Volatility spillovers between WTI and Brent spot crude oil prices: an analysis of granger causality in variance patterns over time

There has been an increase in price volatility in oil prices during and since the global financial crisis (GFC). This study investigates the Granger causality patterns in volatility spillovers between West Texas International (WTI) and Brent crude oil spot prices using daily data. We use Hafner and Herwartz’s (2006) test and employ a rolling sample approach to investigate the changes in the dynamics of volatility spillovers between WTI and Brent oil prices over time. Volatility spillovers from Brent to WTI prices are found to be more pronounced at the beginning of the analysis period, around the GFC, and more recently in 2020. Between 2015 and 2019, the direction of volatility spillovers runs unidirectionally from WTI to Brent oil prices. In 2020, however, a Granger-causal feedback relation between the volatility of WTI and Brent crude oil prices is again detected. This is due to the uncertainty surrounding how the COVID-19 pandemic will evolve and how long the economies and financial markets will be affected. In this uncertain environment, commodities markets participants could be reacting to prices and volatility signals on both WTI and Brent, leading to the detection of a feedback relation.     

Predictive regressions under asymmetric loss: Factor augmentation and model selection

This paper discusses the specifics of forecasting using factor-augmented predictive regressions under general loss functions. In line with the literature, we employ principal component analysis to extract factors from the set of predictors. In addition, we also extract information on the volatility of the series to be predicted, since the volatility is forecast-relevant under non-quadratic loss functions. We ensure asymptotic unbiasedness of the forecasts under the relevant loss by estimating the predictive regression through the minimization of the in-sample average loss. Finally, we select the most promising predictors for the series to be forecast by employing an information criterion that is tailored to the relevant loss. Using a large monthly data set for the US economy, we assess the proposed adjustments in a pseudo out-of-sample forecasting exercise for various variables. As expected, the use of estimation under the relevant loss is found to be effective. Using an additional volatility proxy as the predictor and conducting model selection that is tailored to the relevant loss function enhances the forecast performance significantly.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Cross-border exchanges and volatility forecasting

We test for the performance of a series of volatility forecasting models (GARCH 1,1; EGARCH 1,1; CGARCH) in the context of several indices from the two oldest cross-border exchanges (Euronext; OMX). Our findings overall indicate that the EGARCH (1,1) model outperforms the other two, both before and after the outbreak of the global financial crisis. Controlling for the presence of feedback traders, the accuracy of the EGARCH (1,1) model is not affected, something further confirmed for both the pre and post crisis periods. Overall, ARCH effects can be found in the Euronext and OMX indices, with our results further indicating the presence of significant positive feedback trading in several of our tests.     

Financial integration and the price of world covariance risk: Large- vs. small-cap stocks

We investigate whether recent country-level evidence of global pricing is particular to large-cap stocks. Specifically, we examine cross-country return correlations and conduct asset pricing tests on three size-based stock portfolios for nine developed countries over the period from 1980 to 2004. We find that large-cap stocks realize significant comovements across countries, whereas small-cap stocks realize smaller average correlations (relative to both large-cap stocks and small-cap stocks across countries). More important, asset pricing tests suggest that while large-cap stocks are priced globally, global pricing is rejected for most small-cap stocks. Finally, the evidence indicates that financial integration deepened in recent years primarily for large-cap stocks. Overall, the results suggest that the global pricing pertains chiefly to large-cap stocks.     

THE CHANGING VOLATILITY OF THE SOUTH AFRICAN ECONOMY

During the last decade economic literature explored the presence of and reasons for what became known as “the great moderation” in the US and other G7 countries. “The great moderation” describes the decrease in economic volatility experienced in many of the G7 countries. This paper finds that in South Africa volatility is also not constant (it even finds that there are autoregressive conditional heteroskedastic effects present) and that volatility also decreased, particularly since 1994. Following the literature, the paper explores several reasons for this decrease and finds that smaller shocks, better monetary policy and improvements in the financial sector that place less liquidity constraints on individuals and allow them to manage their debt better are some of the main reasons for the reduction in the volatility of the South African economy. The literature on the G7 also suggests that better inventory management contributed to the lower volatility. However, this seems not to be true for South Africa.     

Analysis of cointegrated VARMA processes

VARMA (vector autoregressive moving average) processes are proposed for modelling cointegrated variables. For this purpose the echelon form is combined with the error correction form. Procedures for estimating the Kronecker indices which characterize the echelon form and for specifying the cointegration rank are discussed. The asymptotic distribution of the coefficient estimators is given. An example based o n US macroeconomic data illustrates the procedure and demonstrates its feasibility in practice.     

The productivity of private and social farms: Multilateral Malmquist indices for Slovene dairying enterprises

Applying programming techniques to farm-level panel data for four cooperative and 12 private dairy farms in the Yugoslav Republic of Slovenia gives estimates of technical efficiency, scale efficiency, and technical progress. These are used to construct multilateral Malmquist indices of total factor productivity (TFP). For the cooperatives, TFP growth has been slow and is attributable to technical progress. For the private farms, technical progress was also slow, but TFP growth was faster due to improved efficiency. Scale inefficiency explains the poorer absolute performance of the private farms, indicating the need for land reform as well as technological change.     

我国切花出口国际竞争力的实证分析

花卉产业是世界农业中惟一不受农产品配额限制的产业。切花产品已经成为国际上的大宗商品，对于切花的需求逐年增长。我国地大物博，气候条件多样，适于种植花卉。作为世贸组织成员国，近年来切花出口增长的趋势明显。本文运用贸易竞争指数、市场占有率、市场集中度和显示性比较优势指数，从我国切花产品与国际市场相对应的角度，定量比较分析我国切花产品国际竞争力，并分析影响切花国际竞争力的主要因素，从而对优化我国切花产品的出口环境提出相应建议。