Georgia Institute of Technology College of Sciences

The functional autoregressive process has become a useful tool in the analysis of functional time series data. In this model, the observations and the errors are curves, and the role of the autoregressive coefficient is played by an integral operator. To ensure meaningful inference and prediction, it is important to verify that this operator does not change with time. We propose a method for testing its constancy which uses the functional principal component analysis. The test statistic is constructed to have a Kiefer type asymptotic distribution. The asymptotic justification of the procedure is very delicate and touches upon central notions of functional data analysis. The test is implemented using the R package fda. Its finite sample performance is illustrated by an application to credit card transaction data.

We propose a new two stage semi-parametric test and estimation procedure to investigate predictability of stochastic jump arrivals in asset prices. It allows us to search for conditional information that affects the likelihood of jump occurrences up to the intra-day levels so that usual factor analysis for jump dynamics can be achieved. Based on the new theory of inference, we find empirical evidence of jump clustering in U.S. individual equity markets during normal trading hours. We also present other intra-day jump predictors such as analysts recommendation updates and stock news releases.

This paper introduces a new nonlinear long memory volatility process, denoted by Smooth Transition FIGARCH, or ST-FIGARCH, which is designed to account for both long memory and nonlinear dynamics in the conditional variance process. The nonlinearity is introduced via a logistic transition function which is characterized by a transition parameter and a variable. The model can capture smooth jumps in the altitude of the volatility clusters as well as asymmetric response to negative and positive shocks. A Monte Carlo study finds that the ST-FIGARCH model outperforms the standard FIGARCH model when nonlinearity is present, and performs at least as well without nonlinearity. Applications reported in the paper show both nonlinearity and long memory characterize the conditional volatility in exchange rate and stock returns and therefore presence of nonlinearity may not be the source of long memory found in the data.