This paper analyses moment and near-epoch dependence properties for the general class of models in which the conditional variance is a linear function of squared lags of the process. It is shown how the properties of these processes depend independently on the sum and rate of convergence of the lag coefficients, the former controlling the existence of moments, and the latter the memory of the v...

What dynamics govern a time series representing the appearance of words in social media data? In this paper, we investigate an elementary dynamics, from which word-dependent special effects are segregated, such as breaking news, increasing (or decreasing) concerns, or seasonality. To elucidate this problem, we investigated approximately three billion Japanese blog articles over a period of six ...

We propose a new time series representation of persistence in conditional variance called a long memory stochastic volatility (LMSV) model. The LMSV model is constructed by incorporating an ARFIMA process in a standard stochastic volatility scheme. Strongly consistent estimators of the parameters of the model are obtained by maximizing the spectral approximation to the Gaussian likelihood. The ...

We study asymptotic expansion of the likelihood of a certain class of Gaussian processes characterized by their spectral density fθ. We consider the case where fθ(x) ∼x→0 |x|Lθ(x) with Lθ a slowly varying function and α(θ) ∈ (−∞, 1). We prove LAN property for these models which include in particular fractional Brownian motion or ARFIMA processes.

A moment bound for the normalized conditional-sum-of-squares (CSS) estimate of a general autoregressive fractionally integrated moving average (ARFIMA) model with an arbitrary unknown memory parameter is derived in this paper. To achieve this goal, a uniform moment bound for the inverse of the normalized objective function is established. An important application of these results is to establis...