CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS

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.摘要 实证研究表明，条件资本资产定价模型（Conditional Capital Asset Pricing Model，CAPM）相较于其无条件版本具有更高的解释力，尤其适用于通过卡尔曼滤波（Kalman filter）估计贝塔值的状态空间形式模型。现有多数实证分析均以股票投资组合为样本以解释金融异象，但仅有少数研究致力于提升投资基金的业绩评估效果。本研究的主要贡献在于，通过基于状态空间形式的CAPM模型（分别设置异方差与同方差误差项）估计得到的传统业绩衡量指标，结合无条件CAPM与四因子模型等替代模型，对巴西投资基金进行评估。本文采用2005年5月至2015年4月的股票型基金样本开展实证分析，结果显示条件CAPM模型相较于各类替代模型表现更优，可为基金的选股能力与择时能力评估提供更完善的实践方法。