Using irregularly spaced returns to estimate multi-factor models: application to Brazilian equity data

Multi-factor models constitute a useful tool to explain cross-sectional covariance in equities returns. We propose in this paper the use of irregularly spaced returns in the multi-factor model estimation and provide an empirical example with the 389 most liquid equities in the Brazilian Market. The market index shows itself significant to explain equity returns while the US$/Brazilian Real exchange rate and the Brazilian standard interest rate does not. This example shows the usefulness of the estimation method in further using the model to fill in missing values and to provide interval forecasts.

Using irregularly spaced returns to estimate multi-factor models : application to Brazilian equity data

Multi-factor models constitute a use fui tool to explain cross-sectional covariance in equities retums. We propose in this paper the use of irregularly spaced returns in the multi-factor model estimation and provide an empirical example with the 389 most liquid equities in the Brazilian Market. The market index shows itself significant to explain equity returns while the US$/Brazilian Real exchange rate and the Brazilian standard interest rate does not. This example shows the usefulness of the estimation method in further using the model to fill in missing values and to provide intervaI forecasts.

Simulation-based smoothing and filtering in factor stochastic volatility models : two econometric applications

In this article we use factor models to describe a certain class of covariance structure for financiaI time series models. More specifical1y, we concentrate on situations where the factor variances are modeled by a multivariate stochastic volatility structure. We build on previous work by allowing the factor loadings, in the factor mo deI structure, to have a time-varying structure and to capture changes in asset weights over time motivated by applications with multi pIe time series of daily exchange rates. We explore and discuss potential extensions to the models exposed here in the prediction area. This discussion leads to open issues on real time implementation and natural model comparisons.

Simulation -based smoothing and filtering in factor stochastic volatility models : two econometric applications (July-2001)

The past decade has wítenessed a series of (well accepted and defined) financial crises periods in the world economy. Most of these events aI,"e country specific and eventually spreaded out across neighbor countries, with the concept of vicinity extrapolating the geographic maps and entering the contagion maps. Unfortunately, what contagion represents and how to measure it are still unanswered questions. In this article we measure the transmission of shocks by cross-market correlation\ coefficients following Forbes and Rigobon's (2000) notion of shift-contagion,. Our main contribution relies upon the use of traditional factor model techniques combined with stochastic volatility mo deIs to study the dependence among Latin American stock price indexes and the North American indexo More specifically, we concentrate on situations where the factor variances are modeled by a multivariate stochastic volatility structure. From a theoretical perspective, we improve currently available methodology by allowing the factor loadings, in the factor model structure, to have a time-varying structure and to capture changes in the series' weights over time. By doing this, we believe that changes and interventions experienced by those five countries are well accommodated by our models which learns and adapts reasonably fast to those economic and idiosyncratic shocks. We empirically show that the time varying covariance structure can be modeled by one or two common factors and that some sort of contagion is present in most of the series' covariances during periods of economical instability, or crisis. Open issues on real time implementation and natural model comparisons are thoroughly discussed.

Constructing common-factor portfolios

In this paper we construct common-factor portfolios using a novel linear transformation of standard factor models extracted from large data sets of asset returns. The simple transformation proposed here keeps the basic properties of the usual factor transformations, although some new interesting properties are further attached to them. Some theoretical advantages are shown to be present. Also, their practical importance is confirmed in two applications: the performance of common-factor portfolios are shown to be superior to that of asset returns and factors commonly employed in the finance literature.

Detectando não-linearidades nos retornos dos fundos multimercados

Estudos recentes apontam que diversas estratégias implementadas em hedge funds geram retornos com características não lineares. Seguindo as sugestões encontradas no paper de Agarwal e Naik (2004), este trabalho mostra que uma série de hedge funds dentro da indústria de fundos de investimentos no Brasil apresenta retornos que se assemelham ao de uma estratégia em opções de compra e venda no índice de mercado Bovespa. Partindo de um modelo de fatores, introduzimos um índice referenciado no retorno sobre opções de modo que tal fator possa explicar melhor que os tradicionais fatores de risco a característica não linear dos retornos dos fundos de investimento.

Common factors in Latin America's business cycles

This paper constructs new business cycle indices for Argentina, Brazil, Chile, and Mexico based on common dynamic factors extracted from a comprehensive set of sectoral output, external trade, fiscal and financial variables. The analysis spans the 135 years since the insertion of these economies into the global economy in the 1870s. The constructed indices are used to derive a business cyc1e chronology for these countries and characterize a set of new stylized facts. In particular, we show that ali four countries have historically displayed a striking combination of high business cyc1e volatility and persistence relative to advanced country benchmarks. Volatility changed considerably over time, however, being very high during early formative decades through the Great Depression, and again during the 1970s and ear1y 1980s, before declining sharply in three of the four countries. We also identify a sizeable common factor across the four economies which variance decompositions ascribe mostly to foreign interest rates and shocks to commodity terms of trade.

Um estudo sobre a capacidade de gestores de fundos multigestor adicionarem valor aos cotistas

Com o aumento do número de gestores especializados em um número cada vez maior de possibilidades de investimentos na indústria de fundos brasileira, os fundos Multigestor se tornaram uma alternativa para os investidores que procuram diversificar seus investimentos e delegam às instituições financeiras o trabalho de alocar os recursos dentro das diferentes estratégias e fundos existentes no mercado. O intuito deste estudo é avaliar a capacidade de gerar retornos anormais (alfa) dos fundos de fundos da indústria brasileira, classificados como Fundos Multimercados Multigestor. Para isso foi estudada uma amostra com 1.421 fundos Multigestor com tributação de Longo Prazo no período de janeiro de 2005 a dezembro de 2011. A análise dos resultados encontrados através de regressões de modelos de vários fatores, derivados do modelo de Jensen (1968), sugere que apenas 3,03% dos fundos estudados conseguem adicionar valor a seus cotistas. Foram estudadas ainda as três principais fontes potenciais de geração de alfa dos fundos de fundos, a escolha das estratégias que compõe a carteira do fundo (alocação estratégica), a antecipação de movimentos de mercado (market timing) e a capacidade selecionar os melhores fundos dentro de cada estratégia (seleção de fundos). A partir da inclusão de termos quadráticos, conforme proposto pelos modelos de Treynor e Mazuy (1966) pode-se verificar que os fundos Multigestor, em média, não conseguem adicionar valor tentando antecipar movimentos de mercado (market timing). Através da construção de uma variável explicativa com a composição estratégica de cada fundo da amostra em cada período de tempo, pode-se verificar que os gestores de fundos de fundos, em média, também fracassam ao tentar selecionar os melhores fundos/gestores da indústria. Já a escolha das estratégias que compõe a carteira do fundo (alocação estratégica) mostrou contribuir positivamente para o retorno dos fundos. Ainda foi avaliada a capacidade de gerar alfa antes dos custos, o que elevou o percentual de fundos com alfa positivo para 6,39% dos fundos estudados, mas foi incapaz de alterar o sinal do alfa médio, que permaneceu negativo.

A estrutura hierárquica das variações de preços e o núcleo da inflação

The approach proposed here explores the hierarchical nature of item-level data on price changes. On one hand, price data is naturally organized around a regional strucuture, with variations being observed on separate cities. Moreover, the itens that comprise the natural structure of CPIs are also normally interpreted in terms of groups that have economic interpretations, such as tradables and non-tradables, energyrelated, raw foodstuff, monitored prices, etc. The hierarchical dynamic factor model allow the estimation of multiple factors that are naturally interpreted as relating to each of these regional and economic levels.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we can model the heteroskedasticity of a linear combination of the errors. We show that this assumption can be satisfied without imposing strong assumptions on the errors in common DID applications. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative inference method that relies on strict stationarity and ergodicity of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment periods. We extend our inference methods to linear factor models when there are few treated groups. We also derive conditions under which a permutation test for the synthetic control estimator proposed by Abadie et al. (2010) is robust to heteroskedasticity and propose a modification on the test statistic that provided a better heteroskedasticity correction in our simulations.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we know how the heteroskedasticity is generated, which is the case when it is generated by variation in the number of observations per group. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative application of our method that relies on assumptions about stationarity and convergence of the moments of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment groups. We extend our inference method to linear factor models when there are few treated groups. We also propose a permutation test for the synthetic control estimator that provided a better heteroskedasticity correction in our simulations than the test suggested by Abadie et al. (2010).

Tests of conditional asset pricing models in the brazilian stock market

In this paper, we test a version of the conditional CAPM with respect to a local market portfolio, proxied by the Brazilian stock index during the period 1976-1992. We also test a conditional APT modeI by using the difference between the 3-day rate (Cdb) and the overnight rate as a second factor in addition to the market portfolio in order to capture the large inflation risk present during this period. The conditional CAPM and APT models are estimated by the Generalized Method of Moments (GMM) and tested on a set of size portfolios created from individual securities exchanged on the Brazilian markets. The inclusion of this second factor proves to be important for the appropriate pricing of the portfolios.

The Capm and Fama-French models in Brazil: a comparative study

This paper confronts the Capital Asset Pricing Model - CAPM - and the 3-Factor Fama-French - FF - model using both Brazilian and US stock market data for the same Sample period (1999-2007). The US data will serve only as a benchmark for comparative purposes. We use two competing econometric methods, the Generalized Method of Moments (GMM) by (Hansen, 1982) and the Iterative Nonlinear Seemingly Unrelated Regression Estimation (ITNLSUR) by Burmeister and McElroy (1988). Both methods nest other options based on the procedure by Fama-MacBeth (1973). The estimations show that the FF model fits the Brazilian data better than CAPM, however it is imprecise compared with the US analog. We argue that this is a consequence of an absence of clear-cut anomalies in Brazilian data, specially those related to firm size. The tests on the efficiency of the models - nullity of intercepts and fitting of the cross-sectional regressions - presented mixed conclusions. The tests on intercept failed to rejected the CAPM when Brazilian value-premium-wise portfolios were used, contrasting with US data, a very well documented conclusion. The ITNLSUR has estimated an economically reasonable and statistically significant market risk premium for Brazil around 6.5% per year without resorting to any particular data set aggregation. However, we could not find the same for the US data during identical period or even using a larger data set. Este estudo procura contribuir com a literatura empírica brasileira de modelos de apreçamento de ativos. Dois dos principais modelos de apreçamento são Infrontados, os modelos Capital Asset Pricing Model (CAPM)e de 3 fatores de Fama-French. São aplicadas ferramentas econométricas pouco exploradas na literatura nacional na estimação de equações de apreçamento: os métodos de GMM e ITNLSUR. Comparam-se as estimativas com as obtidas de dados americanos para o mesmo período e conclui-se que no Brasil o sucesso do modelo de Fama e French é limitado. Como subproduto da análise, (i) testa-se a presença das chamadas anomalias nos retornos, e (ii) calcula-se o prêmio de risco implícito nos retornos das ações. Os dados revelam a presença de um prêmio de valor, porém não de um prêmio de tamanho. Utilizando o método de ITNLSUR, o prêmio de risco de mercado é positivo e significativo, ao redor de 6,5% ao ano.

An empirical comparison of forward-rate and spot-rate models for valuing interest-rate options

Our main goal is to investigate the question of which interest-rate options valuation models are better suited to support the management of interest-rate risk. We use the German market to test seven spot-rate and forward-rate models with one and two factors for interest-rate warrants for the period from 1990 to 1993. We identify a one-factor forward-rate model and two spot-rate models with two faetors that are not significant1y outperformed by any of the other four models. Further rankings are possible if additional cri teria are applied.

Technical change and factor shares

The implications of technical change that directly alters factor shares are examined. Such change can lower the income of some factors of production even when it raises total output, thus offering a possible explanation for episodes of social conflict such as the Luddite uprisings in 19th century England and the recent divergence in the U. S. between wages for skilled and unskilled labor. An explanation also why underdeveloped countries do not adopt the latest technology but continue to use outmoded production methods. Total factor productivity is shown to be a misleading measure of technical progress. Share-altering technical change brings into question the plausibility of a wide class of endogenous growth models.