In this paper, we use weekly stock market data to examine whether the volatility of stock returns of ten emerging capital markets of the new EU member countries has changed since the opening of their capital markets. In particular we are interested in understanding whether there are high and low periods of stock returns volatility and what the degree of correlation across these markets is. We estimate a Markov-Switching ARCH (SWARCH) model proposed by Hamilton and Susmel (1994) and we allow for the possibility that two or three volatility regimes may exist for stock returns volatility. The main finding of the present study is that the high volatility of stock returns of all new EU emerging stock markets is associated mainly with the 1997‒1998 Asian and Russian financial crises as well as over the 2007‒2009 financial turmoil, while there is a transition to the low volatility regime as they approach the accession to the EU in 2004. It is also shown that the capital flows liberalization process has resulted in an increase in volatility of stock returns in most cases.
We discuss the empirical importance of long term cyclical effects in the volatility of financial returns. Following Amado and Teräsvirta (2009), ČiŽek and Spokoiny (2009) and others, we consider a general conditionally heteroscedastic process with stationarity property distorted by a deterministic function that governs the possible time variability of the unconditional variance. The function proposed in this paper can be interpreted as a finite Fourier approximation of an Almost Periodic (AP) function as defined by Corduneanu (1989). The resulting model has a particular form of a GARCH process with time varying parameters, intensively discussed in the recent literature.
In the empirical analyses we apply a generalisation of the Bayesian AR(1)-GARCH model for daily returns of S&P500, covering the period of sixty years of US postwar economy, including the recently observed global financial crisis. The results of a formal Bayesian model comparison clearly indicate the existence of significant long term cyclical patterns in volatility with a strongly supported periodic component corresponding to a 14 year cycle. Our main results are invariant with respect to the changes of the conditional distribution from Normal to Student-tand to the changes of the volatility equation from regular GARCH to the Asymmetric GARCH.
In this paper we present a copula-based model for a binary and a continuous variable in a time series setup. Within this modeling framework both marginals can be equipped with their own dynamics whereas the contemporaneous dependence between both processes can be flexibly captured via a copula function. We propose a method for testing the goodness-of-fit of such a time series model using probability integral transforms (PIT). This verification procedure allows not only a verification of the goodness-of-fit of the estimated marginal distribution for a continuous variable but also the conditional distribution of a continuous variable given the outcome of its binary counterpart (i.e. the adequacy of the copula choice). We test the model on an empirical example: investigating the relationship between trading volume and the indicators of arbitrarily ’large’ price movements on the interbank EUR/PLN spot market.
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