This paper proposes a very general time series framework to capture the long-run behaviour of financial series. The suggested model includes linear and non-linear time trends, and stationary and nonstationary processes based on integer and/or fractional degrees of differentiation. Moreover, the...

This paper investigates persistence in financial time series at three different frequencies (daily, weekly and monthly). The analysis is carried out for various financial markets (stock markets, FOREX, commodity markets) over the period from 2000 to 2016 using two different long memory...

This paper examines the main drawbacks of technical analysis. Although this is widely used by practitioners, from an academic perspective it can only be seen as a form of "voodoo finance". In particular, it runs into the following pitfalls: Subjectivity; Doubtful assumptions; Unjustified...

This paper investigates persistence in high-frequency, intraday data (and also daily and monthly ones) in the case of the EuroStoxx 50 futures over the period from 2002 to 2018 (720 million trade records) using R/S analysis and the Hurst exponent as a measure of persistence. The results indicate...

This paper examines several US monthly financial time series data using fractional integration and cointegration techniques. The univariate analysis based on fractional integration aims to determine whether the series are I(1) (in which case markets might be efficient) or alternatively I(d) with...

This paper estimates a bivariate HEAVY system including daily and intra-daily volatility equations and its macro-augmented asymmetric power extension. It focuses on economic factors that exacerbate stock market volatility and represent major threats to financial stability. In particular, it...

This paper conducts a broad-based comparison of iterated and direct multi-step forecasting approaches applied to both univariate and multivariate models. Theoretical results and Monte Carlo simulations suggest that iterated forecasts dominate direct forecasts when estimation error is a...

For forecasting and economic analysis many variables are used in logarithms (logs). In time series analysis this transformation is often considered to stabilize the variance of a series. We investigate under which conditions taking logs is beneficial for forecasting. Forecasts based on the...

In this paper we specify a multi-factor long-memory process that enables us to estimate the fractional differencing parameters at each frequency separately, and adopt this framework to model quarterly prices in three European countries (France, Italy and the UK). The empirical results suggest...

This paper introduces the concepts of time-specific weak and strong cross section dependence. A double-indexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in...