Forecasting realized volatility of agricultural commodities

We forecast the realized and median realized volatility of agricultural commodities using variants of the heterogeneous autoregressive (HAR) model. We obtain tick-by-tick data on five widely-traded agricultural commodities (corn, rough rice, soybeans, sugar, and wheat) from the CME/ICE. Real out-of-sample forecasts are produced for between 1 and 66 days ahead. Our in-sample analysis shows that the variants of the HAR model which decompose volatility measures into their continuous path and jump components and incorporate leverage effects offer better fitting in the predictive regressions. However, we demonstrate convincingly that such HAR extensions do not offer any superior predictive ability in their out-of-sample results, since none of these extensions produce significantly better forecasts than the simple HAR model. Our results remain robust even when we evaluate them in a Value-at-Risk framework. Thus, there is no benefit from including more complexity, related to the volatility decomposition or relative transformations of the volatility, in the forecasting models.     

A novel cluster HAR-type model for forecasting realized volatility

This paper proposes a cluster HAR-type model that adopts the hierarchical clustering technique to form the cascade of heterogeneous volatility components. In contrast to the conventional HAR-type models, the proposed cluster models are based on the relevant lagged volatilities selected by the cluster group Lasso. Our simulation evidence suggests that the cluster group Lasso dominates other alternatives in terms of variable screening and that the cluster HAR serves as the top performer in forecasting the future realized volatility. The forecasting superiority of the cluster models are also demonstrated in an empirical application where the highest forecasting accuracy tends to be achieved by separating the jumps from the continuous sample path volatility process.     

Volatility swaps and volatility options on discretely sampled realized variance

Volatility swaps and volatility options are financial products written on discretely sampled realized variance. Actively traded in over-the-counter markets, these products are often priced by continuously sampled approximations to simplify the computations. This paper presents an analytical approach to efficiently and accurately price discretely sampled volatility derivatives, under a general stochastic volatility model. We first obtain an accurate approximation for the characteristic function of the discretely sampled realized variance. This characteristic function is then applied to price discrete volatility derivatives through either semi-analytical pricing formulae (up to an inverse Fourier transform) or an efficient Fourier-cosine series method. Numerical experiments show that our approximation is more accurate in comparison to the approximations in the literature. We remark that although discretely sampled variance swaps and options are usually more expensive than their continuously sampled counterparts, discretely sampled volatility swaps are more prone to be cheaper than the continuously sampled counterparts. An analysis is then provided to explain why this is the case in general for realistic contract specifications and reasonable model parameters.     

A two-stage realized volatility approach to estimation of diffusion processes with discrete data

This paper motivates and introduces a two-stage method of estimating diffusion processes based on discretely sampled observations. In the first stage we make use of the feasible central limit theory for realized volatility, as developed in [Jacod, J., 1994. Limit of random measures associated with the increments of a Brownian semiartingal. Working paper, Laboratoire de Probabilities, Universite Pierre et Marie Curie, Paris] and [Barndorff-Nielsen, O., Shephard, N., 2002. Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society. Series B, 64, 253–280], to provide a regression model for estimating the parameters in the diffusion function. In the second stage, the in-fill likelihood function is derived by means of the Girsanov theorem and then used to estimate the parameters in the drift function. Consistency and asymptotic distribution theory for these estimates are established in various contexts. The finite sample performance of the proposed method is compared with that of the approximate maximum likelihood method of [Aït-Sahalia, Y., 2002. Maximum likelihood estimation of discretely sampled diffusion: A closed-form approximation approach. Econometrica. 70, 223–262].     

The impact of sentiment and attention measures on stock market volatility

We analyze the impact of sentiment and attention variables on the stock market volatility by using a novel and extensive dataset that combines social media, news articles, information consumption, and search engine data. We apply a state-of-the-art sentiment classification technique in order to investigate the question of whether sentiment and attention measures contain additional predictive power for realized volatility when controlling for a wide range of economic and financial predictors. Using a penalized regression framework, we identify the most relevant variables to be investors’ attention, as measured by the number of Google searches on financial keywords (e.g. “financial market” and “stock market”), and the daily volume of company-specific short messages posted on StockTwits. In addition, our study shows that attention and sentiment variables are able to improve volatility forecasts significantly, although the magnitudes of the improvements are relatively small from an economic point of view.     

Forecasting the realized volatility of Energy Stock Market: A multimodel comparison

The realized volatility forecasting of energy sector stocks facilitates the establishment of corresponding risk warning mechanisms and investor decisions. In this paper, we collected two different energy sector indices and used different methods, namely principal component analysis (PCA) and sparse principal component analysis (SPCA), to extract features, and combined LSTM and GRU to construct 12 different models. The results show that the SPCA-LSTM model we constructed has the best forecasting performance in the realized volatility forecasting of energy indices, and SPCA has better forecasting results than PCA in the feature extraction stage. The results of the robustness test indicate that our results are robust.     

A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects

We develop an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuous-time components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency intraday data. The model setup allows us to directly assess the structural inter-dependencies among the shocks to returns and the two different volatility components. The model estimates suggest that the leverage effect, or asymmetry between returns and volatility, works primarily through the continuous volatility component. The excellent fit of the model makes it an ideal candidate for an easy-to-implement auxiliary model in the context of indirect estimation of empirically more realistic continuous-time jump diffusion and Lévy-driven stochastic volatility models, effectively incorporating the interdaily dependencies inherent in the high-frequency intraday data.     

Stock market reactions to upside and downside volatility of Bitcoin: A quantile analysis

We investigate how sensitive developed and emerging equity markets are to volatility dynamics of Bitcoin during tranquil, bear, and bull market regimes. Intraday price fluctuations of Bitcoin are represented by three measures of realized volatility, viz. total variance, upside semivariance, and downside semivariance. Our empirical analysis relies on a quantile regression framework, after orthogonalizing raw returns with respect to an array of relevant global factors and accounting for structural shifts in the series. The results suggest that developed-market returns are positively related to the realized variance proxy across various market conditions, while emerging-market returns are positively (negatively) correlated with realized variance during bear (normal and bull) market periods. The upside (downside) component of realized variance has a negative (positive) influence on returns of either market category, and the dependence structure is highly asymmetric across the return distribution. Additionally, we document that developed and emerging markets are more sensitive to downside volatility than to upside volatility when they enter tranquil or bull territory. Our results offer practical implications for policymakers and investors.     

Two-stage stationary bootstrapping for bivariate average realized volatility matrix under market microstructure noise and asynchronicity

Under the two important modern financial market features of noise and non-synchronicity for multiple assets, for consistent estimators of the integrated covariations, we adopt the two-time scale average realized volatility matrix (ARVM) which is a matrix extension of the two-time scale realized volatilities of Zhang et al. (2005). An asymptotic normal theory is provided for the two-time scale ARVM and resulting realized covariations. The asymptotic normality is not directly applicable in practice to construct statistical methods owning to nuisance parameters. To bypass the nuisance parameter problem, two-stage stationary bootstrapping is proposed. We establish consistencies of the bootstrap distributions, and construct confidence intervals and hypothesis tests for the integrated covariance, regression coefficient and correlation coefficient. The validity of the stationary bootstrap for the high frequency heterogeneous returns is proved by showing that there exist parameters of the stationary bootstrap blocks so that the bootstrap consistencies hold. The proposed bootstrap methods extend the i.i.d. bootstrapping methods for realized covariations by Dovonon et al. (2013), that are confined to synchronous noise-free sampling. For high frequency noisy asynchronous samples, a Monte-Carlo experiment shows better finite sample performances of the proposed stationary bootstrap methods based on the two-time scale ARVM estimator than the wild blocks of blocks bootstrap methods of Hounyo (2017), based on pre-averaged truncated estimator.     

A reduced form framework for modeling volatility of speculative prices based on realized variation measures

Building on realized variance and bipower variation measures constructed from high-frequency financial prices, we propose a simple reduced form framework for effectively incorporating intraday data into the modeling of daily return volatility. We decompose the total daily return variability into the continuous sample path variance, the variation arising from discontinuous jumps that occur during the trading day, as well as the overnight return variance. Our empirical results, based on long samples of high-frequency equity and bond futures returns, suggest that the dynamic dependencies in the daily continuous sample path variability are well described by an approximate long-memory HAR–GARCH model, while the overnight returns may be modeled by an augmented GARCH type structure. The dynamic dependencies in the non-parametrically identified significant jumps appear to be well described by the combination of an ACH model for the time-varying jump intensities coupled with a relatively simple log-linear structure for the jump sizes. Finally, we discuss how the resulting reduced form model structure for each of the three components may be used in the construction of out-of-sample forecasts for the total return volatility.     

The role of oil futures intraday information on predicting US stock market volatility

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the proposed models improve their predictive ability with the help of oil futures realized volatility. In particular, the multivariate HAR model outperforms the univariate model. Accordingly, considering the contemporaneous connection is useful to predict the US stock market volatility. Furthermore, these findings are consistent across a variety of robust checks.     

A Model Confidence Set approach to the combination of multivariate volatility forecasts

In predicting conditional covariance matrices of financial portfolios, practitioners are required to choose among several alternative options, facing a number of different sources of uncertainty. A first source is related to the frequency at which prices are observed, either daily or intradaily. Using prices sampled at higher frequency inevitably poses additional sources of uncertainty related to the selection of the optimal intradaily sampling frequency and to the construction of the best realized estimator. Likewise, the choices of model structure and estimation method also have a critical role. In order to alleviate the impact of these sources of uncertainty, we propose a forecast combination strategy based on the Model Confidence Set [MCS] to adaptively identify the set of most accurate predictors. The combined predictor is shown to achieve superior performance with respect to the whole model universe plus three additional competitors, independently of the MCS or portfolio settings.     

Estimation of spot volatility with superposed noisy data

By using high frequency financial data, we nonparametrically estimate the spot volatility at any given time point, while the simultaneous presence of multiple transactions and market microstructure noise in the observation procedure are considered. Our estimator is based on the summation of the locally ranged increments, while kernel smoothing give us spot volatility. Besides, the microstructure noise can be estimated and removed, if it is modeled as bid-ask spread, which is a frequently used assumption. The consistency and asymptotic normality of the estimator are established. We do some simulation studies to assess the finite sample performance of our estimator. The estimator is also applied to some real data sets, further, the relationship between multiple records and spot volatility is also explored.     

城市交通噪声及其控制对策——以北京市三里屯地区为例

我国的城市噪声主要来源于交通噪声。交通噪声扰民,已成为社会的一大公害。通过对三里屯地区交通噪声所进行的具体考察和测试,分析了城市道路交通噪声的现状,进而深入剖析了城市设计因素对交通噪声的影响,提出了基于城市设计视角的对交通噪声的控制对策。     

Independent component analysis for realized volatility: Analysis of the stock market crash of 2008

This paper investigates the factors that drove the U.S. equity market returns from 2007 to early 2010. The period was highlighted by volatile energy and commodity prices, the collapse of insurance and banking firms, extreme implied volatility and a subsequent rally in the overall market. To extract the driving factors, we decompose the returns of the S&P500 sector ETFs into statistically independent signals using independent component analysis. We find that the generated factors have interesting financial interpretations and are consistent with the major economic themes of the period. We find that there are two sets of general market betas during the period along with a dominant factor for energy and materials sector. In addition, we find that the EGARCH model which accommodates asymmetric responses between returns and volatility can plausibly fit the high levels of variance during the crash. Finally, estimated correlations dropped when commodity prices moved higher, but then spiked when the S&P500 crashed in late 2008.     

Information systems for collaborating versus transacting: Impact on manufacturing plant performance in the presence of demand volatility

Research at the nexus of operations management and information systems suggests that manufacturing plants may benefit from the utilization of information systems for collaborating and transacting with suppliers and customers. The objective of this study is to examine the extent to which value generated by information systems for collaborating versus transacting is contingent upon demand volatility. We analyze a unique dataset assembled from non-public U.S. Census Bureau data of manufacturing plants. Our findings suggest that when faced with volatile demand, plants employing information systems for collaborating with suppliers and customers experience positive and significant benefits to performance, in terms of both labor productivity and inventory turnover. In contrast, results suggest that plants employing information systems for transacting in volatile environments do not experience such benefits. Further exploratory analysis suggests that in the context of demand volatility, these two distinct dimensions of IT-based integration have differing performance implications at different stages of the production process in terms of raw-materials inventory and finished-goods inventory, but not in terms of work-in-process inventory. Taken together, our study contributes to theoretical and managerial understanding of the contingent value of information systems in volatile demand conditions in the supply chain context.     

The effect of nominal exchange rate volatility on real macroeconomic performance in the CEE countries

This paper analyzes the relation between nominal exchange rate volatility and several macroeconomic variables, namely real output growth, excess credit, foreign direct investment (FDI) and the current account balance, in the Central and Eastern European EU member states. Using panel estimations for the period between 1995 and 2008, we find that lower exchange rate volatility is associated with higher growth, higher stocks of FDI, higher current account deficits, and higher excess credit. At the same time, the recent evidence seems to suggest that following the global financial crisis, “hard peg” countries may have experienced a more severe adjustment process than “floaters”. The results are economically and statistically significant and robust.     

中小企业发展环境及优化措施研究

文章在阐述中小企业发展环境概念的基础上,通过对中小企业发展的内部和外部环境要素分析,提出了当前优化中小企业发展环境的措施建议。     

Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility

Using monthly data from 1973 through 2020, we explore whether it is possible to improve the accuracy of one-month ahead log-aggregate equity return realized volatility point forecasts by conditioning on various nonlinear crude oil price measures widely relied on in the literature. When evaluating the evidence of unconditional relative equal predictive ability as specified in Diebold and Mariano (1995), we observe that similar to well-known economic variables, such as the dividend yield, the default yield spread and the rate of inflation, we rarely observe evidence of statistical gains in relative point forecast accuracy in favor of the crude oil price-based models. However, when evaluating the evidence of conditionalrelative equal predictive ability as specified in Giacomini and White (2006), we observe that contrary to well-known economic predictors, certain nonlinear crude oil price variables, such as the one-year net crude oil price increase suggested in Hamilton (1996) offer sizable point forecast accuracy gains relative to the benchmark. These statistical gains can also be translated into economic gains.