Forecasting the volatility of asset returns: The informational gains from option prices

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.     

Are REITs more resilient than non-REITs? Evidence from natural experiments

REITs draw attention from investors around the world, yet our understanding of the various risks associated with such securities is limited. Using the introduction of Arrowhead, a low-latency high-frequency trading platform, to the Tokyo Stock Exchange and the financial crisis of 2008 as natural experiments, we compare the resilience of REITs and equities in terms of liquidity and volatility. The results indicate that the introduction of Arrowhead improved the quality of the Japanese REIT market but also increased the probability of flash crashes. We also find that although the financial crisis significantly deteriorated overall equity market quality, the Japanese REIT market was resilient. Finally, using a difference-in-differences regression model, we show that the higher transparency and better price discovery of REITs, compared to non-REITS, protected them from the negative effects of the financial crisis and the introduction of Arrowhead. Overall, our analysis shows that REITs are more resilient than non-REITs.     

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.     

On the asymmetric impact of macro–variables on volatility

We extend the GARCH–MIDAS model to take into account possible different impacts from positive and negative macroeconomic variations on financial market volatility: a Monte Carlo simulation which shows good properties of the estimator with realistic sample sizes. The empirical application is performed on the daily S&P500 volatility dynamics with the U.S. monthly industrial production and national activity index as additional (signed) determinants. We estimate the Relative Marginal Effect of macro variable movements on volatility at different lags. In the out-of-sample analysis, our proposed GARCH–MIDAS model not only statistically outperforms the competing specifications (GARCH, GJR-GARCH and GARCH–MIDAS models), but shows significant utility gains for a mean-variance investor under different risk aversion parameters. Attention to robustness is given by choosing different samples and estimating the model in an international context (six different stock markets).     

Settlement procedures and stock market efficiency

This study examines why most derivatives markets that settle on the day following expiration choose the opening rather than the closing price as the final settlement price (FSP), whereas most markets that settle on the expiration day select an average rather than a single price as the FSP. Four exogenous changes in the Taiwan Futures Exchange settlement procedures provide an experimental basis for studying the settlement procedures’ impact on underlying assets. Greatest market efficiency is observed when the FSP is determined by a single rather than an average price and hypothesize that manipulation is prevented at the expense of market quality.     

One-sided provision of a public good through bargaining under representative democracy

We examine a trade-off between strategic delegation and the internalization of interregional externalities through bargaining in the context of political economy. We show that in the case of one-sided provision of a public good, if the public good produced by one region generates a sufficiently significant spillover to another region, then interregional negotiation increases the total surplus of the entire economy.     

河北省产业承接的技术溢出效应研究

京津冀协同发展战略上升为国家战略,河北加快承接京津产业转移的步伐,对河北经济转型升级和科技创新能力提升具有重要意义。以河北省为研究对象,基于2005-2016年的数据,使用DEA的曼奎斯特指数方法测算出河北省十二年的技术进步变化情况,并以此为核心变量建立技术溢出模型实证检验了产业承接的技术溢出效应。研究结果发现,河北存在承接区际产业的技术溢出效应,但FDI的技术溢出效应并不显著;R&D经费投入对河北技术进步的促进作用最为明显,而人力资本投入对技术进步的拉升作用较小。     

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.     

普惠金融与县域资金外流：减贫还是致贫*——基于中国592个国家级贫困县的研究

本文利用全国592个国家级贫困县的数据，采用空间计量模型实证分析了普惠金融对县域资金外流的影响，并验证了贫困县资金外流是否会产生致贫效应。本文研究发现：如果普惠金融只注重解决贫困地区对金融机构的接触性排斥，会进一步加剧资金外流，对减贫产生负向影响，即存在显著的致贫效应。这种致贫效应具有明显的空间外溢性，邻近县域的贫困状况在很大程度上会彼此“传染”，并具有空间衰减特征的地理边界。因而需要客观认识普惠金融的本质，有针对性地选择恰当有效的实施载体，解决好对信贷产品等关键金融服务的使用性排斥问题。