Thèses sur le sujet « Financial markets – European Union countries »

In the first chapter of this thesis, the generalized dynamic factor model of Forni et. al (2002) is employed to explore the predictive content of the asset returns in forecasting Consumer Price Index (CPI) inflation and the growth rate of Industrial Production (IP). The connection between stock markets and economic growth is well known. In the fundamental valuation of equity, the stock price is equal to the discounted future streams of expected dividends. Since the future dividends are related to future growth, a revision of prices, and hence returns, should signal movements in the future growth path. Though other important transmission channels, such as the Tobin's q theory (Tobin, 1969), the wealth effect as well as capital market imperfections, have been widely studied in this literature. I show that an aggregate index, such as the S&P500, could be misleading if used as a proxy for the informative content of the stock market as a whole. Despite the widespread wisdom of considering such index as a leading variable, only part of the assets included in the composition of the index has a leading behaviour with respect to the variables of interest. Its forecasting performance might be poor, leading to sceptical conclusions about the effectiveness of asset prices in forecasting macroeconomic variables. The main idea of the first essay is therefore to analyze the lead-lag structure of the assets composing the S&P500. The classification in leading, lagging and coincident variables is achieved by means of the cross correlation function cleaned of idiosyncratic noise and short run fluctuations. I assume that asset returns follow a factor structure. That is, they are the sum of two parts: a common part driven by few shocks common to all the assets and an idiosyncratic part, which is rather asset specific. The correlation

function, computed on the common part of the series, is not affected by the assets' specific dynamics and should provide information only on the series driven by the same common factors. Once the leading series are identified, they are grouped within the economic sector they belong to. The predictive content that such aggregates have in forecasting IP growth and CPI inflation is then explored and compared with the forecasting power of the S&P500 composite index. The forecasting exercise is addressed in the following way: first, in an autoregressive (AR) model I choose the truncation lag that minimizes the Mean Square Forecast Error (MSFE) in 11 years out of sample simulations for 1, 6 and 12 steps ahead, both for the IP growth rate and the CPI inflation. Second, the S&P500 is added as an explanatory variable to the previous AR specification. I repeat the simulation exercise and find that there are very small improvements of the MSFE statistics. Third, averages of stock return leading series, in the respective sector, are added as additional explanatory variables in the benchmark regression. Remarkable improvements are achieved with respect to the benchmark specification especially for one year horizon forecast. Significant improvements are also achieved for the shorter forecast horizons, when the leading series of the technology and energy sectors are used.

The second chapter of this thesis disentangles the sources of aggregate risk and measures the extent of co-movements in five European stock markets. Based on the static factor model of Stock and Watson (2002), it proposes a new method for measuring the impact of international, national and industry-specific shocks. The process of European economic and monetary integration with the advent of the EMU has been a central issue for investors and policy makers. During these years, the number of studies on the integration and linkages among European stock markets has increased enormously. Given their forward looking nature, stock prices are considered a key variable to use for establishing the developments in the economic and financial markets. Therefore, measuring the extent of co-movements between European stock markets has became, especially over the last years, one of the main concerns both for policy makers, who want to best shape their policy responses, and for investors who need to adapt their hedging strategies to the new political and economic environment. An optimal portfolio allocation strategy is based on a timely identification of the factors affecting asset returns. So far, literature dating back to Solnik (1974) identifies national factors as the main contributors to the co-variations among stock returns, with the industry factors playing a marginal role. The increasing financial and economic integration over the past years, fostered by the decline of trade barriers and a greater policy coordination, should have strongly reduced the importance of national factors and increased the importance of global determinants, such as industry determinants. However, somehow puzzling, recent studies demonstrated that countries sources are still very important and generally more important of the industry ones. This paper tries to cast some light on these conflicting results. The chapter proposes an econometric estimation strategy more flexible and suitable to disentangle and measure the impact of global and country factors. Results point to a declining influence of national determinants and to an increasing influence of the industries ones. The international influences remains the most important driving forces of excess returns. These findings overturn the results in the literature and have important implications for strategic portfolio allocation policies; they need to be revisited and adapted to the changed financial and economic scenario.

The third chapter presents a new stylized fact which can be helpful for discriminating among alternative explanations of the U.S. macroeconomic stability. The main finding is that the fall in time series volatility is associated with a sizable decline, of the order of 30% on average, in the predictive accuracy of several widely used forecasting models, included the factor models proposed by Stock and Watson (2002). This pattern is not limited to the measures of inflation but also extends to several indicators of real economic activity and interest rates. The generalized fall in predictive ability after the mid-1980s is particularly pronounced for forecast horizons beyond one quarter. Furthermore, this empirical regularity is not simply specific to a single method, rather it is a common feature of all models including those used by public and private institutions. In particular, the forecasts for output and inflation of the Fed's Green book and the Survey of Professional Forecasters (SPF) are significantly more accurate than a random walk only before 1985. After this date, in contrast, the hypothesis of equal predictive ability between naive random walk forecasts and the predictions of those institutions is not rejected for all horizons, the only exception being the current quarter. The results of this chapter may also be of interest for the empirical literature on asymmetric information. Romer and Romer (2000), for instance, consider a sample ending in the early 1990s and find that the Fed produced more accurate forecasts of inflation and output compared to several commercial providers. The results imply that the informational advantage of the Fed and those private forecasters is in fact limited to the 1970s and the beginning of the 1980s. In contrast, during the last two decades no forecasting model is better than "tossing a coin" beyond the first quarter horizon, thereby implying that on average uninformed economic agents can effectively anticipate future macroeconomics developments. On the other hand, econometric models and economists' judgement are quite helpful for the forecasts over the very short horizon, that is relevant for conjunctural analysis. Moreover, the literature on forecasting methods, recently surveyed by Stock and Watson (2005), has devoted a great deal of attention towards identifying the best model for predicting inflation and output. The majority of studies however are based on full-sample periods. The main findings in the chapter reveal that most of the full sample predictability of U.S. macroeconomic series arises from the years before 1985. Long time series appear

to attach a far larger weight on the earlier sub-sample, which is characterized by a larger volatility of inflation and output. Results also suggest that some caution should be used in evaluating the performance of alternative forecasting models on the basis of a pool of different sub-periods as full sample analysis are likely to miss parameter instability.

The fourth chapter performs a detailed forecast comparison between the static factor model of Stock and Watson (2002) (SW) and the dynamic factor model of Forni et. al. (2005) (FHLR). It is not the first work in performing such an evaluation. Boivin and Ng (2005) focus on a very similar problem, while Stock and Watson (2005) compare the performances of a larger class of predictors. The SW and FHLR methods essentially differ in the computation of the forecast of the common component. In particular, they differ in the estimation of the factor space and in the way projections onto this space are performed. In SW, the factors are estimated by static Principal Components (PC) of the sample covariance matrix and the forecast of the common component is simply the projection of the predicted variable on the factors. FHLR propose efficiency improvements in two directions. First, they estimate the common factors based on Generalized Principal Components (GPC) in which observations are weighted according to their signal to noise ratio. Second, they impose the constraints implied by the dynamic factors structure when the variables of interest are projected on the common factors. Specifically, they take into account the leading and lagging relations across series by means of principal components in the frequency domain. This allows for an efficient aggregation of variables that may be out of phase. Whether these efficiency improvements are helpful to forecast in a finite sample is however an empirical question. Literature has not yet reached a consensus. On the one hand, Stock and Watson (2005) show that both methods perform similarly (although they focus on the weighting of the idiosyncratic and not on the dynamic restrictions), while Boivin and Ng (2005) show that SW's method largely outperforms the FHLR's and, in particular, conjecture that the dynamic restrictions implied by the method are harmful for the forecast accuracy of the model. This chapter tries to shed some new light on these conflicting results. It

focuses on the Industrial Production index (IP) and the Consumer Price Index (CPI) and bases the evaluation on a simulated out-of sample forecasting exercise. The data set, borrowed from Stock and Watson (2002), consists of 146 monthly observations for the US economy. The data spans from 1959 to 1999. In order to isolate and evaluate specific characteristics of the methods, a procedure, where the

two non-parametric approaches are nested in a common framework, is designed. In addition, for both versions of the factor model forecasts, the chapter studies the contribution of the idiosyncratic component to the forecast. Other non-core aspects of the model are also investigated: robustness with respect to the choice of the number of factors and variable transformations. Finally, the chapter performs a sub-sample performances of the factor based forecasts. The purpose of this exercise is to design an experiment for assessing the contribution of the core characteristics of different models to the forecasting performance and discussing auxiliary issues. Hopefully this may also serve as a guide for practitioners in the field. As in Stock and Watson (2005), results show that efficiency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts, but, in contrast to Boivin and Ng (2005), it is shown that the dynamic restrictions imposed by the procedure of Forni et al. (2005) are not harmful for predictability. The main conclusion is that the two methods have a similar performance and produce highly collinear forecasts.

Doctorat en sciences économiques, Orientation économie
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The first essay, analysed in the first chapter of the thesis, contributes to the literature on Efficient Market Hypothesis and in particular in understanding several issues associated with how prices are determined for individual stocks. The chapter, in particular, provides further evidence of price and volume effects associated with index compositional changes by analysing the inclusions (exclusions) from the French CAC40 and SBF120 indices, as well as the FTSE100. I find evidence supporting the price pressure hypothesis associated with index fund rebalancing, but weak or no evidence for the imperfect substitution, liquidity and information hypotheses. The results improve on recent evidence from the S&P500 index. The evidence for the FTSE100 additions shows, in particular, that markets learn about an imminent inclusion and incorporate this information into prices, even before the announcement date.

The other two essays of this thesis relate to Corporate Governance issues. Chapters 2 and 3, in particular, analyze some aspects of two corporate governance mechanisms: ownership concentration and managerial labour market.

Chapter 2 provides an overview of the evolution of control in listed Slovenian corporations and evaluates the impact of the current changes in ownership on firm performance. Ownership and control has been concentrating in most transition countries. This consolidation of control introduces changes in the power distribution within privatised firms and, most importantly, redirects the corporate governance problem to a conflict between large and small shareholders. The chapter evaluate the ownership changes in Slovenian privatised firms through an analysis of stock price reactions to the entrance of a new blockholder (the shared benefits of control) and through an estimation of the premiums paid for large blocks (the private benefits of control). It provides evidence and discuss the reasons for the failures of the privatization investment funds in implementing control over firm managers and in promoting the restructuring of firms in the first post-privatization years.

Chapter 3 concentrates on one specific aspect of the managerial labour market: monetary remuneration schemes. The purpose of this chapter is to examine the interconnection between pay and corporate governance approaches with respect to the different rules found across European legal systems. The research data on reported pay practices for 2001 among FTSE Eurotop300 companies reveal a reliance on performance-based pay generally and a somewhat variable adoption of share options programs and other equity-based incentive contracts, which generate difficulties in dispersed ownership systems. Furthermore, on the basis of the regulation on executive remuneration disclosure discussed in this chapter and on the basis of the disclosure practices resulting from the data collected for the FTSE Eurotop300 constituents, I construct some disclosure indicators and analyse empirically how country and firm characteristics affect remuneration disclosure.

Doctorat en sciences économiques, Orientation économie
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is composed of three chapters. The first two chapters are on nowcasting,

a topic that has received an increasing attention both among practitioners and

the academics especially in conjunction and in the aftermath of the 2008-2009

economic crisis. At the heart of the two chapters is the idea of exploiting the

information from data published at a higher frequency for obtaining early estimates

of the macroeconomic variable of interest. The models used to compute

the nowcasts are dynamic models conceived for handling in an efficient way

the characteristics of the data used in a real-time context, like the fact that due to the different frequencies and the non-synchronicity of the releases

the time series have in general missing data at the end of the sample. While

the first chapter uses a small model like a VAR for nowcasting Italian GDP,

the second one makes use of a dynamic factor model, more suitable to handle

medium-large data sets, for providing early estimates of the employment in

the euro area. The third chapter develops a topic only marginally touched

by the second chapter, i.e. the estimation of dynamic factor models on data characterized by block-structures.

The firrst chapter assesses the accuracy of the Italian GDP nowcasts based

on a small information set consisting of GDP itself, the industrial production

index and the Economic Sentiment Indicator. The task is carried out by using

real-time vintages of data in an out-of-sample exercise over rolling windows

of data. Beside using real-time data, the real-time setting of the exercise is

also guaranteed by updating the nowcasts according to the historical release calendar. The model used to compute the nowcasts is a mixed-frequency Vector

Autoregressive (VAR) model, cast in state-space form and estimated by

maximum likelihood. The results show that the model can provide quite accurate

early estimates of the Italian GDP growth rates not only with respect

to a naive benchmark but also with respect to a bridge model based on the

same information set and a mixed-frequency VAR with only GDP and the industrial production index.

The chapter also analyzes with some attention the role of the Economic Sentiment

Indicator, and of soft information in general. The comparison of our

mixed-frequency VAR with one with only GDP and the industrial production

index clearly shows that using soft information helps obtaining more accurate

early estimates. Evidence is also found that the advantage from using soft

information goes beyond its timeliness.

In the second chapter we focus on nowcasting the quarterly national account

employment of the euro area making use of both country-specific and

area wide information. The relevance of anticipating Eurostat estimates of

employment rests on the fact that, despite it represents an important macroeconomic

variable, euro area employment is measured at a relatively low frequency

(quarterly) and published with a considerable delay (approximately

two months and a half). Obtaining an early estimate of this variable is possible

thanks to the fact that several Member States publish employment data and

employment-related statistics in advance with respect to the Eurostat release

of the euro area employment. Data availability represents, nevertheless, a

major limit as country-level time series are in general non homogeneous, have

different starting periods and, in some cases, are very short. We construct a

data set of monthly and quarterly time series consisting of both aggregate and

country-level data on Quarterly National Account employment, employment

expectations from business surveys and Labour Force Survey employment and

unemployment. In order to perform a real time out-of-sample exercise simulating

the (pseudo) real-time availability of the data, we construct an artificial

calendar of data releases based on the effective calendar observed during the first quarter of 2012. The model used to compute the nowcasts is a dynamic

factor model allowing for mixed-frequency data, missing data at the beginning

of the sample and ragged edges typical of non synchronous data releases. Our

results show that using country-specific information as soon as it is available

allows to obtain reasonably accurate estimates of the employment of the euro

area about fifteen days before the end of the quarter.

We also look at the nowcasts of employment of the four largest Member

States. We find that (with the exception of France) augmenting the dynamic

factor model with country-specific factors provides better results than those

obtained with the model without country-specific factors.

The third chapter of the thesis deals with dynamic factor models on data

characterized by local cross-correlation due to the presence of block-structures.

The latter is modeled by introducing block-specific factors, i.e. factors that

are specific to blocks of time series. We propose an algorithm to estimate the model by (quasi) maximum likelihood and use it to run Monte Carlo

simulations to evaluate the effects of modeling or not the block-structure on

the estimates of common factors. We find two main results: first, that in finite samples modeling the block-structure, beside being interesting per se, can help

reducing the model miss-specification and getting more accurate estimates

of the common factors; second, that imposing a wrong block-structure or

imposing a block-structure when it is not present does not have negative

effects on the estimates of the common factors. These two results allow us

to conclude that it is always recommendable to model the block-structure

especially if the characteristics of the data suggest that there is one.
Doctorat en Sciences économiques et de gestion
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