Finance and accounting are two research fields in management that have witnessed very important methodological developments in recent years. For example in Finance, the sophistication of the financial industry requires advanced models to solve problems of portfolio and risk management. Despite the progress, practioneers still face many challenges. For instance, it is well known that financial and economic variables present upward and downward trends. A simple but actual illustration is to think about recessions and expansions or in bull and bear markets. An econometric tool that has been developed to address market regimes is the regime switching model (RSM). It has been applied successfully to many time series data such as interest rates and stock returns. Regime switching models have nevertheless shortcomes. First, because it requires complex maximum likelihood optimization procedures, when applied to the joint estimation of many variables the number of estimated parameters easily reaches dozens, becoming unfeasible to estimate. Moreover, when applied to panel data (for example, more than one stock market indices) RSM assumes by construction the same regime at a given time point to all observed values, not allowing differentiating diverse market behaviour.

Recently, the RSM have been extended to allow for different regime switching of variables (recognizing heterogeneity in variables) to panel data (Dias et al. (2008, 2009)). However, when applying this methodology to finance and accounting variables, it does not account some well known patterns in financial data like persistence and volatility clustering. This project aims to extend previous developments by incorporating a Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) structure in modeling the conditional variances with clustering and regime switching, simultaneously. Both types of models allow capturing general nonlinear structures in data by incorporating unobserved heterogeneity (and structural breaks in time series). This approach can deal easily with the specific features of financial and economic time series data, such as asymmetry, kurtosis, nonlinearities, volatility clustering, overcoming problems of linear time series models, which cannot capture these stylized facts, so common in financial economics.

Apart from the theoretical development, the applied tasks illustrate the advantages of the proposed methodology. They will illustrate how this methodology can be easily implemented and provide answers to several important questions in finance and accounting.

First, we will look at the homogeneity of classifications of industries used in finance and accounting research as well as in the financial industry. We want to validate whether current industry classification schemes provide a consistent classification in terms of industry cycles (regimes) as they are currently used by portfolio and risk managers.

Second, we explore the debate within the financial literature between geographical versus industrial diversification. This debate became livelier with events such as the deregulation of markets and the elimination of international barriers to capital movements which are considered catalysts of market integration and thus affect the dominance of country factors. The prime example is the European Monetary Union (EMU). Studies have provided contradictory evidence on the effect of EMU. Whereas some provide evidence that industry factors are already more important than country factors (Cavaglia et al., 2000), others suggest that only industry effects are becoming increasingly important while countries are losing explanatory power (Baca et al., 2000). Our goal is to introduce a new methodology based on RSM and provide answers to the debate.

The third application is in accounting and deals with measures of the quality of financial information using attributes of earnings such as accruals quality, persistence, predictability, smoothness, value relevance and timeliness. Francis et al (2004) for example used a time series of 30 years for each firm but looked only at average effects disregarding that information quality may have changed over time. This application examines how the quality of financial information clusters around

firm-specific characteristics such as size, ownership and board structure, industry group, taking into account regime switching over time and unobserved heterogeneity across firms.

Overall, the project provides important methodological advances that will overcome current methodological limitations by introducing models that account for market regimes and unobserved heterogeneity. Many areas of research considered in this project are of direct interest to the private sector, economic policy and decision makers.

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