Forecasting the return distribution using high-frequency volatility measures

The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a benchmark GARCH model. The results suggest that the model outperforms an asymmetric GARCH specification when applied to the S&P 500 futures returns, in particular on the right tail of the distribution. However, the model provides similar accuracy to a GARCH (1, 1) model when the 30-year Treasury bond futures return is considered.     

Forecasting volatility of the U.S. oil market

We examine the information content of the CBOE Crude Oil Volatility Index (OVX) when forecasting realized volatility in the WTI futures market. Additionally, we study whether other market variables, such as volume, open interest, daily returns, bid-ask spread and the slope of the futures curve, contain predictive power beyond what is embedded in the implied volatility. In out-of-sample forecasting we find that econometric models based on realized volatility can be improved by including implied volatility and other variables. Our results show that including implied volatility significantly improves daily and weekly volatility forecasts; however, including other market variables significantly improves daily, weekly and monthly volatility forecasts.     

Modeling CO2 emission allowance prices and derivatives: Evidence from the European trading scheme

This paper studies the three main markets for emission allowances within the European Union Emissions Trading Scheme (EU ETS): Powernext, Nord Pool and European Climate Exchange (ECX). The analysis suggests that the prohibition of banking of emission allowances between distinct phases of the EU ETS has significant implications in terms of futures pricing. Motivated by these findings, we develop an empirically and theoretically valid framework for the pricing and hedging of intra-phase and inter-phase futures and options on futures, respectively.     

Price impact of block trades: the curious case of downstairs trading in the EU emissions futures market

Using high-frequency data from the European Climate Exchange (ECX), we examine the determinants of price impact of €21 billion worth of block trades during 2008–2011 in the European carbon market. We find that wider bid-ask spreads and volatility are characterised by a smaller price impact. Larger levels of price impact are more likely to occur during the middle of the trading day, specifically the four-hour period between 11 a.m. and 3 p.m., than during the first or final hours. Purchase block trades induce a relatively smaller price impact on price run-up, while sell block trades exhibit a larger price impact on price run-up. We conclude that block trades on the ECX induce less price impact than in equity or conventional futures markets, and that a significant proportion of the effects contradict findings on block trades in those markets; thus, we provide the first evidence of the curious bent to block trading in the European Union emissions trading scheme.     

Forecasting Value-at-Risk using high frequency data: The realized range model

Current studies on financial market risk measures usually use daily returns based on GARCH type models. This paper models realized range using intraday high frequency data based on CARR framework and apply it to VaR forecasting. Kupiec LR test and dynamic quantile test are used to compare the performance of VaR forecasting of realized range model with another intraday realized volatility model and daily GARCH type models. Empirical results of Chinese Stock Indices show that realized range model performs the same with realized volatility model, which performs much better than daily models.     

INTRADAY MARKET BEHAVIOR AND THE EXTENT OF FEEDBACK BETWEEN S&P 500 FUTURES PRICES AND THE S&P 500 INDEX

In this study we empirically examine the intraday lead/lag relation between S&P 500 futures prices and the S&P 500 index, and whether daily market characteristics are associated with changes in the relation. We estimate daily Geweke measures of feedback and regress time series of these measures on daily price volatility and volume characteristics. Results indicate that the contemporaneous price relation is substantive and that measures of contemporaneous feedback are positively associated with the daily range of the futures price. The primary implication is that the relation between cash and futures prices becomes stronger as futures price volatility increases. As volatility increases, information is being impounded at a faster rate so that futures and equity markets operate more closely as one market. Large futures price moves, by themselves, are not responsible for breakdowns in the stock-futures price relation.     

Realized volatility spillovers between US spot and futures during ECB news: Evidence from the European sovereign debt crisis

We study the simultaneity impact of the European Central Bank news on the daily realized volatility transmission mechanism (spillovers) among various US spot and futures markets. To this end, we apply a bias-corrected vector autoregressive model via Wild bootstrap simulation. We use minute-by-minute intraday data to construct daily realized volatility. We consider 429 news form the ECB as important events employing two major classifications, namely, a country classification with the highest total number of days related ECB news and a type of ECB news classification. We find that investors in futures markets react more vigorously and mainly for the ECB news that is associated with the group of EMU member states applied structural reforms. Yet, more importantly, we show that the US stock markets response heterogeneously to the ECB news, as we find key disagreements in the reactions both across the US markets and the types of ECB news studied. Such evidence is consistent with the explanation of the differential interpretation of information among market participants. From a practical point of view, we suggest that investors in the US spot market can effectively use two or more futures contracts to minimize their exposure to volatility risk associated with that news.     

投资者情绪、期权隐含信息与股市波动率预测——基于上证50ETF期权的经验研究

在异质自回归模型(HAR-RV)中引入中国上证50ETF期权隐含信息和投资者情绪,本文分别对中国股票市场未来日、周和月波动率进行预测。研究发现,期权隐含信息和投资者情绪能够提高HAR-RV模型对股票市场未来波动率的预测效果。投资者情绪对未来波动率的影响存在两种机制:在情绪高涨期间,月已实现波动率与未来波动率正相关,说明以个人投资者占主体所引起的价格信息机制,在中国股票市场交易中占主导作用;风险中性偏度与未来波动率负相关,说明以个人投资者占主体所引起的噪声交易机制占主导作用。     

How Crashes Develop: Intradaily Volatility and Crash Evolution

This paper explores whether affine models with volatility jumps estimated on intradaily S&P 500 futures data over 1983 to 2008 can capture major daily outliers such as the 1987 stock market crash. Intradaily jumps in futures prices are typically small; self‐exciting but short‐lived volatility spikes capture intradaily and daily returns better. Multifactor models of the evolution of diffusive variance and jump intensities improve fits substantially, including out‐of‐sample over 2009 to 2016. The models capture reasonably well the conditional distributions of daily returns and realized variance outliers, but underpredict realized variance inliers. I also examine option pricing implications.     

Modeling the relationship between European carbon permits and certified emission reductions

Recent years have seen an expansion of carbon markets around the world as various policymakers attempt to reduce CO2 emissions. This paper considers two of the major types of carbon permits: European Union Allowances (EUAs, arising from the European Union Emissions Trading Scheme, EU ETS) and certified emissions reductions (CERs, arising from agreements made under the Kyoto Protocol). The rules of the EU ETS allow for some use of CERs in place of EUAs by EU firms, but this substitutability is only partial. Allowing for carbon permits from different sources to substitute for one another should help achieve CO2 emissions reductions at least cost. Understanding the degree and nature of linkages (if any) between the markets for EUAs and CER is, thus, an important policy issue. In this paper, we jointly model the spot and future prices of an EUA along with the price of a CER using flexible multivariate time series methods which allow for time-variation in parameters. We find evidence of contemporaneous causality between these three variables with the EUA futures price playing the dominant role in driving this relationship. We also document time-variation in this relationship which is associated with macroeconomic events such as the financial crisis of late 2008 and early 2009. We find very little evidence of volatility spillovers or of Granger causality among any of the variables. We discuss how these empirical findings are consistent with markets which are loosely linked, but are not tightly linked as would be found for perfectly substitutable assets in efficient financial markets.     

Detecting and modelling the jump risk of CO2 emission allowances and their impact on the valuation of option on futures contracts

Modelling CO₂ emission allowance prices is important for pricing CO₂ emission allowance linked assets in the emissions trading scheme (ETS). Some statistical properties of CO₂ emission allowance prices have been discovered in the literature ignoring price jumps. By employing real data from the ETS, this research first detects the jump risk using a jump test and then verifies jump effects in modelling CO₂ emission allowance prices by comparing the in-sample and out-of-sample model performance. We suggest a model which can capture the statistical properties of autocorrelation, volatility clustering and jump effects is more appropriate for modelling CO₂ emission allowance prices. We establish a general framework for pricing CO₂ emission allowance options on futures contracts with these properties and find that the jump risk significantly affects the value of the CO₂ emission allowance option on futures contracts. More importantly, we demonstrate that the dynamic jump ARMA–GARCH model can provide more accurate valuations of the CO₂ emission allowance options on futures than other models in terms of pricing error.     

Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models

The study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naïve models to the relatively complex ARCH-class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH-M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility.     

News-based sentiment and bitcoin volatility

In this work, I studied whether news media sentiments have an impact on Bitcoin volatility. In doing so, I applied three different range-based volatility estimates along with two different sentiments, namely psychological sentiments and financial sentiments, incorporating four various sentiment dictionaries. By analyzing 17,490 news coverages by 91 major English-language newspapers listed in the LexisNexis database from around the globe from January 2012 until August 2021, I found news media sentiments to play a significant role in Bitcoin volatility. Following the heterogeneous autoregressive model for realized volatility (HAR-RV)—which uses the heterogeneous market idea to create a simple additive volatility model at different scales to learn which factor is influencing the time series—along with news sentiments as explanatory variables, showed a better fit and higher forecasting accuracy. Furthermore, I also found that psychological sentiments have medium-term and financial sentiments have long-term effects on Bitcoin volatility. Moreover, the National Research Council Emotion Lexicon showed the main emotional drivers of Bitcoin volatility to be anticipation and trust.     

Information content of options trading volume for future volatility: Evidence from the Taiwan options market

This study follows the approach of Ni et al. [Ni, S.X., Pan, J., Poteshman, A.M., 2008. Volatility information trading in the option market. Journal of Finance 63, 1059–1091] – based upon the vega-weighted net demand for volatility – to determine whether volatility information exists within the Taiwan options market. Our empirical results show that foreign institutional investors possess the strongest and most direct volatility information, which is realized by the delta-neutral options/futures trades. In addition, a few individual investors (less than 1% of individuals’ trades) might be informed and realize their volatility information using the strangle strategy. Surprisingly, we find no evidence to support the predictive ability of the volatility demand from straddle trades, despite the widespread acknowledgement that such trades are sensitive to volatility.     

Dynamic hedging with futures: a copula-based GARCH model with high-frequency data

Modeling the joint distribution of spot and futures returns is crucial for establishing optimal hedging strategies. This paper proposes a new class of dynamic copula-GARCH models that exploits information from high-frequency data for hedge ratio estimation. The copula theory facilitates constructing a flexible distribution; the inclusion of realized volatility measures constructed from high-frequency data enables copula forecasts to swiftly adapt to changing markets. By using data concerning equity index returns, the estimation results show that the inclusion of realized measures of volatility and correlation greatly enhances the explanatory power in the modeling. Moreover, the out-of-sample forecasting results show that the hedged portfolios constructed from the proposed model are superior to those constructed from the prevailing models in reducing the (estimated) conditional hedged portfolio variance. Finally, the economic gains from exploiting high-frequency data for estimating the hedge ratios are examined. It is found that hedgers obtain additional benefits by including high-frequency data in their hedging decisions; more risk-averse hedgers generate greater benefits.     

The volatility of temperature and pricing of weather derivatives

We propose an Ornstein–Uhlenbeck process with seasonal volatility to model the time dynamics of daily average temperatures. The model is fitted to approximately 45 years of daily observations recorded in Stockholm, one of the European cities for which there is a trade in weather futures and options on the Chicago Mercantile Exchange. Explicit pricing dynamics for futures contracts written on the number of heating/cooling degree-days (so-called HDD/CDD futures) and the cumulative average daily temperature (so-called CAT futures) are calculated, along with a discussion on how to evaluate call and put options with these futures as underlying.     

The relationship between futures trading activity and exchange rate volatility,revisited

This paper examines the relationship between trading activity in currency futures and exchange rate volatility. In order to measure trading activity, the paper uses both volume and open interest to distinguish between speculators/day traders and hedgers. The study uses three different measures of volatility: (1) the extreme value estimator that measures intra-day volatility; (2) historical volatility; and (3) conditional volatility from the GARCH (1, 1) process. Main finding is that speculators and day traders destabilize the market for futures. Whether hedgers stabilize or destabilize the market is inconclusive. The results suggest that speculators’ demand for futures goes down in response to increased volatility. Meanwhile, the demand from hedgers shows mixed results, depending on the method used to measure volatility.     

Quantile forecasts of daily exchange rate returns from forecasts of realized volatility

Quantile forecasts are central to risk management decisions because of the widespread use of Value-at-Risk. A quantile forecast is the product of two factors: the model used to forecast volatility, and the method of computing quantiles from the volatility forecasts. In this paper we calculate and evaluate quantile forecasts of the daily exchange rate returns of five currencies. The forecasting models that have been used in recent analyses of the predictability of daily realized volatility permit a comparison of the predictive power of different measures of intraday variation and intraday returns in forecasting exchange rate variability. The methods of computing quantile forecasts include making distributional assumptions for future daily returns as well as using the empirical distribution of predicted standardized returns with both rolling and recursive samples. Our main findings are that the Heterogenous Autoregressive model provides more accurate volatility and quantile forecasts for currencies which experience shifts in volatility, such as the Canadian dollar, and that the use of the empirical distribution to calculate quantiles can improve forecasts when there are shifts.     

Modeling interest rate volatility: A Realized GARCH approach

We propose using a Realized GARCH (RGARCH) model to estimate the daily volatility of the short-term interest rate in the euro–yen market. The model better fits the data and provides more accurate volatility forecasts by extracting additional information from realized measures. In addition, we propose using the ARMA–Realized GARCH (ARMA–RGARCH) model to capture the volatility clustering and the mean reversion effects of interest rate behavior. We find the ARMA–RGARCH model fits the data better than the simple RGARCH model does, but it does not provide superior volatility forecasts.     

基于CVaR-SV-N模型的股指期货风险度量

CVaR-SV-N模型能够更好地刻画股指期货收益率序列尖峰、厚尾和波动集群性的特征。以我国沪深300指数期货合约(IF1012)的日收益率为样本的实证分析表明建立在SV-N模型基础上的CVaR预测收益率涨跌波动与原始收益率的变化趋势比较一致,CVaR准确性检验说明CVaR预测收益的准确性在统计上是显著的,能够较准确地预测风险。