A key application of long memory time series models concerns innation. Long memory implies that shocks have a long-lasting eeect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since such level shifts are not unlikely for innation, where the shifts may be caused by sudden oil price shocks, we examine whether evidence for long memory (...

A mean square error criterion is used in this paper to provide a systematic approach to approximate a long-memory time series by a short-memory ARMA(1; 1) process. Analytic expressions are derived to assess the accuracy of such an approximation. These results are valid not only for the pure fractional noise case, but also for a general autoregressive fractional moving average long-memory time s...

The hedging of weather risks has become extremely relevant in recent years, promoting the diffusion of weather derivative contracts. The pricing of such contracts require the development of appropriate models for the prediction of the underlying weather variables. Within this framework, we present a modification of the double long memory ARFIMA-FIGARCH model introducing time-varying memory coef...

! ! The daily return and the realized volatility are simultaneously modeled in the stochastic volatility model with leverage and long memory. In addition to the stochastic volatility model with leverage for the daily returns, ARFIMA process is jointly considered for the realized volatilities. Using a state space representation of the model, we estimate parameters by Markov chain Monte Carlo met...

This paper describes the development and approbation of software solution, which implements methodology forming recommendations for composition investment portfolios using fractal analysis long memory predictive models, is result research conducted over several years at Department Information Systems Mathematical Methods in Economics PSU. The general algorithm program includes four main steps: ...