Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage

Many recent papers in macroeconomics have used large vector autoregressions (VARs) involving 100 or more dependent variables. With so many parameters to estimate, Bayesian prior shrinkage is vital to achieve reasonable results. Computational concerns currently limit the range of priors used and render difficult the addition of empirically important features such as stochastic volatility to the large VAR. In this paper, we develop variational Bayesian methods for large VARs that overcome the computational hurdle and allow for Bayesian inference in large VARs with a range of hierarchical shrinkage priors and with time-varying volatilities. We demonstrate the computational feasibility and good forecast performance of our methods in an empirical application involving a large quarterly US macroeconomic data set.     

Conditional value-at-risk forecasts of an optimal foreign currency portfolio

This study provides daily conditional value-at-risk (C-VaR) forecasts for a foreign currency portfolio comprising the USD/EUR, USD/JPY, and USD/BRL currencies. To do so, we estimate multivariate stochastic volatility models with time-varying conditional correlations using a Bayesian Markov chain Monte Carlo algorithm. Then, given the model-specific currency return density forecasts, we make the optimal portfolio choice by minimizing the C-VaR through numerical optimization. According to out-of-sample experiment, including emerging markets into the currency basket is essential for downside risk management, and considering model uncertainty as well as the parameter uncertainty can improve the portfolio performance.     

How does inter-industry spillover improve the performance of volatility forecasting?

This study aims to investigate whether introducing inter-industry spillover information into the GARCH-MIDAS model improves out-of-sample forecasting attempts. We explore the transmission of volatility across sectors, as well as the reliance on inter-industry business links. Our findings demonstrate strong cross-industry volatility spillovers that are related to the degree of the industry-to-industry trading linkage. We compare the out-of-sample volatility forecasting performance of the spillovers-information-incorporated GARCH-MIDAS model with that of the traditional GARCH model. The empirical results show that the GARCH-MIDAS model outperforms traditional GARCH models. Notably, we discover that good (bad) news is always transferred from the back end of the production process to the front end, meaning that economic growth (decline) is driven by consumption expansion (shrinkage).     

Minnesota-type adaptive hierarchical priors for large Bayesian VARs

Large Bayesian VARs with stochastic volatility are increasingly used in empirical macroeconomics. The key to making these highly parameterized VARs useful is the use of shrinkage priors. We develop a family of priors that captures the best features of two prominent classes of shrinkage priors: adaptive hierarchical priors and Minnesota priors. Like adaptive hierarchical priors, these new priors ensure that only ‘small’ coefficients are strongly shrunk to zero, while ‘large’ coefficients remain intact. At the same time, these new priors can also incorporate many useful features of the Minnesota priors such as cross-variable shrinkage and shrinking coefficients on higher lags more aggressively. We introduce a fast posterior sampler to estimate BVARs with this family of priors—for a BVAR with 25 variables and 4 lags, obtaining 10,000 posterior draws takes about 3 min on a standard desktop computer. In a forecasting exercise, we show that these new priors outperform both adaptive hierarchical priors and Minnesota priors.     

涨跌停板制度对期货市场价格发现过程及波动性的影响--基于上海期货交易所的实证研究

本文通过检验在出现涨跌停板之后一个交易日的期货价格及其波动性的变化情况,研究了涨跌停板制度对上海期货交易所期货价格变动的影响。研究结果显示,对不同的期货品种,涨跌停板制度的影响存在一定的差异,但总体而言,涨跌停板制度并没有起到防范价格过度反应和降低市场波动性的作用。相反,在一定程度上延缓了期货市场价格发现功能的发挥,增大了市场的波动性。     

Volatility activity: Specification and estimation

The paper examines volatility activity and its asymmetry and undertakes further specification analysis of volatility models based on it. We develop new nonparametric statistics using high-frequency option-based VIX data to test for asymmetry in volatility jumps. We also develop methods for estimating and evaluating, using price data alone, a general encompassing model for volatility dynamics where volatility activity is unrestricted. The nonparametric application to VIX data, along with model estimation for S&P index returns, suggests that volatility moves are best captured by an infinite variation pure-jump martingale with a symmetric jump compensator around zero. The latter provides a parsimonious generalization of the jump-diffusions commonly used for volatility modeling.     

Is there one safe-haven for various turbulences? The evidence from gold,Bitcoin and Ether

This paper aims to investigate the safe-haven properties of gold and two cryptocurrencies, Bitcoin and Ether. Safe havens are the financial assets that allow investors to protect their portfolios within the market turmoil. The research sample covers five years and includes several downturns on the financial markets, starting from the Chinese stock market turbulences in 2015/2016 and ending up with the recent pandemic outbreak in 2020. We find that only gold used to be a strong safe-haven against the stock market indices. Yet, this property evaporated during the crisis caused by the COVID pandemic. Occasionally, cryptocurrencies could have been considered weak safe-havens against the examined instruments. Ether acted more often as a weak safe-haven against DAX or S&P500, while Bitcoin played this role against FTSE250, STOXX600 and S&P500.     

Would the Bundesbank have prevented the Great Inflation in the United States?

Policy counterfactuals based on estimated structural VARs routinely suggest that bringing Alan Greenspan back in the 1970s United States would not have prevented the Great Inflation. We show that a standard policy counterfactual suggests that the Bundesbank—which is near-universally credited for sparing West Germany the Great Inflation—would also not have been able to prevent the Great Inflation in the United States.The implausibility of this result sounds a cautionary note on taking the outcome of SVAR-based policy counterfactuals at face value, and raises questions on the reliability of such exercises.     

上海期货交易所的涨跌停板制度对波动性的影响

本文通过对上海期货交易所的三个品种的涨跌停板制度进行检验,检验方法为:从收益率所拟和的ARMA模型中滤出残差,进行波动率的GARCH模型回归。波动率模型中加入了哑元变量来体现涨停板对后一日波动的影响。实证结果显示,铜、铝、天然橡胶的涨跌停板本应显著地使收益率的波动率减小的作用未检验出,相反却得到涨停板使三个品种显著波动率增大的检验结果。是否需要扩大涨跌停板,提高市场效率?检验结果带给我们如何使涨跌停板制度趋于合理化的思考。     

Forecasting realized variance measures using time-varying coefficient models

This paper considers the problem of forecasting realized variance measures. These measures are highly persistent estimates of the underlying integrated variance, but are also noisy. Bollerslev, Patton and Quaedvlieg (2016), Journal of Econometrics 192(1), 1–18 exploited this so as to extend the commonly used heterogeneous autoregressive (HAR) by letting the model parameters vary over time depending on the estimated measurement error variances. We propose an alternative specification that allows the autoregressive parameters of HAR models to be driven by a latent Gaussian autoregressive process that may also depend on the estimated measurement error variance. The model parameters are estimated by maximum likelihood using the Kalman filter. Our empirical analysis considers the realized variances of 40 stocks from the S&P 500. Our model based on log variances shows the best overall performance and generates superior forecasts both in terms of a range of different loss functions and for various subsamples of the forecasting period.     

Shifts in volatility driven by large stock market shocks

This paper presents an extension of the stochastic volatility model which allows for level shifts in volatility of stock market returns, known as structural breaks. These shifts are endogenously driven by large return shocks (innovations), reflecting large pieces of market news. These shocks are identified from the data as being bigger in absolute terms than the values of two threshold parameters of the model: one for the negative shocks and one for the positive shocks. The model can be employed to investigate different sources of stock market volatility shifts driven by market news, without relying on exogenous information. In addition to this, it has a number of interesting features which enable us to study the effects of large return shocks on future levels of market volatility. The above properties of the model are shown based on a study for the US stock market volatility.