Birth control and poverty in South America

.Poverty, South America

Poverty, inequality and redistribution: A methodology to define the rich

The paper proposes a simple methodology to estimate an affluence line that depends on the knowledge of the income distribution and the poverty line for a given population. The idea that poverty is morally unacceptable and can be eradicated through redistribution of wealth provides the grounds for the methodology. The line is defined as the value that delimitates the aggregated income required to eradicate poverty by the way of transfers from the rich to the poor. I estimate an affluence line using Brazilian 1999 National Household Survey data and briefly discuss the results.Poverty, Affluence, Rich, Social inequality

Modeling and predicting the CBOE market volatility index

This paper performs a thorough statistical examination of the time-series properties of the market volatility index (VIX) from the Chicago Board Options Exchange (CBOE). The motivation lies on the widespread consensus that the VIX is a barometer to the overall market sentiment as to what concerns risk appetite. To assess the statistical behavior of the time series, we run a series of preliminary analyses whose results suggest there is some long-range dependence in the VIX index. This is consistent with the strong empirical evidence in the literature supporting long memory in both options-implied and realized volatilities. We thus resort to linear and nonlinear heterogeneous autoregressive (HAR) processes, including smooth transition and threshold HAR-type models, as well as to smooth transition autoregressive trees (START) for modeling and forecasting purposes. The in-sample results for the HAR-type indicate that they cope with the long-range dependence in the VIX time series as well as the more popular ARFIMA model. In addition, the highly nonlinear START specification also does a god job in controlling for the long memory. The out-of-sample analysis evince that the linear ARMA and ARFIMA models perform very well in the short run and very poorly in the long-run, whereas the START model entails by far the best results for the longer horizon despite of failing at shorter horizons. In contrast, the HAR-type models entail reasonable relative performances in most horizons. Finally, we also show how a simple forecast combination brings about great improvements in terms of predictive ability for most horizons.heterogeneous autoregression, implied volatility, smooth transition, VIX.

What Do We Mean by ?Feminization of Poverty??

The ?feminization of poverty? is an idea that dates back to the 1970s. It was popularized at the start of the 1990s, not least in research by United Nation agencies. The concept has various meanings, some of which are not entirely consistent with its implicit notion of change. We propose a definition that is in line with many recent studies in the field: the feminization of poverty is a change in poverty levels that is biased against women or female-headed households. (...)What Do We Mean by ?Feminization of Poverty??

Poverty among women in Latin America: Feminization or over-representation?

We propose two different concepts of feminization of poverty and analyze household survey data to verify if there is an ongoing feminization of poverty in eight Latin American countries, according to each of these concepts. We also verify if our results respond to changes in values of poverty lines and to different scenarios of intra-household inequalities, concluding that poverty may be higher among women, but there is no clear evidence of a recent and widespread feminization of poverty in the countries studied.Feminization of poverty, Gender inequalities, Poverty, Female headed households, Latin America

Forecasting Realized Volatility with Linear and Nonlinear Univariate Models

In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 futures. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed.Financial econometrics; volatility forecasting; neural networks; nonlinear models; realized volatility; bagging

Forecasting Realized Volatility with Linear and Nonlinear Models

In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in this paper.Financial econometrics, volatility forecasting, neural networks, nonlinear models, realized volatility, bagging.

Modelling multiple regimes in financial volatility with a flexible coefficient GARCH model

In this paper a flexible GARCH-type model is developed with the aim of describing sign and size asymmetries in financial volatility as well as intermittent dynamics and excess of kurtosis. A sufficient condition for strict stationarity and ergodicity of the model is established and the existence of the second- and fourth-order moments is discussed. It is shown that the model may have explosive regimes and still be strictly stationary and ergodic. Furthermore, estimation of the parameters is carefully addressed and the asymptotic properties of the quasi-maximum likelihood estimator is derived. A modeling cycle based on a sequence of simple and easily implemented Lagrange multiplier tests is discussed in order to avoid the estimation of unidentified models. A Monte-Carlo experiment is designed to evaluate the methodology. Empirical examples are used to illustrate the use of the model in practical situations.Volatility, GARCH models, multiple regimes, nonlinear time series, smooth transition, finance, asymmetry, leverage effect, excess of kurtosis.

Asymmetric effects and long memory in the volatility of Dow Jones stocks

Does volatility reflect a continuous reaction to past shocks or changes in the markets induce shifts in the volatility dynamics? In this paper, we provide empirical evidence that cumulated price variations convey meaningful information about multiple regimes in the realized volatility of stocks, where large falls (rises) in prices are linked to persistent regimes of high (low) variance in stock returns. Incorporating past cumulated daily returns as a explanatory variable in a flexible and systematic nonlinear framework, we estimate that falls of different magnitudes over less than two months are associated with volatility levels 20% and 60% higher than the average of periods with stable or rising prices. We show that this effect accounts for large empirical values of long memory parameter estimates. Finally, we analyze that the proposed model significantly improves out of sample performance in relation to standard methods. This result is more pronounced in periods of high volatility.Realized volatility, long memory, nonlinear models, asymmetric effects, regime switching, regression trees, smooth transition, value-at-risk, forecasting, empirical finance.

Forecasting realized volatility models:the benefits of bagging and nonlinear specifications

We forecast daily realized volatilities with linear and nonlinear models and evaluate the benefits of bootstrap aggregation (bagging) in producing more precise forecasts. We consider the linear autoregressive (AR) model, the Heterogeneous Autoregressive model (HAR), and a non-linear HAR model based on a neural network specification that allows for logistic transition effects (NNHAR). The models and the bagging schemes are applied to the realized volatility time series of the S&P500 index from 3-Jan-2000 through 30-Dec-2005. Our main findings are: (1) For the HAR model, bagging successfully averages over the randomness of variable selection; however, when the NN model is considered, there is no clear benefit from using bagging; (2) including past returns in the models improves the forecast precision; and (3) the NNHAR model outperforms the linear alternatives.