High-low range in GARCH models of stock return volatility

We suggest a simple and general way to improve the GARCH volatility models using the intraday range between the highest and the lowest price to proxy volatility. We illustrate the method by modifying a GARCH(1,1) model to a range-GARCH(1,1) model. Our empirical analysis conducted on stocks, stock indices and simulated data shows that the range-GARCH(1,1) model performs significantly better than the standard GARCH(1,1) model both in terms of in-sample fit and out-of-sample forecasting ability.     

The distribution of index futures realised volatility under seasonality and microstructure noise

Previous research documents that the distribution of realised volatility appears approximately log-normal. However, formal tests reject normality fairly convincingly, which may indicate intrinsic features in the intraday data series, namely, the presence of seasonal intraday patterns and microstructure noise. Because many models are based on a normality assumption, this must be verified in order to validate the results. We find departures from normality due to the seasonal and noise components of intraday data, such that, after controlling for both features, the volatility estimates follow a log-normal distribution. Our results reveal that failing to account for these market imperfections can have important implications for analyses of volatility transmission and for investment and hedging decisions.     

A study of Shanghai fuel oil futures price volatility based on high frequency data: Long-range dependence,modeling and forecasting

In existing researches, the investigations of oil price volatility are always performed based on daily data and squared daily return is always taken as the proxy of actual volatility. However, it is widely accepted that the popular realized volatility (RV) based on high frequency data is a more robust measure of actual volatility than squared return. Due to this motivation, we investigate dynamics of daily volatility of Shanghai fuel oil futures prices employing 5-minute high frequency data. First, using a nonparametric method, we find that RV displays strong long-range dependence and recent financial crisis can cause a lower degree of long-range dependence. Second, we model daily volatility using RV models and GARCH-class models. Our results indicate that RV models for intraday data overwhelmingly outperform GARCH-class models for daily data in forecasting fuel oil price volatility, regardless the proxy of actual volatility. Finally, we investigate the major source of such volatile prices and found that trader activity has major contribution to fierce variations of fuel oil prices.     

Volatility and Spillover Effects of Yen Interventions

We consider the effects of interventions by the Bank of Japan's (BoJ) on the intraday volatility of the US dollar/Japanese yen (USD/JPY) exchange rates and their spillovers to volatility of the euro/JPY exchange rates. We use 15‐minute data during the period 2000–2004 and employ multivariate generalized autoregressive conditional heteroskedasticity (GARCH) modeling and quartile plots of intraday volatility to analyze the intraday effects of the BoJ interventions on exchange rate volatility. The results indicate that the BoJ interventions decrease daily volatility of the USD/JPY exchange rate but increase the volatility of the euro/JPY series. On intervention days, the intraday volatility has different patterns to those on non‐intervention days.     

Foreign News and Spillovers in Emerging European Stock Markets

We analyze foreign news and spillovers in the emerging EU stock markets (the Czech Republic, Hungary, and Poland). We employ high‐frequency five‐minute intraday data on stock market index returns and four classes of EU and US macroeconomic announcements during 2004–07. We account for the difference of each announcement from its market expectation and we jointly model the volatility of the returns accounting for intraday movements and day‐of‐the‐week effects. Our findings show that intraday interactions on the new EU markets are strongly determined by mature stock markets as well as the macroeconomic news originating thereby. We show that strong contemporaneous links across markets are present even after controlling for macroeconomic announcements. Finally, in terms of specific announcements, we are able to show the exact sources of macro news spillovers from the developed foreign markets to the three new EU markets under research.     

Intraday return and volatility relationships between the Ibex 35 spot and futures markets

This paper analyses the intraday lead-lag relationships between returns and volatilities in the Ibex 35 spot and futures markets. Using hourly data, we jointly analyze the interactions between markets, estimating a bivariate error correction model with GARCH perturbations which captures stochastically the presence of an intraday U-shaped curve for both spot and futures market volatility. Our findings show a bidirectional causal relationship between market volatilities, with a positive feedback. This two-way transmission of volatility is consistent with market prices evolving according to a long-run equilibrium relationship, and shocks affecting both markets in the same direction. Our empirical results also support a unidirectional cross interaction from futures to spot market returns. This pattern suggests that the futures market leads the spot market in order to incorporate the arrival of new information.     

Forecasting the oil futures price volatility: A new approach

This study provides a new perspective of modelling and forecasting realized range-based volatility (RRV) for crude oil futures. We are the first to improve the Heterogeneous Autoregressive model of Realized Range-based Volatility (HAR-RRV) model by considering the significant jump components, signed returns and volatility of realized range-based volatility. The empirical results show that the volatility of volatility significantly exists in the oil futures market. Moreover, our new proposed models with significant jump components, signed returns and volatility of volatility can gain higher forecast accuracy than HAR-RRV-type models. The results are robust to different forecasting windows and forecasting horizons. Our new findings are strategically important for investors making better decisions.     

Effects of a price limit change on market stability at the intraday horizon in the Korean stock market

This article investigates the effects of a price limit change on the volatility of the Korean stock market’s (KRX) intraday stock price process. Based on the most recent transaction data from the KRX, which experienced a change in the price limit on 15 June 2015, we examine the change in realized variance after the price limit change to investigate the overall effects of the change on the intraday market volatility. We then analyse the effects in more detail by applying the discrete Fourier transform to the data set. We find evidence that the market becomes more volatile in the intraday horizon because of the increase in the amplitudes of the low-frequency components of the price processes after the price limit change. Therefore, liquidity providers are in a worse situation than they were prior to the change.     

Investigating Intraday Interdependence Between Gold,Silver and Three Major Currencies: the Euro,British Pound and Japanese Yen

In this study, the interrelationship between major exchange rate returns (namely EUR/USD, GBP/USD, JPY/USD) and precious metal returns (gold and silver) is examined using a vector autoregressive model in a multivariate asymmetric GARCH framework on the intraday frequency. Our findings indicate a unidirectional volatility transmission from the majority of our currencies (EUR/USD, GBP/USD) to precious metals. The sluggish response of silver volatility to currency volatility shocks permits implementation of intraday profitable strategies, providing implications against market efficiency when analyzing intraday data. In the case of the British pound and Japanese yen, a volatility shock affects silver volatility more than gold volatility. Crisis events such as the Greek default and US credit rating downgrade reduce significantly the correlation of EUR/USD and gold/silver. The covariance between EUR/USD and silver increases after a volatility shock in EUR/USD. The same happens with JPY/USD and silver. These findings are important for portfolio managers and monetary authorities.     

Further empirical evidence on the forecasting of volatility with smooth transition exponential smoothing

Smooth transition exponential smoothing (STES) uses a logistic function of a user-specified transition variable as adaptive time varying smoothing parameter. This paper empirically addresses three aspects of the use of STES for volatility forecasting. Previous empirical results showed the method performing well in comparison with fixed parameter exponential smoothing and a variety of GARCH models. However, those results related only to forecasting weekly volatility. In this paper, we address the use of STES for forecasting daily volatility. A second issue that we evaluate is the robustness of STES in the presence of extreme outlying observations. The third aspect that we consider is the use of trading volume within a transition variable in the STES method. Our simulation results suggest that STES performs well in terms of robustness, when compared with standard methods and several alternative robust methods. Analysis using stock return data shows that STES has the potential to outperform standard and robust forms of fixed parameter exponential smoothing and GARCH models. The results suggest the use of the sign and size of past shocks as STES transition variables, and provide no clear support for the incorporation of trading volume in a transition variable.     

Volatility forecasting using related markets’ information for the Tokyo stock exchange

Due to lack of information, volatility cannot be estimated via a high-frequency approach when markets are non-trading. In this paper, we focus on volatility forecasting for the Tokyo Stock Exchange (TSE) using high-frequency data of related assets traded in international markets when TSE is closed. We use the heterogenous autoregressive model to identify an optimal approach of this additional information for the ten largest TSE-listed stocks, TOPIX and Nikkei 225. The usefulness of harnessing global and neighbour market information in forecasting the TSE market volatility is confirmed through in-depth empirical analysis. Our findings have important implications for investors and policy makers.     

Which types of commodity price information are more useful for predicting US stock market volatility?

This study aims to investigate which types of commodity price information are more useful for predicting US stock market realized volatility (RV) in a data-rich word. The standard predictive regression framework and monthly RV data are used to explore the RV predictability of commodity futures for the next-month RV on S&P 500 spot index. We utilize principal component analysis (PCA) and factor analysis (FA) to extract the common factors for each type and all types of commodity futures. Our results indicate that the futures volatility information of grains and softs has a significant predictive ability in forecasting the RV of the S&P 500. In addition, the FA method can yield better forecasts than the PCA and average methods in most cases. Further analysis shows that the volatility information of grains and softs exhibits higher informativeness during recessions and pre-crises. Finally, the forecasts of the five combination methods and different out-of-sample periods confirm our results are robust.     

Do liquidity and sampling methods matter in constructing volatility indices? Empirical evidence from Taiwan

This paper proposes four methods by which to sample option prices using proxies for liquidity—1-, 2-, 3-, 7-, and 8-day rollover rules—for option trades in order to construct volatility index series. Based on the sampling method using the average of all midpoints of bid and ask quote option prices, the volatility indices constructed by one-minute tick data have less missing data and are at least as efficient in volatility forecasting as the method suggested by the CBOE. In addition, based on different rollover rules, illiquidity in Taiwan's options market does not lead to substantial errors in the forecasting effectiveness of the volatility indices. Finally, the forecasting ability of VIX based on different sampling methods is found to be superior to that of VXO in Taiwan.     

A range-based volatility approach to measuring volatility contagion in securitized real estate markets

We use a newly-developed time-varying range-based volatility model to capture the dynamics of securitized real estate volatility. The novelty of the model is the use of a smooth transition copula function to capture the nonlinear comovements between major REIT markets in the presence of structural changes. We then investigate the impact of extreme events on the volatility dependence in a broad set of 13 developed countries over the period from 1990 to 2012. We find that information transmission through the volatility channel can exhibit either bi- or uni-directional causality. In addition, financial contagion following the subprime crisis is found between the U.S. and Australia.     

Index arbitrage and dynamics between REIT index futures and spot prices

This article contributes to the real estate literature by investigating the pricing relationship between REIT index futures and spot. Based on the cost-of-carry model, we first show that there exist three arbitrage regimes in Australia’s REIT spot-futures price dynamics. Further analysis indicates that the two thresholds, which separate the regimes, are largely consistent with the level dictated by transaction costs. We then estimate a threshold vector error correction model (TVECM). The results show that mean reversion of the mispricing error only takes place in the two outer regimes. Furthermore, we find evidence that REIT spot market is more informationally efficient than the futures market. Given its short history, it will take time for REIT index futures market to mature. Finally, we find that we can enhance hedging performance by accommodating the feature of threshold cointegration displayed by the data. As the futures-spot relationship differs across regimes, we can develop a hedging strategy by adjusting the hedge ratio based on arbitrage regimes. It leads to a greater variance reduction for the hedged portfolio than some conventional methods examined in the existing real estate literature.     

Conditional market beta for REITs: A comparison of modeling techniques

There has accumulated strong evidence in the literature that market beta (β) is time varying. This paper contributes to the literature by studying how to best model the time varying beta for REITs. We include several commonly used methods and evaluate their performances in terms of in-sample beta estimates and out-of-sample beta forecasts. We apply these methods to U.S. equity REITs. Our results overwhelmingly suggest that the state space model is the best performer. Such a conclusion is supported by different evaluation criteria and robust to different sample splitting. Our findings have direct financial implications. The forecasted betas (preferably through the state space model) can be used in many applications such as estimating the cost of capital for the purpose of capital budgeting involving REITs, identifying equity REIT mispricing, evaluating the performance of managed REIT portfolios, etc.     

Uncertainty and crude oil market volatility: new evidence

ABSTRACT

The main goal of this paper is to investigate the predictability of five economic uncertainty indices for oil price volatility in a changing world. We employ the standard predictive regression framework, several model combination approaches, as well as two prevailing model shrinkage methods to evaluate the performances of the uncertainty indices. The empirical results based on simple autoregression models including only one index suggest that global economic policy uncertainty (GEPU) and US equity market volatility (EMV) indices have significant predictive power for crude oil market volatility. In addition, the model combination approaches adopted in this paper can improve slightly the performances of individual autoregressive models. Lastly, the two model shrinkage methods, namely Elastin net and Lasso, outperform other individual AR-type model and combination models in most forecasting cases. Other empirical results based on alternative forecasting methods, estimation window sizes, high/low volatility and economic expansion/recession time periods further make sure the robustness of our major conclusions. The findings in this paper also have several important economic implications for oil investors.     

Deregulation,news releases,and price discovery

We hypothesize that the firm’s regulatory environment influences the sensitivity of its equity value to information. Using intraday stock price data of utilities operating in differing regulatory environments we test for systematic differences between the responsiveness of stock prices of utility firms operating in deregulated and regulated environments to a common information set. Our findings reveal sharp differences in responses, with those of utilities operating in deregulated environments the strongest, and the responses of utilities in highly regulated environments the weakest. While the evidence supports our hypothesis, in a broader sense, the evidence suggests that deregulation aids in the process of price discovery. We also find evidence that suggests that deregulation, per se, does not lead to higher stock price volatility.      

期货市场量价分析法的短期预测力检验——基于久期理论的分析

久期作为重要的市场微观结构信号,对于改善资产价格波动预测准确性和流动性风险评价具有重要的作用。本文首先提出了反映价格、成交量和持仓量共同变化的共同久期概念,并分析了不同变量高频数据所具有的日内效应特征。然后,在久期理论框架下构建了扩展的二元选择模型,对上海燃料油期货市场量价分析法的短期预测力进行了实证检验。结果表明,期货市场量价分析法中一半以上的经验法则证明是有效的,说明量价分析法在短期价格预测中具有较高的预测力,是值得信赖的重要技术分析方法之一。     

Linear time-varying regression with a DCC-GARCH model for volatility

This article provides a new linear state space model with time-varying parameters for forecasting financial volatility. The volatility estimates obtained from the model by using the US stock market data almost exactly match the realized volatility. We further compare our model with traditional volatility models in the ex post volatility forecast evaluations. In particular, we use the superior predictive ability and the reality check for data snooping. Evidence can be found supporting that our simple but powerful regression model provides superior forecasts for volatility.