In this paper, I apply univariate and vector autoregressive (VAR) models to forecast inflation in Vietnam. To investigate the forecasting performance of the models, two naive benchmark models (one is a variant of a random walk and the other is an autoregressive model) are first built based on...

We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of...

This paper sheds new light on the mutual relationship between investor sentiment and excess returns corresponding to the bubble component of stock prices. We propose to use the wavelet concept of the phase angle to determine the lead-lag relation between these variables. The wavelet phase angle...

We apply the fractionally cointegrated vector autoregressive (FCVAR) model to analyze the relationship between spot and futures prices in five commodity markets (aluminium, copper, lead, nickel, and zinc). To this end, we first extend the FCVAR model to accommodate de terministic trends in the...

Standard price discovery measures, particularly information shares, rely on the concept of co-integration for non-stationary time series. For the definition of information shares, the existence of a permanent impact of innovations is crucial, as these shares measure the relative contribution of...

We apply the fractionally cointegrated vector autoregressive (FCVAR) model to analyze the relationship between spot and futures prices in five commodity markets (aluminium, copper, lead, nickel, and zinc). To this end, we first extend the FCVAR model to accommodate deterministic trends in the...

We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of...

We develop spectral volume models to systematically estimate, explain, and exploit the high-frequency periodicity in intraday trading activities using Fourier analysis. The framework consistently recovers periodicities at specific frequencies in three steps, despite their low signal-to-noise...

A time series model is discussed that incorporates both permanent and transient effects. Estimation techniques are given, and the power of the likelihood ratio test is assessed. When applied to the monthly price/earnings series of the S&P 500 over the period 1871-2013, both permanent and...