Predicting the Distribution of Stock Returns: Model Formulation, Statistical Evaluation, VaR Analysis and Economic Significance

Massacci, Daniele

doi:10.1002/for.2332

Cited by 15 publications

(5 citation statements)

References 63 publications

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“…(2019) study a two-step Generalized Dynamic Factor Model for volatilities, which also accounts for the factor structure in returns: it would be worth exploring whether our method selection criterion delivers more accurate volatility predictions within that framework. More generally, it would be interesting to study how well large dimensional factor models predict the conditional distribution of equity returns following an approach similar to that in Massacci (2015). Finally, we focused on stock returns: the analysis of bond returns predictability is high in our research agenda (see Gargano et al, 2017, and references therein).…”

Section: Discussionmentioning

confidence: 99%

Forecasting stock returns with large dimensional factor models

Giovannelli

Massacci

Soccorsi

2021

Journal of Empirical Finance

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We study equity premium out-of-sample predictability by extracting the information contained in a high number of macroeconomic predictors via large dimensional factor models. We compare the well known factor model with a static representation of the common components with a more general model known as the Generalized Dynamic Factor Model. Using statistical and economic evaluation criteria, we empirically show that the Generalized Dynamic Factor Model helps predicting the equity premium. Exploiting the link between business cycle and return predictability, we find more accurate predictions by combining rolling and recursive forecasts in real-time, with promising results in the aftermath of the Great Financial Crisis.

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Section: Discussionmentioning

confidence: 99%

Forecasting stock returns with large dimensional factor models

Giovannelli

Massacci

Soccorsi

2021

Journal of Empirical Finance

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“…As we focus on point forecast of stock returns, the conditional variance of the disturbance term is assumed to be constant. For the distribution forecast, one can refer to the methods used in Massacci (2015). Massacci (2015) considers several specifications for the conditional density allowing for dynamic conditional volatility.…”

Section: Methodsmentioning

confidence: 99%

“…For the distribution forecast, one can refer to the methods used in Massacci (2015). Massacci (2015) considers several specifications for the conditional density allowing for dynamic conditional volatility.…”

Section: Methodsmentioning

confidence: 99%

Uncertainty and the predictability of stock returns

Cai

Pan

Wang

2021

Journal of Forecasting

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While several theoretical models imply that uncertainty has predictive ability for stock returns, few studies investigate this issue using empirical data. We fill this gap by comparing the predictive ability of uncertainty variables with the predictive ability of well‐known economic level variables. We find the in‐sample and out‐of‐sample return predictability using the combining uncertainty information. The predictability is significant from both economic and statistical perspectives. Further analysis shows that macroeconomic uncertainty and level information provide complementary predictive ability over the business cycle. We obtain stronger and more robust return predictability using both types of information together than using either source of information alone.

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“…Cenesizoglu and Timmermann (2008) also find evidence of predictive power especially in the upper tail of the return distribution. Our paper is also related to Massacci (2015) who evaluates the accuracy of density forecasts but restricts the economic and financial variables to predict the location of the distribution only. Durham and Geweke (2014) predict higher-frequency, daily returns allowing for realized intraday volatility and option-implied volatility but restrict these variables to predict the scale of the distribution only.…”

Section: Introductionmentioning

confidence: 99%

Changing Risk-Return Profiles

2018

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We show that realized volatility, especially the realized volatility of financial sector stock returns, has strong predictive content for the future distribution of market returns. This is a robust feature of the last century of U.S. data and, most importantly, can be exploited in real time. Current realized volatility has the most information content on the uncertainty of future returns, whereas it has only limited content about the location of the future return distribution. When volatility is low, the predicted distribution of returns is less dispersed and probabilistic forecasts are sharper. Given this finding on the importance of financial sector volatility not just to financial equity return uncertainty but to the broader market, we test for changes in the realized volatility of banks over a $50 billion threshold associated with more stringent Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank) requirements. We find that the equity volatility of these large banks is differentially lower by 9 percentage points after Dodd-Frank compared to pre-crisis levels, controlling for changes over the same period for all banks and all large firms.

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Predicting the Distribution of Stock Returns: Model Formulation, Statistical Evaluation, VaR Analysis and Economic Significance

Cited by 15 publications

References 63 publications

Forecasting stock returns with large dimensional factor models

Forecasting stock returns with large dimensional factor models

Uncertainty and the predictability of stock returns

Changing Risk-Return Profiles

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