Volume 11, Issue 2 -

Vine copulas are multivariate dependence models constructed from pair-copulas (bivariate copulas). In this paper, we allow the dependence parameters of the pair-copulas in a D-vine decomposition to be potentially time-varying, following a restricted ARMA(1, m) process, in order to obtain a very flexible dependence model for applications to multivariate financial return data. We investigate the dependence among the broad stock market indexes from Germany (DAX), France (CAC 40), Britain (FTSE 100), the United States (S&P 500) and Brazil (IBOVESPA) both in a crisis and in a non-crisis period. We find evidence of stronger dependence among the indexes in bear markets. Surprisingly, though, the dynamic D-vine copula indicates the occurrence of a sharp decrease in dependence between the indexes FTSE and CAC in the beginning of 2011, and also between CAC and DAX during mid-2011 and in the beginning of 2008, suggesting the absence of contagion in these cases. We evaluate the dynamic D-vine copula with respect to Value-at-Risk (VaR) forecasting accuracy in crisis periods. The dynamic D-vine outperforms the static D-vine in terms of predictive accuracy for our real data sets. We also investigate the dynamic D-vine copula in a simulation study and the overall results of the Monte Carlo experiments are quite favorable to the dynamic D-vine copula in comparison with a static D-vine copula.

This study employs four (4) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) variants namely GARCH (1, 1), Glosten–Jagannathan–Runkle (GJR), Auto Regressive Integrated Moving Average (ARIMA)-GARCH and ARIMA-GJR as benchmark models to assess the performance of a proposed novel Gene Expression Programming (GEP) based univariate time series modeling approach used to conduct ex ante oil price volatility forecasts. The report illustrates that the GEP model is more superior to any of the traditional models on issues relating to both loss functions applied. The GEP model is of a greater volatility forecasting precision at different forecast horizons, therefore. There is also the existence of evidence that GJR and ARIMA-GJR differ in their loss functions, the performance is nevertheless better than GARCH (1, 1) and ARIMA-GARCH. This study conducted herein achieves importance in literature by broadening the application of gene algorithms in finance and forecasting. It also solves the problem of high error associated with the use of GARCH related models in oil price volatility forecasting.

Party ideology, elections and economic performance can have a significant impact on the overall economic performance. Governments are formed by parties that compete at elections and, based on their ideology, have different preferences regarding the size and scope of government. With respect to economic policy, left-wing parties advocate for government intervention in order to ease the effects of the business cycle whilst right-wing parties favour market solutions as a response to economic fluctuations. According to the partisan theory, left-wing parties are more willing to adopt expansionary fiscal and monetary policies when in government, as they are mainly concerned with employment. On the opposite side, right-wing parties prioritize inflation and fiscal discipline as objectives. Both left-wing and right-wing parties understand the impact that the economy has on their re-election prospects. When in government, in order to maximize their probability of being re-elected, all parties will try and make voters believe that their economic policies were successful. How the electorate will react to this behaviour depends on its ability to understand and predict the impact of current policies on future welfare. In this paper we examine the influence of government ideology and elections on the economy. Using quarterly data for government consumption, money supply, taxation and welfare expenditure, we find that both partisan and opportunistic political cycles characterize Australian politics thus confirming the insights put forward by Nordhaus, W.D. (1975. “The Political Business Cycle.” The Review of Economic Studies 42 (2): 169–190.) and Hibbs, D. (1977. “Political Parties and Macroeconomic Policy.” The American Political Science Review 7: 1467–1487).

Motivated by the risk inherent in intraday investing, we propose several ways of quantifying extremal behavior of a time series of curves. A curve can be extreme if it has shape and/or magnitude much different than the bulk of observed curves. Our approach is at the nexus of functional data analysis and extreme value theory. The risk measures we propose allow us to assess probabilities of observing extreme curves not seen in a historical record. These measures complement risk measures based on point-to-point returns, but have different interpretation and information content. Using our approach, we study how the financial crisis of 2008 impacted the extreme behavior of intraday cumulative return curves. We discover different impacts on shares in important sectors of the US economy. The information our analysis provides is in some cases different from the conclusions based on the extreme value analysis of daily closing price returns.