What drives volatility persistence in the foreign exchange market?

We propose a new empirical specification of volatility that links volatility to the information flow, measured as the order flow in the market, and to the price sensitivity to that information. The time-varying market sensitivity to information is estimated from high-frequency data, and movements in volatility can therefore be directly related to movements in order flow and market sensitivity. Empirically, the model explains a large share of the long-run variation in volatility. Importantly, the time variation in the market's sensitivity to information is at least as relevant in explaining the persistence of volatility as the rate of information arrival itself. This may be evidence of a link between changes over time in the aggregate behavior of market participants and the time-series properties of realized volatility.     

Investor Attention and Time‐varying Comovements

This paper analyses the effect of an increase in market‐wide uncertainty on information flow and asset price comovements. We use the daily realised volatility of the 30‐year treasury bond futures to assess macroeconomic shocks that affect market‐wide uncertainty. We use the ratio of a stock's idiosyncratic realised volatility with respect to the S&P500 futures relative to its total realised volatility to capture the asset price comovement with the market. We find that market volatility and the comovement of individual stocks with the market increase contemporaneously with the arrival of market‐wide macroeconomic shocks, but decrease significantly in the following five trading days. This pattern supports the hypothesis that investors shift their (limited) attention to processing market‐level information following an increase in market‐wide uncertainty and then subsequently divert their attention back to asset‐specific information.     

Interest Rate Caps “Smile” Too! But Can the LIBOR Market Models Capture the Smile?

Using 3 years of interest rate caps price data, we provide a comprehensive documentation of volatility smiles in the caps market. To capture the volatility smiles, we develop a multifactor term structure model with stochastic volatility and jumps that yields a closed‐form formula for cap prices. We show that although a three‐factor stochastic volatility model can price at‐the‐money caps well, significant negative jumps in interest rates are needed to capture the smile. The volatility smile contains information that is not available using only at‐the‐money caps, and this information is important for understanding term structure models.     

Price Limits and Stock Market Volatility in the Athens Stock Exchange

In this paper, we have examined the effects of price limits on the stock volatility in the Athens Stock Exchange. We put forward two hypotheses, the information hypothesis, which implies that price limits only slow down the process of adjustment and have no effect on stock volatility; and the over-reaction hypothesis, which assumes that investors tend to overreact to new information, so that price limits give them time to reassess the information and reduce stock volatility. Our results show strong support for the information hypothesis. This evidence is obtained by performing the tests on ten stocks, which include heavily traded stocks as well as less active stocks, and covering a variety of industries, and on a market wide price index. The results are also robust to the frequency of the measurement of the returns, and to the tightness of the limits.     

The role of gold futures in mitigating the impact of economic uncertainty on spot prices: Evidence from China

This paper studies the co-integration relationship and volatility spillover effect between China's gold futures and spot prices through the VECM-BEKK-GARCH model. Then, MSGARCH and DCCE-GARCH are applied to study the relationship among China's gold futures market, spot market price volatility and the stabilization effect in uncertain economic environments. This paper enriches the current research, providing gold market participants with hints to address economic uncertainty. The empirical results show that China's gold futures market has a weak stabilization effect on spot price volatility. In scenarios with uncertain economic information and uncertain macroeconomic changes, the correlation between gold futures and spot price volatility is reduced in China, and the role of gold futures in stabilizing the spot price weakens. Furthermore, with economic uncertainty, the fluctuation range of the gold futures price is greater than that of the spot price, with a tendency of more frequent fluctuations. This also means that the effectiveness of the futures market in regulating the spot price will be reduced, and gold market regulators need to stabilize the market through alternative methods to futures.     

Word-of-mouth communication,noise-driven volatility,and public disclosure

This paper examines how a firm adjusts its disclosure quality in response to technological innovations that improve investors' private information. We show that more precise private information can endogenously amplify supply shocks and, hence, increase noise-driven (or non-fundamental) price volatility. We study how the firm reacts to such changes and derive a necessary and sufficient condition under which the firm improves its disclosure quality when investors are informed with better private signals. We then apply our model to study investors' private word-of-mouth communication. Our analysis highlights a “dark side” of word-of-mouth communication and a call for better public disclosure even if private communication is assumed to be unbiased and truthful. We provide empirical predictions regarding how price volatility, market depth, and firms’ disclosure qualities would change as technological innovations, such as social media, facilitate information sharing among investors.     

Consolidation, fragmentation, and the disclosure of trading information

It is commonly believed that fragmented security markets havea natural tendency to consolidate. This article examines thisbelief, focusing on the effect of disclosing trading informationto market participants. We show that large traders who placemultiple trades can benefit from the absence of trade disclosurein a fragmented market, as can dealers who face less price competitionthan in a unified market. Consequently, a fragmented marketneed not coalesce into a single market unless trade disclosureis mandatory. We also compare and contrast fragmented and consolidatedmarkets. Fragmentation results in higher price volatility andviolations of price efficiency.     

交易限制与股票市场定价效率——基于创业板涨跌幅限制放宽的准自然实验研究

本文基于2020年8月24日创业板涨跌幅限制由10%扩大到20%这一政策变化建立准自然实验,从市场层面与公司事件层面探讨交易限制放宽的外生冲击下市场定价效率的变化。研究发现,涨跌幅限制放宽政策实施后,股票价格能更灵敏地反映公开市场信息,更多地包含公司层面特质信息,整体市场定价效率显著提升。进一步研究表明,涨跌幅限制放宽有效缓解了交易干扰问题,避免了过度交易行为延后,缓解了波动性外溢与价格发现延迟。异质性分析表明,无论是市场层面定价效率改善,还是事件层面波动性外溢、价格发现推迟与交易干扰问题的缓解,均在低信息透明度公司中更为显著。本文研究发现为验证涨跌幅限制会抑制股票市场定价效率的理论提供了直接经验证据,同时为推广完善市场化交易制度提供了有益启示。     

Methodological issues in asset pricing: Random walk or chaotic dynamics

We analyze the theoretical foundations of the efficient market hypothesis by stressing the efficient use of information and its effect upon price volatility. The “random walk” hypothesis assumes that price volatility is exogenous and unexplained. Randomness means that a knowledge of the past cannot help to predict the future. We accept the view that randomness appears because information is incomplete. The larger the subset of information available and known, the less emphasis one must place upon the generic term randomness. We construct a general and well accepted intertemporal price determination model, and show that price volatility reflects the output of a higher order dynamic system with an underlying stochastic foundation. Our analysis is used to explain the learning process and the efficient use of information in our archetype model. We estimate a general unrestricted system for financial and agricultural markets to see which specifications we can reject. What emerges is that a system very close to our archetype model is consistent with the evidence. We obtain an equation for price volatility which looks a lot like the GARCH equation. The price variability is a serially correlated variable which is affected by the Bayesian error, and the Bayesian error is a serially correlated variable which is affected by the noisiness of the system. In this manner we have explained some of the determinants of what has been called the “randomness” of price changes.     

Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

This paper proposes a two-state Markov-switching model for stock market returns in which the state-dependent expected returns, their variance and associated regime-switching dynamics are allowed to respond to market information. More specifically, we apply this model to examine the explanatory and predictive power of price range and trading volume for return volatility. Our findings indicate that a negative relation between equity market returns and volatility prevails even after having controlled for the time-varying determinants of conditional volatility within each regime. We also find an asymmetry in the effect of price range on intra- and inter-regime return volatility. While price range has a stronger effect in the high volatility state, it appears to significantly affect only the transition probabilities when the stock market is in the low volatility state but not in the high volatility state. Finally, we provide evidence consistent with the ‘rebound’ model of asset returns proposed by Samuelson (1991), suggesting that long-horizon investors are expected to invest more in risky assets than short-horizon investors.     

机构持股对股价微观波动影响的非对称性

文章利用topview机构投资者日持股数据,构建股价波动率与机构投资者日净买率等指标,使用GMM回归、滚动回归、递归回归,从个股角度动态分析机构投资者对股价微观波动的影响。实证结果表明,机构投资者对股价微观波动的影响因不同的市场状态而具有非对称性,并可以用信息假说进行解释。管理层应依据不同的市场状态而采取合适的措施以实现机构投资者的稳定作用。     

Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks

Oil markets are subject to extreme shocks (e.g. Iraq’s invasion of Kuwait), causing the oil market price exhibits extreme movements, called jumps (or spikes). These jumps pose challenges on oil market volatility forecasting using conventional volatility dynamic models (e.g. GARCH model) This paper characterizes dynamics of jumps in oil market price using high frequency data from three perspectives: the probability (or intensity) of jump occurrence, the sign (e.g. positive or negative) of jumps, and the concurrence with stock market jumps. And then, the paper exploits predictive ability of these jump-related information for oil market volatility forecasting under the mixed data sampling (MIDAS) modeling framework. Our empirical results show that augmenting standard MIDAS model using the three jump-related information significantly improves the accuracy of oil market volatility forecasting. The jump intensity and negative jump size are particularly useful for predicting future oil volatility. These results are widely consistent across a variety of robustness tests. This work provides new insights on how to forecast oil market volatility in the presence of extreme shocks.     

The relation between implied and realised volatility in the Danish option and equity markets

We show that the conclusions to be drawn concerning the informational efficiency of illiquid options markets depend critically on whether one carefully recognises and appropriately deals with the econometrics of the errors‐in‐variables problem. This paper examines the information content of options on the Danish KFX share index. We consider the relation between the volatility implied in an option's price and the subsequently realised index return volatility. Since these options are traded infrequently and in low volumes, the errors‐in‐variables problem is potentially large. We address the problem directly using instrumental variables techniques. We find that when measurement errors are controlled for, call option prices even in this very illiquid market contain information about future realised volatility over and above the information contained in historical volatility.     

Volume,volatility and information linkages in the stock and option markets

This paper examines the relationship between option trading activity and stock market volatility. Although the option market is uniquely suited for trading on volatility information, there is little analysis on how trading activity in this market is linked to stock price volatility. The bulk of the discussion tends to focus on whether trading activity in the stock market is informative about stock volatility. To analyze the information in option trading activity for stock market volatility, a sample of 15 stocks with the highest option trading volume is selected. For each stock, it is noted that the trading activities in the put and call option markets have significant explanatory power for stock market volatility. In addition, the results indicate that the call option trading activity has a stronger impact on stock volatility compared with that of the put options. Our results demonstrate that information and sentiment in the option market is useful for the estimation of stock market volatility. Also, the significance of the effects of option trading activity on stock price volatility is observed to be comparable to that of stock market trading activity. Furthermore, the persistence and asymmetric effects in the volatility of some stocks tend to disappear once option trading activity is taken into account.     

ARE PRICE LIMITS EFFECTIVE? EVIDENCE FROM THE ISTANBUL STOCK EXCHANGE

There is considerable discussion about controlling volatility by imposing price limits on asset prices. We examine the effects of price limits on a stock market by testing the volatility spillover, delayed price discovery, and trading interference hypotheses in a leading emerging market, the Istanbul Stock Exchange, which has a unique market microstructure as related to price limits. Our results support the volatility spillover, delayed price discovery, and trading interference hypotheses. We also show price locks at limits provide significantly stronger evidence regarding the effects of price limits than limit moves only. Finally, price limits have a significant effect on the stock market, casting doubt on their effectiveness.     

To Trade or Not to Trade: The Effect of Broker Search and Discretionary Trading on Securities Market Performance

In this article we examine the interaction of brokerage search with the Bayesian learning behavior of competitive dealers under asymmetric information. We particularly focus on the effects of price search and discretionary trading on the performance of a dealer market. A search process is incorporated into a model in which brokers determine their reservation price and whether to continue their trades. The model enables us to uncover the interrelationships among search cost, bid‐ask spread, and price volatility. We show that both spread revision and price volatility are dependent upon the optimal search process, inventory fluctuation, and search cost. Furthermore, our model predicts a negative relationship between price volatility and liquidity trading volume.     

Forecasting oil price realized volatility using information channels from other asset classes

Motivated from Ross (1989) who maintains that asset volatilities are synonymous to the information flow, we claim that cross-market volatility transmission effects are synonymous to cross-market information flows or “information channels” from one market to another. Based on this assertion we assess whether cross-market volatility flows contain important information that can improve the accuracy of oil price realized volatility forecasting. We concentrate on realized volatilities derived from the intra-day prices of the Brent crude oil and four different asset classes (Stocks, Forex, Commodities and Macro), which represent the different “information channels” by which oil price volatility is impacted from. We employ a HAR framework and estimate forecasts for 1-day to 66-days ahead. Our findings provide strong evidence that the use of the different “information channels” enhances the predictive accuracy of oil price realized volatility at all forecasting horizons. Numerous forecasting evaluation tests and alternative model specifications confirm the robustness of our results.     

Information,market power,and price volatility

We consider demand function competition with a finite number of agents and private information. We show that any degree of market power can arise in the unique equilibrium under an information structure that is arbitrarily close to complete information. Regardless of the number of agents and the correlation of payoff shocks, market power may be arbitrarily close to zero (the competitive outcome) or arbitrarily large (so there is no trade). By contrast, price volatility is always lower than the variance of the aggregate shock across all information structures. Alternative trading mechanisms lead to very distinct bounds as a comparison with Cournot competition establishes.     

Price discovery and volatility spillovers in the European Union emissions trading scheme: A high-frequency analysis

This paper models the relationship of European Union Allowance spot- and futures-prices within the second commitment period of the European Union emissions trading scheme. Based on high-frequency data, we analyze the transmission of information in first and second conditional moments. To reveal long-run price discovery, we compute common factor weights of Schwarz and Szakmary (1994) and information shares of Hasbrouck (1995) based on estimated coefficients of a VECM. To analyze the short-run dynamics, we perform Granger-causality tests. We identify the futures market to be the leader of the long-run price discovery process, whereas the informational role of the futures market increases over time. In addition, we employ a version of the UECCC-GARCH model as introduced by Conrad and Karanasos (2010) to analyze the volatility transmission structure. The volatility analysis indicates a close relationship between the volatility dynamics of both markets, whereas in particular we observe spillovers from the futures to the spot market. As a whole the investigation reveals that the futures market incorporates information first and then transfers the information to the spot market.     

State‐preference pricing and volatility indices

This study uses a state‐preference pricing approach to develop a state‐price volatility index (SVX), as a forecast for market future realised volatility. We show that SVX is a more efficient forecaster than CBOE VIX for 30‐day realised volatility of SPX returns, using both in‐the‐sample and out‐of‐the‐sample tests. This result is robust to different measures of realised market volatilities. We also show that SVX provides a better volatility forecast than other alternative measures, including the at‐the‐money implied volatilities and GARCH (1, 1) volatility. Our results provide a foundation for forecasting higher risk‐neutral moments using the same state prices.