We analyze the stock market return predictability for three different periods. We evaluate the co... more We analyze the stock market return predictability for three different periods. We evaluate the conditional variance (CV) and the variance risk premium (VRP) as predictors of stock market returns for which we are using well-established versions of the heterogeneous auto-regressive (HAR) model and propose two new semi-parametric extensions. Results show that the CV and VRP are predictors of future stock market returns in the period before the global financial crisis (GFC). However, these variables lose predictive power after the Dodd-Frank Act (DFA) and change sign, indicating that investors are willing to pay a risk premium for "good uncertainty".
We analyze the stock market return predictability for three different periods. We evaluate the co... more We analyze the stock market return predictability for three different periods. We evaluate the conditional variance (CV) and the variance risk premium (VRP) as predictors of stock market returns for which we are using well-established versions of the heterogeneous auto-regressive (HAR) model and propose two new semi-parametric extensions. Results show that the CV and VRP are predictors of future stock market returns in the period before the global financial crisis (GFC). However, these variables lose predictive power after the Dodd-Frank Act (DFA) and change sign, indicating that investors are willing to pay a risk premium for "good uncertainty".
In this paper we estimate, for several investment horizons, minimum capital risk requirements for... more In this paper we estimate, for several investment horizons, minimum capital risk requirements for short and long positions, using the unconditional distribution of three daily indexes futures returns and a set of GARCH-type and stochastic volatility models. We consider the possibility that errors follow a t-Student distribution in order to capture the kurtosis of the returns distributions. The results suggest that an accurate modeling of extreme returns obtained for long and short trading investment positions is possible with a simple autoregressive stochastic volatility model. Moreover, modeling volatility as a fractional integrated process produces, in general, excessive volatility persistence and consequently leads to large minimum capital risk requirement estimates. The performance of models is assessed with the help of out-of-sample tests and p-values of them are reported.
This paper proposes a new stochastic volatility model to represent the dynamic evolution of condi... more This paper proposes a new stochastic volatility model to represent the dynamic evolution of conditionally heteroscedastic time series with leverage effect. Although there are already several models proposed in the literature with the same purpose, our main justification for a further new model is that it nests some of the most popular stochastic volatility specifications usually implemented to real time series of financial returns. We derive closed-form expressions of its statistical properties and, consequently, of those of the nested specifications. Some of these properties were previously unknown in the literature although the restricted models are often fitted by empirical researchers. By comparing the properties of the restricted models, we are able to establish the advantages and limitations of each of them. Finally, we analyze the performance of a MCMC estimator of the parameters and volatilities of the new proposed model and show that, if the error distribution is known, it has appropriate finite sample properties. Furthermore, estimating the new model using the MCMC estimator, one can correctly identify the restricted true specifications. All the results are illustrated by estimating the parameters and volatilities of simulated time series and of a series of daily S&P500 returns
Computational Statistics & Data Analysis, 2010
Outliers in financial data can lead to model parameter estimation biases, invalid inferences and ... more Outliers in financial data can lead to model parameter estimation biases, invalid inferences and poor volatility forecasts. Therefore, their detection and correction should be taken seriously when modeling financial data. The present paper focuses on these issues and ...
According to the Taylor-Effect the autocorrelations of absolute financial returns are higher than... more According to the Taylor-Effect the autocorrelations of absolute financial returns are higher than the ones of squared returns. In this work, we analyze this empirical property for three different asymmetric stochastic volatility models, with short and/or long memory. Specially, we ...
Outliers in financial data can lead to model parameter estimation biases, invalid inferences and ... more Outliers in financial data can lead to model parameter estimation biases, invalid inferences and poor volatility forecasts. Therefore, their detection and correction should be taken seriously when modeling financial data. This paper focuses on these issues and proposes ...
In this paper we estimate, for several investment horizons, minimum capital risk requirements for... more In this paper we estimate, for several investment horizons, minimum capital risk requirements for short and long positions, using the unconditional distribution of three daily indexes futures returns and a set of GARCH-type and stochastic volatility models. We consider the ...
We analyze the stock market return predictability for three different periods. We evaluate the co... more We analyze the stock market return predictability for three different periods. We evaluate the conditional variance (CV) and the variance risk premium (VRP) as predictors of stock market returns for which we are using well-established versions of the heterogeneous auto-regressive (HAR) model and propose two new semi-parametric extensions. Results show that the CV and VRP are predictors of future stock market returns in the period before the global financial crisis (GFC). However, these variables lose predictive power after the Dodd-Frank Act (DFA) and change sign, indicating that investors are willing to pay a risk premium for "good uncertainty".
We analyze the stock market return predictability for three different periods. We evaluate the co... more We analyze the stock market return predictability for three different periods. We evaluate the conditional variance (CV) and the variance risk premium (VRP) as predictors of stock market returns for which we are using well-established versions of the heterogeneous auto-regressive (HAR) model and propose two new semi-parametric extensions. Results show that the CV and VRP are predictors of future stock market returns in the period before the global financial crisis (GFC). However, these variables lose predictive power after the Dodd-Frank Act (DFA) and change sign, indicating that investors are willing to pay a risk premium for "good uncertainty".
In this paper we estimate, for several investment horizons, minimum capital risk requirements for... more In this paper we estimate, for several investment horizons, minimum capital risk requirements for short and long positions, using the unconditional distribution of three daily indexes futures returns and a set of GARCH-type and stochastic volatility models. We consider the possibility that errors follow a t-Student distribution in order to capture the kurtosis of the returns distributions. The results suggest that an accurate modeling of extreme returns obtained for long and short trading investment positions is possible with a simple autoregressive stochastic volatility model. Moreover, modeling volatility as a fractional integrated process produces, in general, excessive volatility persistence and consequently leads to large minimum capital risk requirement estimates. The performance of models is assessed with the help of out-of-sample tests and p-values of them are reported.
This paper proposes a new stochastic volatility model to represent the dynamic evolution of condi... more This paper proposes a new stochastic volatility model to represent the dynamic evolution of conditionally heteroscedastic time series with leverage effect. Although there are already several models proposed in the literature with the same purpose, our main justification for a further new model is that it nests some of the most popular stochastic volatility specifications usually implemented to real time series of financial returns. We derive closed-form expressions of its statistical properties and, consequently, of those of the nested specifications. Some of these properties were previously unknown in the literature although the restricted models are often fitted by empirical researchers. By comparing the properties of the restricted models, we are able to establish the advantages and limitations of each of them. Finally, we analyze the performance of a MCMC estimator of the parameters and volatilities of the new proposed model and show that, if the error distribution is known, it has appropriate finite sample properties. Furthermore, estimating the new model using the MCMC estimator, one can correctly identify the restricted true specifications. All the results are illustrated by estimating the parameters and volatilities of simulated time series and of a series of daily S&P500 returns
Computational Statistics & Data Analysis, 2010
Outliers in financial data can lead to model parameter estimation biases, invalid inferences and ... more Outliers in financial data can lead to model parameter estimation biases, invalid inferences and poor volatility forecasts. Therefore, their detection and correction should be taken seriously when modeling financial data. The present paper focuses on these issues and ...
According to the Taylor-Effect the autocorrelations of absolute financial returns are higher than... more According to the Taylor-Effect the autocorrelations of absolute financial returns are higher than the ones of squared returns. In this work, we analyze this empirical property for three different asymmetric stochastic volatility models, with short and/or long memory. Specially, we ...
Outliers in financial data can lead to model parameter estimation biases, invalid inferences and ... more Outliers in financial data can lead to model parameter estimation biases, invalid inferences and poor volatility forecasts. Therefore, their detection and correction should be taken seriously when modeling financial data. This paper focuses on these issues and proposes ...
In this paper we estimate, for several investment horizons, minimum capital risk requirements for... more In this paper we estimate, for several investment horizons, minimum capital risk requirements for short and long positions, using the unconditional distribution of three daily indexes futures returns and a set of GARCH-type and stochastic volatility models. We consider the ...
In this paper we estimate, for several investment horizons, minimum capital risk requirements for... more In this paper we estimate, for several investment horizons, minimum capital risk requirements for short and long positions, using the unconditional distribution of three daily indexes futures returns and a set of GARCH-type and stochastic volatility models. We consider the possibility that errors follow a t-Student distribution in order to capture the kurtosis of the returns distributions. The results suggest
In this paper, we estimate minimum capital risk requirements for short, long positions and three ... more In this paper, we estimate minimum capital risk requirements for short, long positions and three investment horizons, using the traditional GARCH model and two other GARCH-type models that incorporate the possibility of asymmetric responses of volatility to price changes; and, ...
Outliers in financial data can lead to model parameter estimation biases, invalid inferences and ... more Outliers in financial data can lead to model parameter estimation biases, invalid inferences and poor volatility forecasts. Therefore, their detection and correction should be taken seriously when modeling financial data. This paper focuses on these issues and proposes a general detection and correction method based on wavelets that can be applied to a large class of volatility models. The effectiveness
In this paper we focus on the impact of additive level outliers on the calculation of risk measur... more In this paper we focus on the impact of additive level outliers on the calculation of risk measures, such as minimum capital risk requirements, and compare four alternatives of reducing these measures' estimation biases. The first three proposals proceed by detecting and correcting outliers before estimating these risk measures with the GARCH(1,1) model, while the fourth procedure fits a Student’s
Uploads
Papers by Helena Veiga