Integrated variance forecasting: Model based vs. reduced form

This paper compares model-based and reduced-form forecasts of financial volatility when high-frequency return data are available. We derived exact formulas for the forecast errors and analyzed the contribution of the “wrong” data modeling and errors in forecast inputs. The comparison is made for “feasible” forecasts, i.e., we assumed that the true data generating process, latent states and parameters are unknown. As an illustration, the same comparison is carried out empirically for spot 5 min returns of DM/USD exchange rates.It is shown that the comparison between feasible reduced-form and model-based forecasts is not always in favor of the latter in contrast to their infeasible versions. The reduced-form approach is generally better for long-horizon forecasting and for short-horizon forecasting in the presence of microstructure noise.     

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).     

一种寻找Heston期权定价模型参数的新方法

Heston随机波动率模型的期权定价比Black-Sholes模型更符合市场情况,是金融衍生品定价研究的热点。但应用时需要确定五个待估参数,参数的确定属于组合优化问题,此问题的求解通常比较困难。本文利用遗传算法解决该优化问题,从而得到Heston模型的待估参数。该算法避免丢失最优解,具有群体搜索的特点,有着很好的概率跳出局部极小值,从而以概率1收敛到全局极小值。在实证研究中,利用香港恒生股票指数期权在2014年6月10日和2014年6月25日交易的数据为样本,得到待估参数,并用该参数对2014年6月12日的买入期权和2014年6月27日的卖出期权进行了模拟定价。数值结果与进化过程表明本文方法的有效性和可行性。     

Booking horizon forecasting with dynamic updating: A case study of hotel reservation data

A highly accurate demand forecast is fundamental to the success of every revenue management model. As is often required in both practice and theory, we aim to forecast the accumulated booking curve, as well as the number of reservations expected for each day in the booking horizon. To reduce the dimensionality of this problem, we apply singular value decomposition to the historical booking profiles. The forecast of the remaining part of the booking horizon is dynamically adjusted to the earlier observations using the penalized least squares and historical proportion methods. Our proposed updating procedure considers the correlation and dynamics of bookings both within the booking horizon and between successive product instances. The approach is tested on real hotel reservation data and shows a significant improvement in forecast accuracy.     

Using neural network to forecast stock index option price: a new hybrid GARCH approach

This study aims to apply a new hybrid approach to estimate volatility in neural network option-pricing model. The analytical results also indicate that the new hybrid method can be used to forecast the prices of derivative securities. Owing to combines the grey forecasting model with the GARCH to improve the estimated ability, the empirical evidence shows that the new hybrid GARCH model outperforms the other approaches in the neural network option-pricing model.     

随机波动率LIBOR市场利率动态模型的理论估计与蒙特卡罗模拟

本文首先基于诸多Libor市场模型改进方法的基础之上,在标准市场模型中加入Heston随机波动率过程,建立随机波动率假设的新型Libor市场模型;其次,运用Black逆推参数校正方法和MCMC参数估计方法对该Libor利率市场模型中的局部波动率和随机波动率过程中的参数进行校正和估计;最后是实证模拟。研究结论认为,在构建Libor利率动态模型时,若在单因子Libor利率市场模型基础上引入随机波动率过程,可大大提高利率模型的解释力。     

中国股市日内流动性效应分析——基于高频数据的流动性度量、特征及其影响因素

本文归纳了流动性刻画维度和度量指标,选取不同规模和价位股票的高频数据作样本,吸收Amivest流动性比率计算原理,设定价格对交易量变动的敏感性为流动性度量指标,分析股票日内交易特征和流动性影响因素。结果发现：日内模式价格变动呈仰卧“F”形,交易量呈仰卧“E”形,而非传统的“L”或“U”型;日内交易模式、股票规模和股票价位均影响着股票流动性;日内模式异动时间内,股票流动性差;大规模股票流动性强;高价股流动性差。     

Optimal prediction under LINLIN loss: Empirical evidence

I compare the forecasts of returns from the mean predictor (optimal under MSE), with the pseudo-optimal and optimal predictor for an asymmetric loss function under the assumption that agents have an asymmetric LINLIN loss function. The results strongly suggest not using the conditional mean predictor under conditions of asymmetry. In general, forecasts can be improved by the use of optimal predictor rather than the pseudo-optimal predictor, suggesting that the loss reduction from using the optimal predictor can actually be important for practitioners as well.     

Are low-frequency data really uninformative? A forecasting combination perspective

In this study, we investigate whether low-frequency data improve volatility forecasting when high-frequency data are available. To answer this question, we utilize four forecast combination strategies that combine low-frequency and high-frequency volatility models and employ a rolling window and a range of loss functions in the framework of the novel Model Confidence Set test. Out-of-sample results show that combination forecasts with GARCH-class models can achieve high forecast accuracy. However, the combination forecast methods appear not to significantly outperform individual high-frequency volatility models. Furthermore, we find that models that combine low-frequency and high-frequency volatility yield significantly better performance than other models and combination forecast strategies in both a statistical and economic sense.     

Forecasting exchange rate volatility using high-frequency data: Is the euro different?

We assess the performances of alternative procedures for forecasting the daily volatility of the euro’s bilateral exchange rates using 15 min data. We use realized volatility and traditional time series volatility models. Our results indicate that using high-frequency data and considering their long memory dimension enhances the performance of volatility forecasts significantly. We find that the intraday FIGARCH model and the ARFIMA model outperform other traditional models for all exchange rate series.     

Does faith move stock markets? Evidence from Saudi Arabia

This paper investigates the effects of religious beliefs on stock prices. Our findings support the viewpoint that the religious tenets have important bearing on portfolio choices of investors. It is found that Shariah-compliant stocks have higher return and volatility than their non-Shariah compliant counterparts.     

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.     

高维稀疏数据对象—属性的非关联子空间分析

针对高维稀疏数据对象-属性子空间的优化问题,本文从稀疏性的角度提出了RUSAUBSC算法,通过剔除非关联子空间实现子空间的优化,并通过实验研究证明了该算法的有效性。