Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models

The study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naïve models to the relatively complex ARCH-class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH-M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility.     

Initial Margin Requirements, Volatility, and the Individual Investor: Insights from Japan

Initial margin requirements represent: (1) a cost impediment to the wealth constrained investor and (2) a potential way of mitigating excessive volatility. However, prior empirical research finds that margins are not an effective tool in reducing volatility. We consider the possibility that margins primarily affect certain stocks and investors. Specifically, we test whether margins affect individuals who, as a group, we believe to be the investors most affected when margin requirements change. Our initial empirical tests, however, do not support this contention.     

Likelihood-based specification analysis of continuous-time models of the short-term interest rate

An extensive collection of continuous-time models of the short-term interest rate is evaluated over data sets that have appeared previously in the literature. The analysis, which uses the simulated maximum likelihood procedure proposed by Durham and Gallant (2002), provides new insights regarding several previously unresolved questions. For single factor models, I find that the volatility, not the drift, is the critical component in model specification. Allowing for additional flexibility beyond a constant term in the drift provides negligible benefit. While constant drift would appear to imply that the short rate is nonstationary, in fact, stationarity is volatility-induced. The simple constant elasticity of volatility model fits weekly observations of the three-month Treasury bill rate remarkably well but is easily rejected when compared with more flexible volatility specifications over daily data. The methodology of Durham and Gallant can also be used to estimate stochastic volatility models. While adding the latent volatility component provides a large improvement in the likelihood for the physical process, it does little to improve bond-pricing performance.     

Simpler Probabilistic Population Forecasts: Making Scenarios Work

The traditional high-low-medium scenario approach to quantifying uncertainty in population forecasts has been criticized as lacking probabilistic meaning and consistency. This paper shows, under certain assumptions, how appropriately calibrated scenarios can be used to approximate the uncertainty intervals on future population size and age structure obtained with fully stochastic forecasts. As many forecasting organizations already produce scenarios and because dealing with them is familiar territory, the methods presented here offer an attractive intermediate position between probabilistically inconsistent scenario analysis and fully stochastic forecasts.     

How do economic policy uncertainty and geopolitical risk drive Bitcoin volatility?

We investigate how economic policy uncertainty (EPU) and geopolitical risks (GPR) impact Bitcoin volatility with respect to factors related to type and nationality of uncertainty, investigated period, relationship horizon and extreme conditions. Applying ARDL model and quantile regression for monthly data from August 2010 to September 2021, we reveal that June 2014 corresponds to a key date that marks a reversal in the investigated relationship. Furthermore, we show that the relationship between uncertainty and bitcoin volatility changes according to different factors. US uncertainty has short run effects on Bitcoin volatility, while China’s uncertainty has rather long run effects. Moreover, Bitcoin volatility responds in the same manner to US EPU and GPR, while, it responds differently to China's EPU and GPR. In extreme quantiles, we find that Bitcoin hedges against US EPU and GPR. Further, Bitcoin hedges against either individual or joint effects of US uncertainty, but not both.     

我国股权分置改革下权证定价的实证研究

权证在性质上也是一种期权，但并不是一种单纯的期权，定价要比普通期权复杂。我国在近十年后，重新推出了权证。在一系列假设条件下，利用Black—Scholes和二叉树模型，推导出权证定价公式。最后采用我国发行的权证及标的股票的收盘价格资料，计算了公司权益的波动率，从而利用已推导的公式对权证理论价值进行了实证分析。并同其实际市场价进行了比较，为权证的投资决策提供了参考依据。     

基于外汇期权方法的汇率预测研究

文章利用2004年芝加哥商品交易所的美元-欧元期货期权的信息,分析了其隐含偏度和隐含波动率在预测短期汇率中的效力。结果发现,隐含偏度、隐含波动率的偏度与每日汇率变化率有紧密的联系。     

上海股市收益与波动的周内效应研究

股市周内效应一直是金融投资者关注的焦点问题，许多学者已做了大量研究，但多数文献将收益与波动的周内效应分开来进行研究和检验，忽视了波动与收益的共生性，其结果缺乏严密性和说服力。针对这种情况，提出平行数据GARCH模型并给出了参数的极大似然估计方法，进而对上海股市收益和波动的周内效应进行检验，既反映收益与风险存在共生关系，又避免了分别判断收益和波动的周内效应所致的缺点。     

资本市场开放与公司内部控制质量——基于“沪港通”的证据

以中国“沪港通”交易制度的实施为政策背景,采用多时点双重差分模型,考察资本市场开放对标的公司内部控制质量的影响,研究发现,“沪港通”交易制度的实施显著提升了公司的内部控制质量,在控制其他因素并经过安慰剂检验、去除A+H股影响、改变周期范围等稳健性检验后,结论依然成立。机制检验表明,“沪港通”主要通过内部和外部两种机制对公司内部控制质量产生影响,其中,内部机制是对股价波动性风险控制,外部机制是审计师声誉风险与政府部门监管。     

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.