we prove the strong consistency and asymptotic normality of our estimators as the sample size tends to infinity.

math.ST↗

Functional central limit theorems for stick-breaking priors

We obtain the empirical strong law of large numbers, empirical Glivenko-Cantelli theorem, central limit theorem, functional central limit theorem for various nonparametric Bayesian priors which include the Dirichlet process with general stick-breaking weights, the Poisson-Dirichlet process, the normalized inverse Gaussian process, the normalized generalized gamma process, and the generalized Dirichlet process. For the Dirichlet process with general stick-breaking weights, we introduce two general conditions such that the central limit theorem and functional central limit theorem hold. Except in the case of the generalized Dirichlet process, since the finite dimensional distributions of these processes are either hard to obtain or are complicated to use even they are available, we use the method of moments to obtain the convergence results. For the generalized Dirichlet process we use its finite dimensional marginal distributions to obtain the asymptotics although the computations are highly technical.

math.ST↗

Positivity preserving logarithmic Euler-Maruyama type scheme for stochastic differential equations

In this paper, we propose a class of explicit positivity preserving numerical methods for general stochastic differential equations which have positive solutions. Namely, all the numerical solutions are positive. Under some reasonable conditions, we obtain the convergence and the convergence rate results for these methods. The main difficulty is to obtain the strong convergence and the convergence rate for stochastic differential equations whose coefficients are of exponential growth. Some numerical experiments are provided to illustrate the theoretical results for our schemes.

math.NA↗

Jump Models with delay -- option pricing and logarithmic Euler-Maruyama scheme

In this paper, we obtain the existence, uniqueness and positivity of the solution to delayed stochastic differential equations with jumps. This equation is then applied to model the price movement of the risky asset in a financial market and the Black-Scholes formula for the price of European options is obtained together with the hedging portfolios. The option price is evaluated analytically at the last delayed period by using the Fourier transformation technique. But in general, there is no analytical expression for the option price. To evaluate the price numerically we then use the Monte-Carlo method. To this end, we need to simulate the delayed stochastic differential equations with jumps. We propose a logarithmic Euler-Maruyama scheme to approximate the equation and prove that all the approximations remain positive and the rate of convergence of the scheme is proved to be $0.5$.

q-fin.MF↗

Estimation of all parameters in the reflected Orntein-Uhlenbeck process from discrete observations

Assuming that a reflected Ornstein-Uhlenbeck state process is observed at discrete time instants, we propose generalized moment estimators to estimate all drift and diffusion parameters via the celebrated ergodic theorem. With the sampling time step h > 0 arbitrarily fixed, we prove the strong consistency and asymptotic normality of our estimators as the sampling size n tends to infinity. This provides a complete solution to an open problem left in Hu et al. [5].

math.ST↗

Local time of infinite time horizon Brownian bridge

We introduce an infinite time horizon Brownian bridge which is determined by a stochastic Langevin equation with time dependent drift coefficient. We show that this process goes to zero almost surely when the time goes to infinity and study the existence and asymptotic behavior of its local time as well as its Hölder continuity in time variable and in location variable. The main difficulty is the lack of stationarity of the process so that the powerful tools for stationary (Gaussian) processes are not applicable. We employ the Garsia-Rodemich-Rumsey inequality to get around this type of difficulty.

math.PR↗

Numerical methods for stochastic Volterra integral equations with weakly singular kernels

In this paper, we first establish the existence, uniqueness and Hölder continuity of the solution to stochastic Volterra integral equations with weakly singular kernels. Then, we propose a $θ$-Euler-Maruyama scheme and a Milstein scheme to solve the equations numerically and we obtain the strong rates of convergence for both schemes in $L^{p}$ norm for any $p\geq 1$. For the $θ$-Euler-Maruyama scheme the rate is $\min\{1-α,\frac{1}{2}-β\}~ % (0<α<1, 0< β<\frac{1}{2})$ and for the Milstein scheme the rate is $\min\{1-α,1-2β\}$ when $α\neq \frac 12$, where $(0<α<1, 0< β<\frac{1}{2})$. These results on the rates of convergence are significantly different from that of the similar schemes for the stochastic Volterra integral equations with regular kernels. The difficulty to obtain our results is the lack of Itô formula for the equations. To get around of this difficulty we use instead the Taylor formula and then carry a sophisticated analysis on the equation the solution satisfies.

math.NA↗

Estimation Of all parameters in the Fractional Ornstein-Uhlenbeck model under discrete observations

Let the Ornstein-Uhlenbeck process $(X_t)_{t\ge0}$ driven by a fractional Brownian motion $B^{H }$, described by $dX_t = -θX_t dt + σdB_t^{H }$ be observed at discrete time instants $t_k=kh$, $k=0, 1, 2, \cdots, 2n+2 $. We propose ergodic type statistical estimators $\hat θ_n $, $\hat H_n $ and $\hat σ_n $ to estimate all the parameters $θ$, $H $ and $σ$ in the above Ornstein-Uhlenbeck model simultaneously. We prove the strong consistence and the rate of convergence of the estimators. The step size $h$ can be arbitrarily fixed and will not be forced to go zero, which is usually a reality. The tools to use are the generalized moment approach (via ergodic theorem) and the Malliavin calculus.

math.ST↗

Mean-field backward stochastic differential equations and applications

In this paper we study the mean-field backward stochastic differential equations (mean-field bsde) of the form dY(t) =-f(t,Y(t),Z(t),K(t, . ),E[φ(Y(t),Z(t),K(t,.))])dt+Z(t)dB(t) +\int_{R_{0}}K(t,ζ)\tilde{N}(dt,dζ), where B is a Brownian motion, \tilde{N} is the compensated Poisson random measure. Under some mild conditions, we prove the existence and uniqueness of the solution triplet (Y,Z,K). It is commonly believed that there is no comparison theorem for general mean-field bsde. However, we prove a comparison theorem for a subclass of these equations. When the mean-field bsde is linear, we give an explicit formula for the first component Y(t) of the solution triplet. Our results are applied to solve a mean-field recursive utility optimization problem in finance.

math.OC↗

Hölder continuity of the solutions to a class of SPDEs arising from multidimensional superprocesses in random environment

We consider a $d$-dimensional branching particle system in a random environment. Suppose that the initial measures converge weakly to a measure with bounded density. Under the Mytnik-Sturm branching mechanism, we prove that the corresponding empirical measure $X_t^n$ converges weakly in the Skorohod space $D([0,T];M_F(\mathbb{R}^d))$ and the limit has a density $u_t(x)$, where $M_F(\mathbb{R}^d)$ is the space of finite measures on $\mathbb{R}^d$. We also derive a stochastic partial differential equation $u_t(x)$ satisfies. By using the techniques of Malliavin calculus, we prove that $u_t(x)$ is jointly Hölder continuous in time with exponent $\frac{1}{2}-ε$ and in space with exponent $1-ε$ for any $ε>0$.

math.PR↗

On the necessary and sufficient conditions to solve a heat equation with general additive Gaussian noise

In this note we consider stochastic heat equation with general additive Gaussian noise. Our aim is to derive some necessary and sufficient conditions on the Gaussian noise in order to solve the corresponding heat equation. We investigate this problem invoking two different methods, respectively based on variance computations and on path-wise considerations in Besov spaces. We are going to see that, as anticipated, both approaches lead to the same necessary and sufficient condition on the noise. In addition, the path-wise approach brings out regularity results for the solution.

math.PR↗

Drift parameter estimation for nonlinear stochastic differential equations driven by fractional Brownian motion

We derive the strong consistency of the least squares estimator for the drift coefficient of a fractional stochastic differential system. The drift coeffcient is one-sided dissipative Lipschitz and the driving noise is additive and fractional with Hurst parameter $H \in (\frac{1}{4}, 1)$. We assume that continuous observation is possible. The main tools are ergodic theorem and Malliavin calculus. As a by-product, we derive a maximum inequality for Skorohod integrals, which plays an important role to obtain the strong consistency of the least squares estimator.

math.PR↗

Asymptotics of the density of parabolic Anderson random fields

We investigate the sharp density $ρ(t,x; y)$ of the solution $u(t,x)$ to stochastic partial differential equation $\frac{\partial }{\partial t} u(t,x)=\frac12 Δu(t,x)+u\diamond \dot W(t,x)$, where $\dot W$ is a general Gaussian noise and $\diamond$ denotes the Wick product. We mainly concern with the asymptotic behavior of $ρ(t,x; y)$ when $y\rightarrow \infty$ or when $t\to0+$. Both upper and lower bounds are obtained and these two bounds match each other modulo some multiplicative constants. If the initial datum is positive, then $ρ(t,x;y)$ is supported on the positive half line $y\in [0, \infty)$ and in this case we show that $ρ(t,x; 0+)=0$ and obtain an upper bound for $ρ(t,x; y)$ when $y\rightarrow 0+$.

math.PR↗