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This paper develops textual sentiment measures for China's stock market by extracting the textual tone of 60 million messages posted on a major online investor forum in China from 2008 to 2018. We conduct sentiment extraction by using both conventional dictionary methods based on customized word lists and supervised machine-learning methods (support vector machine and convolutional neural network). The market-level textual sentiment index is constructed as the average of message-level sentiment scores, and the textual disagreement index is constructed as their dispersion. These textual measures allow us to test a range of predictions of classical behavioral asset-pricing models within a unified empirical setting. We find that textual sentiment can significantly predict market return, exhibiting a salient underreaction-overreaction pattern on a time scale of several months. This effect is more pronounced for small and growth stocks, and is stronger under higher investor attention and during more volatile periods. We also find that textual sentiment exerts a significant and asymmetric impact on future volatility. Finally, we show that trading volume will be higher when textual sentiment is unusually high or low and when there are more differences of opinion, as measured by our textual disagreement. Based on a massive textual dataset, our analysis provides support for the noise-trading theory and the limits-to-arbitrage argument, as well as predictions from limited-attention and disagreement models.

We investigate the effects of economic uncertainty on the return volatility of financial assets, including equities, bonds, foreign exchange and commodities. We use several popular measures of economic uncertainty, and find the uncertainty displays significant but heterogeneous effect on financial volatility. Economic uncertainty constructed in a data rich environment shows strong effects for most financial assets. In particular, the first principal component of the economic uncertainty measures provides a good balance of the effects. The effects of economic uncertainty on financial volatility appear to be closely related to the state of the economy and are more pronounced around recession periods. Furthermore, our out-of-sample analysis shows that investors can use economic uncertainty to predict financial volatility, from both the statistical and economic perspectives.

We study financial volatility during the Global Financial Crisis and use the largest volatility shocks to identify major events during the crisis. Our analysis makes extensive use of high-frequency financial data to model volatility and to determine the timing within the day when the largest volatility shocks occurred. The latter helps us identify the events that may be associated with each of these shocks, and serves to illustrate the benefits of using high-frequency data. Some of the largest volatility shocks coincide, not surprisingly, with the bankruptcy of Lehman Brothers on September 15, 2008 and Congress’s failure to pass the Emergency Economic Stabilization Act on September 29, 2008. Yet, the largest volatility shock was on February 27, 2007, the date when Freddie Mac announced a stricter policy for underwriting subprime loans and a date that was marked by a crash on the Chinese stock market. However, the intraday high-frequency data shows that the main culprit was a computer glitch in the trading system. The days with the largest drops in volatility can in most cases be related to interventions by governments and central banks.

We introduce the Realized Exponential GARCH model that can utilize multiple realized volatility measures for the modeling of a return series. The model specifies the dynamic properties of both returns and realized measures, and is characterized by a flexible modeling of the dependence between returns and volatility. We apply the model to 27 stocks and an exchange traded fund that tracks the S&P 500 index and find specifications with multiple realized measures that dominate those that rely on a single realized measure. The empirical analysis suggests some convenient simplifications and highlights the advantages of the new specification.

We propose a new observation-driven time-varying parameter framework to model the financial return and realized variance jointly. The latent dynamic factor is updated by the scaled local density score as a function of past daily return and realized variance. The new model shares the advantages of both the GAS model of Creal et al. (2013) and Realized GARCH model of Hansen et al. (2012). It is robust to extreme outliers in observations as the volatility dynamics is related to the heavy-tailedness of innovation density. In the meanwhile, it adapts quickly to drastic volatility changes by incorporating realized measures of volatility based on high frequency data. We apply the model to a number of equity returns and demonstrate its promising performance, even during the 2008 financial crisis.

We proposed a method to estimate extreme conditional quantiles by combining quantile GARCH model of Xiao and Koenker (2009) and extreme value theory (EVT) approach. We first estimate the latent volatility process using the information of intermediate quantiles. We then apply EVT to the tail observations to obtain a sound estimate of the likelihood of experiencing an extreme event. Quantile autoregression and EVT together improve efficiency in estimation of extreme quantiles, by borrowing information from neighbor quantiles. Monte Carlo simulation indicates that, the proposed method is promising to provide more accurate estimates for VaR of a financial portfolio, where non-Gaussian tail is present.

During the 1970s, oil market models offered a framework for understanding the growing market power being exercised by major oil producing countries. Few such models have been developed in recent years. Moreover, most large institutions do not use models directly for explaining recent oil price trends or projecting their future levels. Models of oil prices have become more computational, more data driven, less structural and increasingly short run since 2004. Quantitative analysis has shifted strongly towards identifying the role of financial instruments in shaping oil price movements. Although it is important to understand these short-run issues, a large vacuum exists between explanations that track short-run volatility within the context of long-run equilibrium conditions. The theories and models of oil demand and supply that are reviewed in this paper, although imperfect in many respects, offer a clear and well-defined perspective on the forces that are shaping the markets for crude oil and refined products.

The complexity of the world oil market has increased dramatically in recent years and new approaches are needed to understand, model, and forecast oil prices today. In addition to the commencement of the financialization era in oil markets, there have been structural changes in the global oil market. Financial instruments are communicating information about future conditions much more rapidly than in the past. Prices from long and short duration contracts have started moving more together. Sudden supply and demand adjustments, such as the financial crisis of 2008-2009, faster Chinese economic growth, the Libyan uprising, the Iranian Nuclear standstill or the Deepwater Horizon oil spill, change expectations and current prices. Although volatility appears greater, financialization makes price discovery more robust. Most empirical economic studies suggest that fundamental values shaped expectations over 2004-2008, although financial bubbles may have emerged just prior to and during the summer of 2008. With increased price volatility, major exporters are considering ways and means to achieve more price stability to improve long-term production and consumption decisions. Managing excess capacity has historically been an important method for keeping world crude oil prices stable during periods of sharp demand or supply shifts. Building and maintaining excess capacity in current markets allow greater price stability when Asian economic growth suddenly accelerates or during periods of supply uncertainty in major producing regions. OPEC can contribute to price stability more easily when members agree on the best use of oil production capacity.Important structural changes have emerged in the global oil market after major price increases. Partially motivated by government policies major improvements in energy and oil efficiencies occurred after the oil price increases of the early and the late 70s such as the improved vehicle fuel efficiency, building codes, power grids and systems etc. On the supply side, seismic imaging and horizontal drilling as well as favorable tax regimes expanded production capacity in countries outside OPEC. After the oil price increases of 2004-2008, investments in oil sands, deep water, biofuels and other non-conventional sources accelerated. Recent improvements in shale gas production could well be transferred to oil-producing activities, resulting in expanded oil supplies in areas recently considered prohibitively expensive. The search for alternative transportation fuels continues with expanded research into compressed natural gas, biofuels, diesel made from natural gas, and electric vehicles.Still some aspects of the world oil market are not well understood. Despite numerous attempts to model the behavior of OPEC or its members, there exists no credible, verifiable theory about the behavior of the 50 years old organization. OPEC has not consistently acted like a monolithic cartel, constraining supplies to raise prices. Empirical evidence suggests that members sometimes coordinate supply responses and at other times compete with each other. Supply-restraint strategies include slower capacity expansions as well as curtailed production from existing capacity. Regional political considerations and broader economic goals (beyond oil) are influential factors in a country’s oil decisions. Furthermore, the economies of OPEC members as well as their financial needs have changed dramatically from 1970s and 1980s. This review represents a broad review of economic research and literature related to the structure and functioning of the world oil market. The theories and models of oil demand and supply reviewed here, although imperfect in many respects, offer a clear and well-defined perspective on the forces that are shaping the markets for crude oil and refined products. Much work remains to be done if we are to achieve a more complete understanding of these forces and the trends that lie ahead. The contents that follow represent an assessment of how far we have come and where we are headed. Of course, the entire world shares a vital interest in the many benefits that flow from an efficient, well-functioning oil market. It is intended and hoped, therefore, that the discussion in this review will find a broader audience.

The complexity of the world oil market has increased dramatically in recent years and new approaches are needed to understand, model, and forecast oil prices today. In addition to the commencement of the financialization era in oil markets, there have been structural changes in the global oil market. Financial instruments are communicating information about future conditions much more rapidly than in the past. Prices from long and short duration contracts have started moving more together. Sudden supply and demand adjustments, such as the financial crisis of 2008-2009, faster Chinese economic growth, the Libyan uprising, the Iranian nuclear standstill or the deepwater horizon oil spill, change expectations and current prices. The daily Brent spot price fluctuated between $30 and above $140 per barrel since the beginning of 2004. Both fundamental and financial explanations have been offered as explanatory factors. This paper selectively reviews the voluminous literature on oil price determinants since the early 1970s. It concludes that most researchers attribute the long-run oil price path to fundamental factors such as economic growth, resource depletion, technical advancements in both oil supply and demand, and the market organization of major oil petroleum exporting countries (OPEC). Short-run price movements are more difficult to explain. Many researchers attribute short-run price movements to fundamental supply and demand factors in a market with very little quantity response to price changes. Nevertheless, there appears to be some evidence of occasional financial bubbles particularly in months leading up to the financial collapse in 2008. These conflicting stories will not be properly integrated without a meeting of the minds between financial and energy economists.

We introduce a new framework, Realized GARCH, for the joint modeling of returns and realized measures of volatility. A key feature is a measurement equation that relates the realized measure to the conditional variance of returns. The measurement equation facilitates a simple modeling of the dependence between returns and future volatility. Realized GARCH models with a linear or log-linear specification have many attractive features. They are parsimonious, simple to estimate, and imply an ARMA structure for the conditional variance and the realized measure. An empirical application with DJIA stocks and an exchange traded index fund shows that a simple Realized GARCH structure leads to substantial improvements in the empirical fit over standard GARCH models.