Purpose The purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns volatility. Design/methodology/approach competing models are autoregressive moving average (ARMA) model fractional integrated (ARFIMA) for returns. For volatility, exponential generalized conditional heteroscedasticity (EGARCH) with GARCH (FIGARCH) component...

We introduce a general approach which unifies some previous attempts for modeling the dynamic of multivariate time series or regression analysis when data are mixed type (binary/count/continuous). Our is quite flexible since conditionally on past values, each coordinate at t can have distribution compatible with standard univariate model such as GARCH, ARMA, INGARCH logistic models whereas valu...

Article history: Received 18 July 2013 Received in revised form 9 May 2014 Accepted 15 May 2014 Available online 22 May 2014 Highly volatile scenarios, such as those provoked by the recent subprime and sovereign debt crises, have questioned the accuracy of current risk forecasting methods. This paper adds fuel to this debate by comparing the performance of alternative specifications for modelin...

In this paper, a new econometric model of volatility is proposed using hybrid Support Vector machine for Regression (SVR) combined with Chaotic Genetic Algorithm (CGA) to fit conditional mean and then conditional variance of stock market returns. The CGA, integrated by chaotic optimization algorithm with Genetic Algorithm (GA), is used to overcome premature local optimum in determining three hy...

Frequently econometricians are interested in verifying a relationship between two or more time series. Such analysis is typically carried out by causality and/or independence tests which have been well studied when the data is univariate or multivariate. Modern data though is increasingly of a high dimensional or functional nature for which finite dimensional methods are not suitable. In the pr...

Long memory effects can be found in different kind of data from finance to hydrology. Therefore, models which can reflect these properties have become more popular in recent years especially in the fields of time series analysis, econometrics and financial mathematics. Mandelbrot-Van Ness fractional Lévy processes allow for such stationary long memory effects in their increments and have been u...

In view of the recent documented hedging bias attributable to failing to accommodate volatility long memory, we suggest to use the simple, yet superior, realized variancecovariance (RVCOV) in dynamic hedging. For its incremental value from intradaily information, model-free and inherent long memory, RVCOV has been shown to be accurate without misspecification bias and easily generalized to high...

Since the outbreak of COVID-19 epidemic, great changes have taken place in world's economic situation. The interest rate increase by Federal Reserve has become one most influential actions. Researchers found that Fed's hike not only an impact on American financial market but also world situation to a certain extent. Therefore, this paper collects share price US dollar and exchange between RMB s...

An approach to the modelling of volatile time series using a class uniformity-preserving transforms for uniform random variables is proposed. V-transforms describe relationship between quantiles stationary distribution and predictable volatility proxy variable. They can be represented as copulas permit formulation estimation models that combine arbitrary marginal distributions with copula proce...