This paper proposes an omnibus test for testing a generalized version of the martingale difference hypothesis (MDH). The generalized hypothesis includes the usual MDH or testing for conditional moments constancy such as conditional homoscedasticity (ARCH effects). Here we propose a unified approach for dealing with all of them. These hypotheses are long standing problems in econometric time ser...

This paper studies the stability of nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a nonlinear first order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. Conditions under which the model is stable in the sense that its Markov chain repre...

GARCH models with Markov-switching regimes are often used for volatility analysis of …nancial time series. Such models imply less persistence in the conditional variance than the standard GARCH model, and potentially provide a signi…cant improvement in volatility forecast. Nevertheless, conditions for asymptotic wide-sense stationarity have been derived only for some degenerated models. In this...

The relationship between volatility and risk has been one of the main factors underlying the interest in volatility modelling. An important question for international diversification is whether shocks in one market influence, or have spillovers into, returns and volatility in other markets. This paper tests for the existence of volatility spillovers among the S&P 500, FTSE 100 and Nikkei 225 st...

The three most popular univariate conditional volatility models are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the exponential GARCH (or EGARCH) model of Nelson (1990, 1991). The underlying stochastic specification to obtain GARCH was demonstr...

The present thesis deals with asymptotic analysis of financial time series models with conditional heteroscedasticity. It is well-established within financial econometrics that most financial time series data exhibit time varying conditional volatility, as well as other types of non-linearities. Reflecting this, all four essays of this thesis consider models allowing for time varying conditiona...

A class of martingale estimating functions is convenient and plays an important role for inference for nonlinear time series models. However, when the information about the first four conditional moments of the observed process becomes available, the quadratic estimating functions are more informative. In this paper, a general framework for joint estimation of conditional mean and variance para...

Recent researches on behavioral finance have tested for, among others, evidence for the relations between weather, investors’ mood, and investment decisions. Many of related to influence some weather factors, such as sunshine duration stock exchange returns, but there is no complex research taking into account a wide group factors determining mood. The main goal article verify basic market para...

We investigate how Global Economic Policy Uncertainty (GEPU) drives the long-run components of volatilities and correlations in crude oil and U.S. industry-level stock markets. Using the modified generalized autoregressive conditional heteroskedasticity mixed data sampling (GARCH-MIDAS) and dynamic conditional correlation mixed data sampling (DCC-MIDAS) specifications, we find that GEPU is posi...