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This paper presents evidence for a significantly positive link between the dynamic conditional beta and the cross section of daily stock returns. An investment strategy that takes a long position in stocks in the highest conditional beta decile and a short position in stocks in the lowest conditional beta decile produces average returns and alphas in the range of 0.60%–0.80% per month. We provide an investor attention-based explanation of this finding. We show that stocks with high conditional beta have strong attention-grabbing characteristics, leading to a higher fraction of buyer-initiated trades for these stocks. We also find that stocks recently bought perform significantly better than stocks recently sold. Hence, the high beta stocks that investors are more likely to buy have higher expected returns than the low beta stocks that investors are more likely to sell. This paper was accepted by Lauren Cohen, finance .

ABSTRACT Empirical studies have revealed that the conditional Capital Asset Pricing Model (CAPM) has a higher explanatory power than its unconditional version, particularly for the model in state-space form where the beta is estimated using Kalman filter. Most empirical analyses are based on stock portfolios to explain financial anomalies, but only a few studies proposed improving investment fund performance. The main contribution of this study is the assessment of Brazilian investment funds through traditional measures estimated from the CAPM model in state-space form with heteroscedastic and homoscedastic errors compared to alternative models, such as the unconditional CAPM and a four-factor model. Using a sample of stock funds from May 2005-April 2015, the results indicate that the conditional CAPM model produces better results than the alternative models, providing better performance evaluation practices for funds in both stock-picking and market-timing ability. RESUMO Os resultados empíricos na literatura demonstram que a versão condicional do Modelo de Precificação de Ativos Financeiros (CAPM), particularmente no que se refere ao modelo na forma em espaço de estado, no qual o beta é estimado pelo filtro de Kalman, possui maior poder explicativo do que a sua versão incondicional. A maioria das análises empíricas na literatura baseia-se em portfólios de ações para explicar anomalias financeiras, porém poucos estudos propõem-se a melhorar a avaliação de desempenho de fundos de investimento. A principal contribuição deste artigo consiste em avaliar fundos de investimento brasileiros por meio de medidas tradicionais estimadas a partir do CAPM na forma em espaço de estado com erros heteroscedásticos e homoscedásticos e comparar seus resultados com modelos alternativos, tais como o CAPM incondicional e o modelo de quatro fatores. Utilizando uma amostra de fundos de ações, os resultados indicam que o modelo CAPM condicional produz melhores resultados do que os modelos alternativos, proporcionando melhores práticas de avaliação de desempenho em relação às habilidades de stock-picking e market-timing. RESUMEN Los resultados empíricos en la literatura revelan que la versión condicional del CAPM, particularmente con respecto al modelo en forma de espacio de estado, en el cual se estima beta mediante el filtro de Kalman, posee mayor poder explicativo que su versión incondicional. La mayoría de los análisis empíricos se basan en carteras de valores para explicar anomalías financieras, pero pocos estudios proponen mejorar el rendimiento de los fondos de inversión. La principal contribución de este estudio a la literatura es que lleva a cabo la evaluación de fondos de inversión a través de medidas condicionales generadas a partir del CAPM en forma espacio-estado con errores heteroscedásticos y homoscedásticos y que compara sus resultados con modelos alternativos, tales como CAPM incondicional, modelo de cuatro factores. Utilizando una muestra de fondos de acciones, los resultados indican que el modelo CAPM condicional produce mejores resultados que los modelos alternativos, proporcionando mejores prácticas de evaluación de desempeño en relación con las habilidades de stock-picking y market-timing.

The conditional capital asset pricing model (CAPM) theory postulates that the systematic risk ( Ø ) of an asset or portfolio varies over time. Several dynamics are thus given to systematic risk in the literature. This article looks for the dynamic that seems to best explain the returns of the assets of the Regional Stock Exchange of West Africa (BRVM) by comparing two dynamics: one by the Kalman filter (assuming that the Ø follow a random walk) and the other by the Markov switching (MS) model (assuming that Ø varies according to regimes) for four portfolios of the BRVM. Having found a link between the beta of the market portfolio and the size criterion (measured by capitalization), the two previous models were re-estimated with the addition of the SMB (Small Minus Big) variable. The results show according to the RMSE criterion that the estimation by the Kalman filter fits better than MS, which suggests that investors cannot anticipate systematic risk because of its high volatility.

Empirical studies have revealed that the conditional Capital Asset Pricing Model (CAPM) has a higher explanatory power than its unconditional version, particularly for the model in state-space form where the beta is estimated using Kalman filter. Most empirical analyses are based on stock portfolios to explain financial anomalies, but only a few studies proposed improving investment fund performance. The main contribution of this study is the assessment of Brazilian investment funds through traditional measures estimated from the CAPM model in state-space form with heteroscedastic and homoscedastic errors com­pared to alternative models, such as the unconditional CAPM and a four-factor model. Using a sample of stock funds from May 2005–April 2015, the results indicate that the conditional CAPM model produces better results than the alternative models, providing better performance evaluation practices for funds in both stock-picking and market-timing ability. Os resultados empíricos na literatura demonstram que a versão condicional do Modelo de Precifica­ção de Ativos Financeiros (CAPM), particularmente no que se refere ao modelo na forma em espaço de estado, no qual o beta é estimado pelo filtro de Kalman, possui maior poder explicativo do que a sua versão incondicional. A maioria das análises empíricas na literatura baseia-se em portfólios de ações para explicar anomalias financeiras, porém poucos estudos propõem-se a melhorar a avaliação de desempenho de fundos de investimento. A principal contribuição deste artigo consiste em avaliar fundos de investimento brasileiros por meio de medidas tradicionais estimadas a partir do CAPM na forma em espaço de estado com erros heteroscedásticos e homoscedásticos e comparar seus resulta­dos com modelos alternativos, tais como o CAPM incondicional e o modelo de quatro fatores. Utilizando uma amostra de fundos de ações, os resultados indicam que o modelo CAPM condicional produz melhores resultados do que os modelos alternativos, proporcionando melhores práticas de avaliação de desempenho em relação às habilidades de stock-picking e market-timing. Los resultados empíricos en la literatura revelan que la versión condicional del CAPM, particularmente con respecto al modelo en forma de espacio de estado, en el cual se estima beta mediante el filtro de Kalman, posee mayor poder explicativo que su versión incondicional. La mayoría de los análisis empíri­cos se basan en carteras de valores para explicar anomalías financieras, pero pocos estudios proponen mejorar el rendimiento de los fondos de inversión. La principal contribución de este estudio a la lite­ratura es que lleva a cabo la evaluación de fondos de inversión a través de medidas condicionales generadas a partir del CAPM en forma espacio-estado con errores heteroscedásticos y homoscedásticos y que compara sus resultados con modelos alternativos, tales como CAPM incondicional, modelo de cuatro factores. Utilizando una muestra de fondos de acciones, los resultados indican que el modelo CAPM condicional produce mejores resultados que los modelos alternativos, proporcionando mejores prácticas de evaluación de desempeño en relación con las habilidades de stock-picking y market-timing.

Purpose - The purpose of this paper is to examine, whether or not, the residuals of the market model (MM) are conditionally heteroscedastic; to examine, whether or not, there exists an intervalling effect in conditional heteroscedasticity in the residuals of the MM; to propose a simple data-driven conditional capital asset pricing model (CAPM); and to examine the effect of conditional heteroscedasticity on the estimation of systematic risk.Design methodology approach - Systematic risk coefficients (betas) are estimated at first using data of various frequencies from the Athens stock exchange without taking into account conditional heteroscedasticity. The same procedure is repeated, but this time taking into consideration conditional heteroscedasticity, which is found to exist. The results of the two approaches are compared.Findings - Empirical evidence is provided for the existence of: conditional heteroscedasticity in MM residuals; a pronounced intervalling effect on autoregressive conditional heteroscedasticity (ARCH) in MM residuals; and generalized autoregressive conditional heteroscedasticity in mean type of conditional heteroscedasticity for the majority of cases where ARCH was present in MM residuals. These findings are conducive to a conditional CAPM, which takes into account the effect of conditional variance on expected returns, rather than the standard CAPM.Practical implications - Better estimates of financial risk.Originality value - The intervalling effect in ARCH in the residuals of the MM is examined for the first time.

This article proposes a new methodology for testing structural stability in models estimated via generalized method of moments. Like most previous studies of this general problem, attention is focused on the case in which some aspect of the model potentially changes at a single point in the sample, known as the \"breakpoint.\" Unlike this earlier work, however, our approach is based on a decomposition of the null hypothesis into two components involving parameter constancy and the validity of the overidentifying restrictions both before and after the suspected breakpoint. Using this framework, we propose a testing strategy that offers the potential to discriminate between parameter variation and more general forms of instability. Statistics are presented for testing our null hypotheses in both the known and unknown breakpoint cases. The tests are applied to the conditional capital asset pricing model used by Harvey to explain the international variation in stock index returns. Harvey reported that data from five of the G7 countries satisfy the full-sample overidentifying restrictions of the model; our results indicate that all five of these models exhibit structural instability and so are misspecified.

This book shows how current and recent market prices convey information about the probability distributions that govern future prices. Moving beyond purely theoretical models, Stephen Taylor applies methods supported by empirical research of equity and foreign exchange markets to show how daily and more frequent asset prices, and the prices of option contracts, can be used to construct and assess predictions about future prices, their volatility, and their probability distributions. Stephen Taylor provides a comprehensive introduction to the dynamic behavior of asset prices, relying on finance theory and statistical evidence. He uses stochastic processes to define mathematical models for price dynamics, but with less mathematics than in alternative texts. The key topics covered include random walk tests, trading rules, ARCH models, stochastic volatility models, high-frequency datasets, and the information that option prices imply about volatility and distributions. Asset Price Dynamics, Volatility, and Predictionis ideal for students of economics, finance, and mathematics who are studying financial econometrics, and will enable researchers to identify and apply appropriate models and methods. It will likewise be a valuable resource for quantitative analysts, fund managers, risk managers, and investors who seek realistic expectations about future asset prices and the risks to which they are exposed.

This paper derives and tests an intertemporal capital asset pricing model (ICAPM) based on a conditional version of the Campbell-Vuolteenaho two-beta ICAPM (bad beta, good beta (BBGB)). The novel factor is a scaled cash-flow factor that results from the interaction between cash-flow news and a lagged state variable (market dividend yield or consumer price index inflation). The cross-sectional tests over 10 portfolios sorted on size, 10 portfolios sorted on book-to-market, and 10 portfolios sorted on momentum show that the scaled ICAPM explains relatively well the dispersion in excess returns on the 30 portfolios. The results for an alternative set of equity portfolios (25 portfolios sorted on size and momentum) show that the scaled ICAPM prices particularly well the momentum portfolios. Moreover, the scaled ICAPM compares favorably with alternative asset pricing models in pricing both sets of equity portfolios. The scaled factor is decisive to account for the dispersion in average excess returns between past winner and past loser stocks. More specifically, past winners are riskier than past losers in times of high price of risk. Therefore, a time-varying cash-flow beta/price of risk provides a rational explanation for momentum. This paper was accepted by Wei Xiong, finance.

Volatility is an important component of asset pricing; an increase in volatility on markets can trigger changes in the risk distribution of financial assets. In conventional financial theory, investors are considered to be rational and any changes in relevant risk are assumed to be a result of the movement in fundamental factors. However, herein this study, it is hypothesized that there are movements in risk that are driven by volatility linked to sentiment-driven noise trader activity whose patterns are irreconcilable with changes in fundamental factors. This assertion is tested using a daily sentiment composite index constructed from a set of proxies and Generalised Autoregressive Conditional Heteroscedasticity models on the South African market over a period spanning July 2002 to June 2018. The results show that there is a significant connection between investor sentiment and stock return volatility which shows that behavioural finance can significantly explain the behaviour of stock returns on the Johannesburg Stock Exchange. It is, thus, recommended that due to the inadequacies of popular asset pricing models such as the Capital Asset Pricing Model, consideration should be made towards augmenting these asset pricing models with a sentiment risk factor.

This study proposes Bayesian estimation of multivariate regular vine (R-vine) copula models with generalized autoregressive conditional heteroskedasticity (GARCH) margins modeled by Gaussian-mixture distributions. The Bayesian estimation approach includes Markov chain Monte Carlo and variational Bayes with data augmentation. Although R-vines typically involve computationally intensive procedures limiting their practical use, we address this challenge through parallel computing techniques. To demonstrate our approach, we employ thirteen bivariate copula families within an R-vine pair-copula construction, applied to a large number of marginal distributions. The margins are modeled as exponential-type GARCH processes with intertemporal capital asset pricing specifications, using a mixture of Gaussian and generalized Pareto distributions. Results from an empirical study involving 100 financial returns confirm the effectiveness of our approach.