Yudong Wang

• Doctor of Philosophy
• Professor (Full) at Nanjing University of Science and Technology

Satellite images of the parking lots of U.S. retail firms provide information about the firms’ future earnings, and the limited access to these images produces information asymmetry between sophisticated and unsophisticated investors. We construct a cloud-based information risk (CIR) measure to capture this satellite information risk and investigat...

The impact of climate risk on economic and financial fields has attracted considerable interest from academics and practitioners. Recent literature documents that climate risk can affect oil volatility through the dual channel of economic activities and financial markets. Inspired by this, this paper uses climate policy uncertainty (CPU) to examine...

In this paper, we find new evidence for the carbon futures volatility prediction by using the spillovers of fossil energy futures returns as a powerful predictor. The in‐sample results show that the spillovers have a significantly positive effect on carbon futures volatility. From the out‐of‐sample analysis with various loss functions, we find that...

This study develops a new approach that shrinks the forecast combination weights towards equal weights by using weighted least squares and towards zero weight by using regularization constraints. We reveal the significant predictability of excess returns to the S&P500 index that can be achieved by using this double shrinkage combination (DSC). Furt...

Estimation windows, either rolling or expanding, are used for volatility forecasting. In this study, we propose a new approach relying on both estimation windows. Our method is based on how well these two windows performed in terms of prediction during a recent period of past time. We will continue to use whichever one has performed better in the p...

Growing literature documents that jump variations are important for comprehending the evolution of asset prices. In this paper, we provide a novel insight on the jump components. Specifically, we forecast the equity premium using the weighted least squares (WLS) approach that assigns the inverse of variance weight to observations, and detect the ro...

Parameter instability and model uncertainty are two key problems affecting forecasting outcomes. In this paper, we propose a time‐dependent weighted least squares with ridge constraint (TWLS‐Ridge) to solve the above two problems in the forecasting procedure. The new TWLS‐Ridge approach is applied to the heterogenous autoregressive realized volatil...

Satellites can “sense” oil inventory, but cloud cover prevents observation, which reduces the flow of information into the oil market and creates uncertainty about information availability. The effects of the availability of such information on oil prices need to be thoroughly explored. Therefore, using time-series prediction, this paper examines t...

Forecasting stock returns is challenging. Traditional economic data that are available to all investors are published with lags and suffer from the problem of frequent revisions. Consequently, they often fail to forecast stock returns. For this reason, investors are increasingly interested in seeking alternative data. This paper forecasts stock ret...

This study investigates the lead–lag effects between product futures and raw material futures. Results show that returns on product futures lead returns on raw material futures: lagged product futures returns can significantly predict raw material futures returns in‐ and out‐of‐sample. This product‐material lead–lag effect is mainly driven by bad n...

In this paper, we aim to improve the predictability of aggregate stock market volatility with industry volatilities. The empirical results show that individual industry volatilities can provide useful predictive information, while the predictive contribution is limited. We further consider the spillover index between industry volatilities and find...

This paper examines the role of climate risk exposure in the cross-sectional pricing of individual stocks in China. We find a premium of low climate risk exposure: stocks with low climate risk exposure significantly outperform those with high climate risk exposure by 0.83% to 0.90% per month in the future, on a risk-adjusted basis. Results of Fama-...

We develop a new statistical constraint to improve the stock return forecasting performance of predictive models. This constraint uses a new objective function that combines the Huber loss function with the Ridge penalty. Out‐of‐sample results indicate that our constraint improves the predictive ability of the univariate models. The constrained uni...

Many studies have investigated the effects of geopolitical risks on oil price dynamics, but few distinguish their impacts by the event categories. In this paper, we employ the structural vector autoregression with time-varying parameters to identify oil price movements in the face of different types of geopolitical shocks. We not only observe diffe...

A well-documented finding is that explicitly using jumps cannot efficiently enhance the predictability of crude oil price volatility. To address this issue, we find a phenomenon, “momentum of jumps” (MoJ), that the predictive ability of the jump component is persistent when forecasting the oil futures market volatility. Specifically, we propose a s...

This paper uses the newspaper-based Equity Market Volatility (EMV) trackers to forecast crude oil market volatility. We focus on three specific EMV trackers, namely, overall EMV (OEMV), commodity EMV (CEMV), and petroleum EMV (PEMV). We find that all the EMV trackers can improve the forecasting performance of crude oil market volatility. CEMV is be...

The ordinary least squares (OLS) estimator inflates the estimation variance of parameters in the presence of outliers, thus providing poor out-of-sample forecasts. We forecast the real price of crude oil using a robust weighted least squares (RWLS) approach that has the potential to improve forecasting performance by dealing with outliers. This app...

This paper documents that option-implied oil price volatility measured by CBOE Crude Oil Volatility Index (OVX) can significantly and positively forecast future returns of stocks along the worldwide crude oil supply chain. The portfolio investment exercise also confirms that this predictive model can produce positive economic gains, especially for...

The nonlinear components of predictors could be informative, while conventional predictive regression models only consider the linear components in the time-series prediction of crude oil price returns. Inspired by this, we propose a novel method to obtain useful information embedded in the predictors’ nonlinearity. We incorporate not only the orig...

In this study, we provide the predictive linkage between global terrorist attacks and stock market volatility. We propose the predictive model by extending the prevailing heterogeneous autoregressive model for realized volatility (HAR-RV) with global terrorism and denote it as HAR-RV-GT. According to the Diebold – Mariano test and the model confide...

This paper constructs an aligned global economic policy uncertainty (GEPU) index based on a modified machine learning approach. We find that the aligned GEPU index is an informative predictor for forecasting crude oil market volatility both in- and out-of-sample. Compared to general GEPU indices without supervised learning, well-recognized economic...

Motivated by recent investigations on the connections between geopolitical risk and crude oil prices, we implement a moving average strategy using the geopolitical risk index to identify risk uptrends and thus forecast real crude oil prices. The empirical results show that geopolitical risk trends can significantly predict oil prices both in- and o...

Technical indicators are widely employed by practitioners, but they receive less attention in the literature of energy market forecasting. In this paper, we propose two enhanced moving average (MA) technical indicators—one that incorporates daily trading information (MA-D) and one that is the normalized form in which the MA is divided by price (MA-...

In this paper, we improve the ordinary least squares (OLS) estimation approach by replacing a normally distributed error with a t-distributed error. Empirically, we investigate the predictability of the Chinese stock market volatility based on this modified approach. Results show that the modified OLS method with a t-distributed error has a signifi...

We develop a new approach that shrinks a given model forecast to the benchmark model forecast in order to improve forecasting performance. Simulation results show the superior performance of our approach, relative to popular methods such as forecast combination and the robustness to model misspecification. We apply our method to forecasting the ret...

This paper aims to investigate the predictability of good and bad volatilities of oil prices for stock returns. Our empirical results show that bad volatility of oil prices, rather than good volatility of oil prices, can predict stock returns after the oil financialization. Especially, bad oil volatility negatively predicts stock returns because it...

This paper aims to improve the predictability of aggregate oil market volatility with a substantially large macroeconomic database, including 127 macro variables. To this end, we use machine learning from both the variable selection (VS) and common factor (i.e., dimension reduction) perspectives. We first use the lasso, elastic net (ENet), and two...

The first Yuan (RMB) denominated crude oil futures contract, SC, was launched in the Shanghai International Energy Exchange (INE) on 26 March 2018, which is extremely meaningful for China and other Asian countries by offering a new option of oil price risk management. To identify the information connectedness among this emerging contract and those...

To improve the predictability of crude oil futures market returns, this paper proposes a new combination approach based on principal component analysis (PCA). The PCA combination approach combines individual forecasts given by all PCA subset regression models that use all potential predictor subsets to construct PCA indexes. The proposed method can...

This study investigates volatility linkages and risk management in stock and commodity markets at the sectoral level. Static and dynamic correlations consistently reveal a high level of co-movement in the stock market, unstable and generally weak dynamic correlations both in commodity sectors and across the two markets. Motivated by the dynamic con...

China’s dependence on oil imports has greatly increased in recent years. Due to the rapid expansion of global trade, exporting plays an important role in the Chinese economy. This paper uses monthly data from January 2005 to April 2021 to examine the short- and long-term effects of oil price increases and decreases on China’s exports. Our empirical...

This paper aims to investigate the impact of the macroeconomic uncertainty index (MU) on the futures returns of energy, including crude oil, heating oil, and natural gas. The quantile regression is used in this paper for examining the heterogeneity across market conditions. The empirical results show that changes in MU negatively affect futures ret...

We develop a new option pricing model that captures the jump dynamics and allows for the different roles of positive and negative return variances. Based on the proposed model, we derive a closed-form solution for option pricing under the condition of a nonmonotonic pricing kernel. Our results indicate that the new model has superior option pricing...

Forecasting stock volatility is of great interest to academics and practitioners because volatility has important implications for many areas such as risk management and portfolio allocation. Recent studies show that economic variables fail to predict stock volatility beyond lagged volatility. In this paper, we find that realized skewness shows sig...

While several theoretical models imply that uncertainty has predictive ability for stock returns, few studies investigate this issue using empirical data. We fill this gap by comparing the predictive ability of uncertainty variables with the predictive ability of well‐known economic level variables. We find the in‐sample and out‐of‐sample return pr...

Asset returns, especially negative returns, represent the leverage effect and are found to be informative for forecasting financial market volatility. The purpose of this paper is to dig out more useful information in Bitcoin returns when we predict Bitcoin volatility. We use the threshold regression model to differentiate positive and negative ret...

Purpose Oil is crucial for industrial development. This paper investigates the impacts of oil price changes on China's industrial growth and examines whether the impacts are asymmetric. The estimations can help determine how oil price shocks are transmitted throughout the economy. Design/methodology/approach This paper adopts West Texas Intermedia...

In this study, we propose a new family of the heterogeneous autoregressive for realized volatility (HAR-RV) models by considering truncated methods for predicting the RV in China’s stock market. By adopting three types of critical values to recognize extremely large values of RV, we show that the modified models are simple but efficient to consiste...

We propose a least squares estimator weighted by a combination of lagged realized semivariances related to positive and negative returns (WLS-CRS) and use univariate models alone and in combination to reveal significant return predictability. For an investor with a mean-variance preference who allocates a portfolio based on an equal-weighted combin...

Macroscopic uncertainty variables have significant effects on long term components of stock price volatility and can be used to improve VaR and ES prediction for a given stock

In this paper, we employ a novel dimension reduction approach, the scaled principal component analysis (s-PCA), to improve the oil price predictability with technical indicators. The empirical results show that the s-PCA model outperforms various competing models both in- and out-of-sample. From a market timing perspective, an oil futures investor...

We examine the relationship between oil prices and corporate investment, conditional on market conditions. Using 27,981 firm-year observations covering 2814 listed firms from 2000 to 2018, we find that, on the whole, oil prices are negatively correlated with corporate investment expenditure. This oil price–investment relationship changes when marke...

This paper investigates the role of cash holdings in controlling the negative risk from oil price uncertainty. We develop a dynamic model and find that firms with oil-linked assets in place are more likely to experience cash flow shortfalls, which affect their future investments and lead to additional costs and losses. We define these potential cos...

In this paper, we explore the hedging performance of CSI 300 stock index futures under the minimum-variance and maximum-utility framework. We employ ten commonly used econometric models including constant and dynamic ones. Our empirical results indicate that for all futures contracts none of the single model can outperform all other models out-of-s...

This paper aims to investigate the impact of investor attention on the oil market volatility by using the Google search volume index as the measurement of investor attention. In particular, we decompose aggregate volatility into good volatility and bad volatility to gain a deeper insight into this issue. Additionally, we discuss whether the relatio...

Intraday return predictability has generated great interest from academics and practitioners, and intraday momentum in the stock market has been widely documented in the literature. China’s crude oil futures market has a unique trading mechanism with a novel W-shaped trading volume pattern. Inspired by this, we use high-frequency data from China’s...

We investigate the information transmission channels between gold and the financial assets of crude oil futures, US stocks, and exchange rates from the perspective of spillovers. Our daily dataset runs from January 3, 1986, to February 25, 2020. By using the VAR-BEKK-GARCH model, we find mean spillovers in all cases, except for the relationship bet...

This paper aims to accurately forecast the US stock market volatility by using international market volatility information flows. Our results show the significant predictive ability of the combined international volatility information to the US stock volatility. The predictability is found both statistically and economically significant. Furthermor...

This paper investigates the impact of managerial ability on idiosyncratic volatility from the perspective of corporate information. Using the companies listed on the Shenzhen A‐share Main Board from 2003 through 2017, we test the relationship between managerial ability and the quality of information disclosure. We further explore the internal mecha...

Many studies have investigated the mean and volatility spillovers between oil prices and exchange rates. However, the risk spillover has been paid little attention in the literature, although it is of great importance for oil-related risk management. By adopting the crude oil prices and the exchange rates in seven major oil-exporting and oil-import...

We show that the detrended equi-correlation of the returns of industry portfolios is a strong predictor of excess returns to the S&P 500 Index. Using a sample from 1927 to 2015, our monthly industry equi-correlation (IEC) index produces an out-of-sample R2 of as high as 0.888%. For an investor with mean–variance utility, the IEC index can generate...

In this paper, we forecast stock returns using time‐varying parameter (TVP) models with parameters driven by economic conditions. An in‐sample specification test shows significant variation in the parameters. Out‐of‐sample results suggest that the TVP models outperform their constant coefficient counterparts. We also find significant return predict...

We improve the performance of stock return forecasts using predictive regressions with ordinary least squares (OLS) estimates weighted by a class of time-dependent functions (TWLS). To address the structural breaks in predictive relationships, these functions assign heavier weights to more recent observations. We find return predictability that is...

In this paper, we develop a new volatility model capturing the effects of macroeconomic variables and jump dynamics on the stock volatility. The proposed GARCH-Jump-MIDAS model is applied to the S&P 500 index. Our in-sample results indicate that macroeconomic activities have important impacts on aggregate market volatility. Out-of-sample evidence s...

This study uses a structural vector autoregressive (SVAR) model to investigate the effects of oil price shocks on macroeconomic fluctuations in China. Our SVAR model is identified by sign restrictions with impulse response functions and variance decomposition. Using monthly data from December 1999 to July 2018, we find that a positive oil price sho...

We decompose economic uncertainty into “good” and “bad” components according to the sign of innovations. Our results indicate that bad uncertainty provides stronger predictive content regarding future market volatility than good uncertainty. The asymmetric models with good and bad uncertainties forecast market volatility in a better way than the sy...

In this paper, we forecast the real price of crude oil via a robust loss function (Huber), with regularization constraints including LASSO, Ridge, and Elastic Net. These modifications are designed to avoid problems with overfitting and improve out-of-sample predictive performance. The efficient implementation of penalized regression for Huber losse...

Forecasting exchange rates is a challenging work. This paper investigates the predictive content of commodity prices for exchange rates. We draw a factor from prices of 17 popular commodities including crude oil to forecast exchange rates. Our results indicate that the average commodity returns can successfully predict the level and excess returns...

We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike previous approaches in the literature, we implement our constraints directly on the predictor, setting it to zero whenever its value falls within the variable’s past 24-month high and low. Empirically, we find that relative to standard unco...

Economic variables are often used for forecasting commodity prices, but technical indicators have received much less attention in the literature. This paper demonstrates the predictability of commodity price changes using many technical indicators. Technical indicators are stronger predictors than economic indicators, and their forecasting performa...

In this paper, we use two prevailing shrinkage methods, the lasso and elastic net, to predict oil price returns with a large set of predictors. The out-of-sample results indicate that the lasso and elastic net models outperform a host of widely used competing models in terms of out-of-sample R-square and success ratio. In an asset allocation exerci...

Most of existing studies on crude oil futures hedging aim to minimizing the variance of hedged portfolio. In this paper, we evaluate the hedging performance in a different framework of minimum-risk and try to find the optimal hedge model. We employ a total of ten popular econometric specifications including three constant and seven dynamic hedge ra...

We show that increases in oil prices, rather than changes in oil prices, can predict stock returns. The revealed stock return predictability is both statistically and economically significant. The forecasting performance of oil price increases is not affected by changes in the choice of subsample, a considerable advantage over other popular predict...

This paper proposes a new measure of belief dispersion for the Chinese stock market based on the closing price data from mobile and PC trading terminals. Our results show that our belief dispersion measure has significant predictive content for aggregate market volatility both in-sample and out-of-sample. The volatility predictability is robust to...

We develop a new generalized autoregressive conditional heteroskedasticity (GARCH) model that accounts for the information spillover between two markets. This model is used to detect the usefulness of the CBOE volatility index (VIX) for improving the performance of volatility forecasting and option pricing. We find the significant ability of VIX to...

This article investigates the risk spillover effect between oil and stock markets using a novel multivariate quantile model (i.e., the VAR for VaR approach) and pseudo impulse-response functions. We explore the risk spillover at different quantiles using daily data over the period from January 4, 2000 through August 31, 2018. Our results indicate t...

This paper solves the dynamic portfolio allocation problem with account of time-varying jump risk. We find that both the initial jump intensity as a state variable and the jump dynamics including the average jump intensity and jump persistence are important for the investor's optimal portfolio decision. The risk-averse investor can benefit from the...

Extensive literature has shown the predictability of stock returns based on various factors originating in the oil markets. We investigate the international stock return predictability at both daily and monthly frequencies from a new perspective of oil market uncertainties, which are measured by the oil volatility risk premium. In addition to tradi...

We forecast the density of crude oil futures returns using both macroeconomic variables and technical indicators over the period January 1986 through December 2015. The macro variables reflect oil market fundamentals while the technical indicators are constructed based on the popular moving average rules. Several combination strategies over both co...

In this paper, we introduce the functional coefficient to existing mixed-frequency data sampling (MIDAS) regression to make the parameter change over time. The proposed time-varying parameter MIDAS (TVP-MIDAS) is employed to forecast the U.S. real GDP growth using crude oil prices. We find the out-of-sample predictability of GDP growth across diffe...

The goal of this paper is to show that crude oil volatility is predictive of stock volatility in the short-term from both in-sample and out-of-sample perspectives. The revealed predictability is also of economic significance, as shown by examining the performance of portfolios constructed on the oil-based forecasts of stock volatility. Results from...

A recent study by Rapach, Strauss, and Zhou (Journal of Finance, 2013, 68(4), 1633–1662) shows that US stock returns can provide predictive content for international stock returns. We extend their work from a volatility perspective. We propose a model, namely a heterogeneous volatility spillover–generalized autoregressive conditional heteroskedasti...

The relations between carbon and energy market is a hot topic but little research has focused on the time-varying spillover in a quantitative way. This paper employs the method introduced by Diebold and Yilmaz (2012) which constructs the spillover index by variance decomposition of the prediction error. The results reveal the asymmetric spillover e...

We find the momentum of predictability (MoP) that the forecasting performance of some univariate regressions is persistent. A univariate model which outperforms the benchmark during recent past period can also beat the benchmark in the near future out-of-sample. Accordingly, we propose a forecasting strategy that involves switching between a model...

Academic research relies extensively on stock market information to forecast oil volatility, with relatively little attention paid to the reverse evidence. Our paper fills this gap by investigating the predictive ability of oil volatility risk to forecast stock market volatility. Using oil volatility risk premium (oil VRP) as the predictor, we find...

We introduce a regime switching GARCH-MIDAS model to investigate the relationships between oil price volatility and its macroeconomic fundamentals. Our model takes into account both effects of long-term macroeconomic factors and short-term structural breaks on oil volatility. The in-sample and out-of-sample results show that macroeconomic fundament...

In this paper, we forecast real prices of crude oil using real-time forecast combinations over time-varying parameter (TVP) models with single predictor. We reveal the significant predictability at all horizons up to 24 months. The mean squared predictive error reduction over the benchmark of no-change forecast is as high as 17% and the directional...

This paper investigates the effects of oil shocks on export duration of China using the firm-level dataset of Chinese industrial enterprises over the period 1999–2009. The results show that oil supply shocks and other oil-specific shocks have significantly negative impacts on China’s export duration, while aggregate demand shocks have a significantly...

In this paper, we introduce the functional coefficient to heterogeneous autoregressive realized volatility (HAR-RV) models to make the parameters change over time. A nonparametric statistic is developed to perform a specification test. The simulation results show that our test displays reliable size and good power. Using the proposed test, we find...

In this paper, we investigate the multifractality of Chinese and the U.S. stock markets using a multifractal detrending moving average algorithm. The results show that stock returns in both markets are multifractal at a similar extent. We detect the source of multifractality and find that long-range correlations are one of the major sources of mult...

In this paper, we investigate the impacts of oil price shocks on the bilateral exchange rates of the U.S. dollar against currencies in 16 OECD countries. Our empirical findings indicate that the responses of dollar exchange rates to oil price shocks differ greatly depending on whether changes in oil prices are driven by supply or aggregate demand....

The multifractality in stock returns have been investigated extensively. However, whether the autocorrelations in portfolio returns are multifractal have not been considered in the literature. In this paper, we detect multifractal behavior of returns of portfolios constructed based on two popular trading rules, size and book-to-market (BM) ratio. U...

Predictability of macroeconomic and financial variables is an important issue in economics. In this paper, we propose a nonparametric test for the predictability of the direction of price changes. The Monte Carlo simulation results show that our method displays better finite-sample property than the traditional parametric Granger causality test (Gr...

Based on the daily price data of spot prices of West Texas Intermediate (WTI) crude oil and ten CSI300 sector indices in China, we apply multifractal detrended cross-correlation analysis (MF-DCCA) method to investigate the cross-correlations between crude oil and Chinese sector stock markets. We find that the strength of multifractality between WTI...

In this paper, we evaluate the usefulness of GARCH-class models in forecasting densities of crude oil futures from an investor perspective. Volatility forecasts are taken as the key inputs in calculating predictive densities. We find that FIEGARCH accommodating both long memory and asymmetric effect provides more accurate density forecasts than the...