Predicting stock index volatility: can market volume help?

This paper explores a number of statistical models for predicting the daily stock return volatility of an aggregate of all stocks traded on the NYSE. An application of linear and non-linear Granger causality tests highlights evidence of bidirectional causality, although the relationship is stronger from volatility to volume than the other way around. The out-of-sample forecasting performance of various linear, GARCH, EGARCH, GJR and neural network models of volatility are evaluated and compared. The models are also augmented by the addition of a measure of lagged volume to form more general ex-ante forecasting models. The results indicate that augmenting models of volatility with measures of lagged volume leads only to very modest improvements, if any, in forecasting performance.

The exchange rate disconnect puzzle: a resolution?

Early empirical studies of exchange rate determinants demonstrated that fundamentals-based monetary models were unable to outperform the benchmark random walk model in out-of-sample forecasts while later papers found evidence in favor of long-run exchange rate predictability. More recent theoretical works have adopted a microeconomic structure; a utility-based new open economy macroeconomic framework and a rational expectations present value model. Some recent empirical work argues that if the models are adjusted for parameter instability, it is a good predictor of nominal exchange rates while others use aggregate idiosyncratic volatility to generate good predictions. This latest research supports the idea that fundamental economic variables are likely to influence exchange rates especially in the long run and further that the emphasis should change to the economic value or utility based value to assess these macroeconomic models.

Forecasting stock index volatility : comparing implied volatility and the intraday high-low price range

The intraday high–low price range offers volatility forecasts similarly efficient to high-quality implied volatility indexes published by the Chicago Board Options Exchange (CBOE) for four stock market indexes: S&P 500, S&P 100, NASDAQ 100, and Dow Jones Industrials. Examination of in-sample and out-of-sample volatility forecasts reveals that neither implied volatility nor intraday high–low range volatility consistently outperforms the other.

Dynamic currency hedging for international stock portfolios

The paper studies dynamic currency risk hedging of international stock portfolios using a currency overlay. A dynamic conditional correlation (DCC) multivariate GARCH model is employed to estimate time-varying covariance among stock market returns and currency returns. The conditional covariance is then used in the estimation of risk-minimizing conditional hedge ratios. The study considers seven developed economies over the period January 2002 to April 2010 and estimates daily conditional hedge ratios for portfolios of various stock market combinations. Conditional hedging is shown to dominate traditional static hedging and unconditional hedging in terms of risk reduction both in-sample and out-of-sample, especially during the recent global financial crisis. Conditional hedging also proves to consistently reduce portfolio risk for various levels of foreign investments.

Oil price uncertainty and sovereign risk: evidence from Asian economies

In this paper, we test whether oil price uncertainty predicts credit default swap (CDS) returns for eight Asian countries. We use the Westerlund and Narayan, 2011 and Westerlund and Narayan, 2012 predictability test that accounts for any persistence in and endogeneity of the predictor variable. The estimator also accounts for any heteroskedasticity in the regression model. In-sample evidence reveals that oil price uncertainty predicts CDS returns for three Asian countries, whereas out-of-sample evidence suggests that oil price uncertainty predicts CDS returns for six countries.

Do oil prices predict economic growth? New global evidence

In this paper, we test whether oil price predicts economic growth for 28 developed and 17 developing countries. We use predictability tests that account for the key features of the data, namely, persistency, endogeneity, and heteroskedasticity. Our analysis considers a large number of countries, shows evidence of more out-of-sample predictability with nominal than real oil prices, finds in-sample predictability to be independent of the use of nominal and real prices, and reveals greater evidence of predictability for developed countries.

Does cash flow predict returns?

In this paper, we propose the hypothesis that cash flow and cash flow volatility predict returns. We categorize firms listed on the New York Stock Exchange into sectors, and apply tests for both in-sample and out-of-sample predictability. While we find strong evidence that cash flow volatility predicts returns for all sectors, the evidence obtained when using cash flow as a predictor is relatively weak. Estimated profits and utility gains also suggest that it is cash flow volatility that is more relevant as a source of information than cash flow.

Intraday volatility interaction between the crude oil and equity markets

This paper investigates the price volatility interaction between the crude oil and equity markets in the US using 5-min data over the period 2009-2012. Our main findings can be summarised as follows. First, we find strong evidence to demonstrate that the integration of the bid-ask spread and trading volume factors leads to a better performance in predicting price volatility. Second, trading information, such as bid-ask spread, trading volume, and the price volatility from cross-markets, improves the price volatility predictability for both in-sample and out-of-sample analyses. Third, the trading strategy based on the predictive regression model that includes trading information from both markets provides significant utility gains to mean-variance investors.

Asymmetric risk and return: evidence from the Australian Stock Exchange

This paper examines volatility asymmetry in a financial market using a stochastic volatility framework. We use the MCMC method for model estimations. There is evidence of volatility asymmetry in the data. Our asymmetric stochastic volatility in mean model, which nests both asymmetric stochastic volatility (ASV) and stochastic volatility in mean models (SVM), indicates ASV sufficiently captures the risk-return relationship; therefore, augmenting it with volatility in mean does not improve its performance. ASV fits the data better and yields more accurate out-of-sample forecasts than alternatives. We also demonstrate that asymmetry mainly emanates from the systematic parts of returns. As a result, it is more pronounced at the market level and the volatility feedback effect dominates the leverage effect.

Metas de inflação : uma análise da experiência brasileira

Desde que foi introduzido pela primeira vez na Nova Zelândia, em março de 1990, o regime de metas de inflação tem sido objeto de crescente interesse na literatura econômica. Seguindo a Nova Zelândia, inúmeros outros países passaram a adotar metas de inflação como regime de política monetária, como, por exemplo, o Canadá, Reino Unido, Suécia, Finlândia, Espanha, Chile, e Brasil. Por se tratar de um regime relativamente novo, surgiram na literatura diversos trabalhos com o propósito de avaliar este regime, tanto de uma perspectiva teórica quanto empírica. O objetivo principal deste trabalho é implementar alguns testes iniciais sobre a efetividade do regime de metas de inflação no Brasil. Para alcançar este objetivo, utilizamos duas abordagens principais. Em primeiro lugar, estimamos uma função de reação do tipo Taylor, e olhamos para mudanças nos pesos relativos sobre a atividade real e sobre a inflação. Na segunda abordagem, obtemos dois modelos Auto-Regressivos Vetoriais (VAR), um restrito e outro irrestrito. Estes dois modelos foram utilizados num exercício de previsão fora da amostra (out-of-sample). Os resultados, embora ainda preliminares produzidos pelos poucos dados disponíveis, permitem-nos concluir que o impacto inicial de metas de inflação foi positivo. Nossos resultados indicam que metas de inflação foi um mecanismo importante para manter a estabilidade de preços obtida a partir do Plano Real, mesmo num contexto de acentuada desvalorização cambial.

Are price limits on futures markets that cool?: evidence from the Brazilian Mercantile and Futures Exchange

This paper investigates the impact of price limits on the Brazilian futures markets using high frequency data. The aim is to identify whether there is a cool-off or a magnet effect. For that purpose, we examine a tick-by-tick data set that includes all contracts on the S˜ao Paulo stock index futures traded on the Brazilian Mercantile and Futures Exchange from January 1997 to December 1999. The results indicate that the conditional mean features a floor cool-off effect, whereas the conditional variance significantly increases as the price approaches the upper limit. We then build a trading strategy that accounts for the cool-off effect in the conditional mean so as to demonstrate that the latter has not only statistical, but also economic significance. The in-sample Sharpe ratio indeed is way superior to the buy-and-hold benchmarks we consider, whereas out-of-sample results evince similar performances.

Forecasting electricity demand using generalized long memory

This paper studies the electricity hourly load demand in the area covered by a utility situated in the southeast of Brazil. We propose a stochastic model which employs generalized long memory (by means of Gegenbauer processes) to model the seasonal behavior of the load. The model is proposed for sectional data, that is, each hour’s load is studied separately as a single series. This approach avoids modeling the intricate intra-day pattern (load profile) displayed by the load, which varies throughout days of the week and seasons. The forecasting performance of the model is compared with a SARIMA benchmark using the years of 1999 and 2000 as the out-of-sample. The model clearly outperforms the benchmark. We conclude for general long memory in the series.

A stochastic discount factor approach to asset pricing using panel data asymptotics

Using the Pricing Equation in a panel-data framework, we construct a novel consistent estimator of the stochastic discount factor (SDF) which relies on the fact that its logarithm is the "common feature" in every asset return of the economy. Our estimator is a simple function of asset returns and does not depend on any parametric function representing preferences. The techniques discussed in this paper were applied to two relevant issues in macroeconomics and finance: the first asks what type of parametric preference-representation could be validated by asset-return data, and the second asks whether or not our SDF estimator can price returns in an out-of-sample forecasting exercise. In formal testing, we cannot reject standard preference specifications used in the macro/finance literature. Estimates of the relative risk-aversion coefficient are between 1 and 2, and statistically equal to unity. We also show that our SDF proxy can price reasonably well the returns of stocks with a higher capitalization level, whereas it shows some difficulty in pricing stocks with a lower level of capitalization.

Avaliando o avaliador: evidências de um experimento de campo sobre as auditorias da CGU

O Brasil praticamente alcançou a provisão universal dos serviços públicos de educação, saúde e assistência social nos últimos anos, mas a qualidade desses serviços ainda está bem atrás da maioria dos países desenvolvidos. As instituições de controle são atores relevantes nesse contexto, pois é seu dever avaliar a efetividade e a eficiência da provisão desses serviços públicos. Entretanto, pouco se sabe sobre a efetividade dessas instituições, especialmente no Brasil. Os artigos de Olken (2007), Reinikka e Svensson (2004) e Di Tella & Schargrodsky (2000) trazem alguns elementos para essa discussão, ao mostrar como e onde políticas de boas práticas podem funcionar em outros países. No Brasil, estudos empíricos sobre essas políticas são escassos. Nesta tese, meu principal objetivo é trazer evidências sobre a efetividade da auditoria pública no Brasil. Utilizando um experimento de campo, eu avalio a efetividade do trabalho de auditoria da Controladoria-Geral da União (CGU) no âmbito do Programa de Fiscalização a partir de Sorteios Públicos. Os principais tópicos discutidos aqui são relativos à gestão de programas em nível local e aos processos licitatórios a eles associados. Os municípios no grupo de tratamento são submetidos a um aumento na probabilidade de receber uma auditoria, enquanto os de controle permanecem com probabilidade inalterada. Os resultados sugerem que os gestores locais são sensíveis ao tratamento quando focamos as licitações, mas não quando a questão é a gestão de programas. Em seguida ao experimento, utilizo um modelo "Fora da Amostra" para sugerir um mecanismo de alocação de recursos financeiros e humanos, para melhorar os níveis de eficiência do trabalho de campo da CGU

Realized volatility: evidence from Brazil

Using intraday data for the most actively traded stocks on the São Paulo Stock Market (BOVESPA) index, this study considers two recently developed models from the literature on the estimation and prediction of realized volatility: the Heterogeneous Autoregressive Model of Realized Volatility (HAR-RV), developed by Corsi (2009), and the Mixed Data Sampling model (MIDAS-RV), developed by Ghysels et al. (2004). Using measurements to compare in-sample and out-of-sample forecasts, better results were obtained with the MIDAS-RV model for in-sample forecasts. For out-of-sample forecasts, however, there was no statistically signi cant di¤erence between the models. We also found evidence that the use of realized volatility induces distributions of standardized returns that are closer to normal