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.     

Modeling the volatility of futures return in rubber and oil—A Copula-based GARCH model approach

This paper attempts to make use of a Copula-based GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) Model to find out the relationships between the volatility of rubber futures returns in the Agricultural Futures Exchange of Thailand (AFET) and other four main markets, namely, the volatility of rubber futures returns in the Singapore Commodity Exchange (SICOM), the volatility of rubber futures returns, crude oil returns, and gas oil returns in the Tokyo Commodity Exchange (TOCOM). The results illustrate that the Student-t dependence only shows better explanatory power than the Gaussian dependence structure and the persistence pertaining to the dependence structure between rubber futures returns in AFET and oil futures returns, namely, crude oil futures returns and gas oil futures returns in TOCOM. Whereas, the Gaussian dependence shows better explanatory ability between rubber futures returns in AFET and other rubber futures returns, namely, the volatility of rubber futures in SICOM and TOCOM. For the multivariate Copula model, all the parameters between AFET and other variables are significant. Based on these results, with the liberalization of agricultural trade and the withdrawal of government support to agricultural producers, there is in many countries a new need for price discovery and even physical trading mechanisms, a need that can often be met by commodity futures exchanges. Hence, this paper recommends that the government supports the hedge mutual funds that can be invested in every commodities futures exchange in the world. It can also put the funds together that will contribute farmers to invest in each commodities futures market.     

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.     

Calendar anomalies in cash and stock index futures: International evidence

This paper examines calendar anomalies (day-of-the-week and monthly seasonal effects) in cash and stock index futures returns. We consider daily data from FTSE100 (UK), FTSE/ASE-20 (Greece), S&P500 (US) and Nasdaq100 (US) spot and future indexes over the period 2004–2011. We employ a Regime-Switching specification which allows us to distinguish between different regimes corresponding to high and low volatile periods. The results show differences in the seasonal patterns in cash and futures indexes due to the existence of basis risk. Calendar effects are also conditioned to the market situation. During a low volatile situation these calendar effects tend to be positive, but these effects turn negative if the market is under a high volatile period. These findings are recommended to financial risk managers dealing with futures markets.     

Time-varying relationship of news sentiment,implied volatility and stock returns

I examine the relationship between aggregate news sentiment, S&P 500 index (SPX) returns, and changes in the implied volatility index (VIX). I find a significant negative contemporaneous relationship between changes in VIX and both news sentiment and stock returns. This relationship is asymmetric whereby changes in VIX are larger following negative news and/or stock market declines. Vector autoregression (VAR) analysis of the dynamics and cross-dependencies between variables reveals a strong positive relationship between previous and current period changes in implied volatility and stock returns, while current period and lagged news sentiment has a significant positive (negative) relationship with stock returns (changes in VIX). I develop a simple trading strategy whereby high (low) levels of implied volatility signal attractive opportunities to take short (long) positions in the underlying index, while extremely negative (positive) news sentiment signals opportunities to enter short (long) index positions. The investor fear gauge (VIX) appears to perform better than news sentiment measures in forecasting future returns.     

Forecasting stock market volatility and information content of implied volatility index

This study investigates the incremental information content of implied volatility index relative to the GARCH family models in forecasting volatility of the three Asia-Pacific stock markets, namely India, Australia and Hong Kong. To examine the in-sample information content, the conditional variance equations of GARCH family models are augmented by incorporating implied volatility index as an explanatory variable. The return-based realized variance and the range-based realized variance constructed from 5-min data are used as proxy for latent volatility. To assess the out-of-sample forecast performance, we generate one-day-ahead rolling forecasts and employ the Mincer–Zarnowitz regression and encompassing regression. We find that the inclusion of implied volatility index in the conditional variance equation of GARCH family model reduces volatility persistence and improves model fitness. The significant and positive coefficient of implied volatility index in the augmented GARCH family models suggests that it contains relevant information in describing the volatility process. The study finds that volatility index is a biased forecast but possesses relevant information in explaining future realized volatility. The results of encompassing regression suggest that implied volatility index contains additional information relevant for forecasting stock market volatility beyond the information contained in the GARCH family model forecasts.     

Exploiting volatility movements in the Sydney futures exchange's bank bill contract

An appropriate stochastic model was fitted to one year of data on the implied volatility of options on 90 day bank accepted bill futures contracts traded in the Sydney Futures Exchange. The model used was ARIMA augmented with day of the week variables, an option time to maturity variable, and recent values of historic volatility. The high ex-post predictive accuracy of the model was then employed as the central element of a strategy of buy low/sell high volatility.We employed two trading schemes with suitably constructed Delta neutral portfolios comprising bill futures and call and put options on those futures over a period of six months, to test whether speculative trading profit could be earned. The existence of trading profits before transaction costs validated the potential of the buy low/sell high volatility strategies to generate speculative profits. The absence of any such trading profits after transaction costs however, showed that the market pricing of these securities is such that the dependencies within implied volatility cannot be profitably exploited.This result may be interpreted as evidence supporting an hypothesis of a semi-strong form of market efficiency.     

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 realized volatility of crude oil futures with equity market uncertainty

This paper examines whether the equity market uncertainty (EMU) index contains incremental information for forecasting the realized volatility of crude oil futures. We use 5-min high-frequency transaction data for WTI crude oil futures and develop six heterogeneous autoregressive (HAR) models based on classical HAR-type models. The empirical results suggest that EMU contains more incremental information than the economic policy uncertainty (EPU) for forecasting the realized volatility of crude oil futures. More importantly, we argue that EMU is a non negligible additional predictive variable that can significantly improve the 1-day ahead predictive accuracy of all six HAR-type models, and improve the 1-week ahead forecasting performance of the HAR-RV, HAR-RV-J, HAR-RSV, HAR-RV-SJ models. These findings highlight a strong short-term and a weak mid-term predictive ability of EMU in the crude oil futures market.     

Firm return volatility and economic gains: The role of oil prices

In this paper we investigate whether the oil price contributes to stock return volatility for 560 firms listed on the NYSE. Using daily data, we find that the oil price is a significant determinant and predictor of firm return variance. We devise trading strategies based on forecasts of firm return variance using the oil prices and historical averages. We find that investors can make substantial gains in returns by using the oil price in forecasting firm return variances.     

Forecasting the volatility of crude oil futures using high-frequency data: further evidence

We forecast the realized volatility of crude oil futures market using the heterogeneous autoregressive model for realized volatility and its various extensions. Out-of-sample findings indicate that the inclusion of jumps does not improve the forecasting accuracy of the volatility models, whereas the “leverage effect” pertaining to the difference between positive and negative realized semi-variances can significantly improve the forecasting accuracy in predicting the short- and medium-term volatility. However, the signed jump variations and its decomposition couldn’t significantly enhance the models’ forecasting accuracy on the long-term volatility.     

Price and volatility dynamics between securitized real estate spot and futures markets

This study is among the first to examine the price, volatility and covariance dynamics between securitized real estate spot and futures markets. It provides a distinctive and yet complementary perspective on the predictability of real estate spot return and spot volatility based on the information from the spot market alone. The results show that for the EPRA/NAREIT Europe index, the spot market tends to lead its futures market in the long run during the sample period, which can be attributed to a rather illiquid real estate futures market in sharp contrast with a voluminous spot market. Furthermore, we find the V-shaped asymmetric effect of the basis on the futures market volatility, which represents the primary channel of strong volatility transmission between securitized real estate spot and futures markets during the whole sample and the post-crisis period. This sheds light on the hedging effectiveness for the REIT index.     

Modelling exchange rate volatility with random level shifts

Recent literature has shown that the volatility of exchange rate returns displays long memory features. It has also been shown that if a short memory process is contaminated by level shifts, the estimate of the long memory parameter tends to be upward biased. In this article, we directly estimate a random level shift model to the logarithm of the absolute returns of five exchange rates series, in order to assess whether random level shifts (RLSs) can explain this long memory property. Our results show that there are few level shifts for the five series, but once they are taken into account the long memory property of the series disappears. We also provide out-of-sample forecasting comparisons, which show that, in most cases, the RLS model outperforms popular models in forecasting volatility. We further support our results using a variety of robustness checks.     

Volatility spillover and multivariate volatility impulse response analysis of GFC news events

This article applies two measures to assess spillovers across markets: the Diebold and Yilmaz’s (2012) spillover index and the Hafner and Herwartz’s (2006) analysis of multivariate GARCH models using volatility impulse response analysis. We use two sets of data, daily realized volatility (RV) estimates taken from the Oxford-Man RV library, for the S&P500 and the FTSE, plus 10 years of daily returns series for the New York Stock Exchange Index and the FTSE 100 index. Both data sets capture both the global Financial Crisis (GFC) and the subsequent European Sovereign Debt Crisis (ESDC). The spillover index captures the transmission of volatility to and from markets, plus net spillovers. The Volatility Impulse Responses (VIRF) have to be calibrated to conditional volatility estimated at a particular point in time. We explore the impact of three different shocks, the onset of the GFC, the height of the GFC, and the impact of the ESDC. Our modelling includes leverage and asymmetric effects applying a multivariate GARCH model, and further analysis using both BEKK and diagonal BEKK (DBEKK) models. We find the impact of negative shocks is larger, but shorter in duration, in this case a difference between 3 and 6 months.     

Structural breaks and GARCH models of stock return volatility: The case of South Africa

This paper investigates the empirical relevance of structural breaks in forecasting stock return volatility using both in-sample and out-of-sample tests applied to daily returns of the Johannesburg Stock Exchange (JSE) All Share Index from 07/02/1995 to 08/25/2010. We find evidence of structural breaks in the unconditional variance of the stock returns series over the period, with high levels of persistence and variability in the parameter estimates of the GARCH(1,1) model across the sub-samples defined by the structural breaks. This indicates that structural breaks are empirically relevant to stock return volatility in South Africa. However, based on the out-of-sample forecasting exercise, we find that even though there structural breaks in the volatility, there are no statistical gains from using competing models that explicitly accounts for structural breaks, relative to a GARCH(1,1) model with expanding window. This could be because of the fact that the two identified structural breaks occurred in our out-of-sample, and recursive estimation of the GARCH(1,1) model is perhaps sufficient to account for the effect of the breaks on the parameter estimates. Finally, we highlight that, given the point of the breaks, perhaps what seems more important in South Africa, is accounting for leverage effects, especially in terms of long-horizon forecasting of stock return volatility.     

Momentum and reversals in Taiwan index futures returns during periods of extreme trading imbalance

The study analyzes the relation between a trading imbalance metric that captures data observable by investors, and future momentum and reversals in Taiwan index futures returns. Standard regression analyses do not show any significant dynamic relations between daily index futures returns and the trading imbalance, regardless of whether the trading imbalance metric is lagged, contemporaneous, or leads the index futures return. However, when the analyses are focused on periods with extreme trading imbalances I find that the daily index futures returns exhibit significant reversals following periods of extreme (low) trading imbalances and low returns. I also find some evidence of residual momentum in consecutive daily index futures returns following periods of extreme (high) trading imbalances and high returns. Trading simulation, directional accuracy, and market timing tests show these effects to be economically significant, even after accounting for transaction costs.     

Value at risk estimation by threshold stochastic volatility model

This article proposes a threshold stochastic volatility model that generates volatility forecasts specifically designed for value at risk (VaR) estimation. The method incorporates extreme downside shocks by modelling left-tail returns separately from other returns. Left-tail returns are generated with a t-distributional process based on the historically observed conditional excess kurtosis. This specification allows VaR estimates to be generated with extreme downside impacts, yet remains empirically widely applicable. This article applies the model to daily returns of seven major stock indices over a 22-year period and compares its forecasts to those of several other forecasting methods. Based on back-testing outcomes and likelihood ratio tests, the new model provides reliable estimates and outperforms others.     

Breaks or long range dependence in the energy futures volatility: Out-of-sample forecasting and VaR analysis

The aim of this paper is to propose an empirical strategy that allows the discrimination between true and spurious long memory behaviors. That strategy is based on the comparison between the estimated long memory parameter before and after filtering out the breaks. To date the breaks, we use the probability smoothing of the Markov Switching GARCH model of Haas et al. (2004). Application of this strategy to the crude oil, heating oil, RBOB regular gasoline and the propane futures energy with the one, two, three and four months maturities show strong evidence for the presence of long range dependence in all futures energy prices volatility¹ time series. This result of long range dependence in the volatility is confirmed by the superiority of the FIGARCH and FIEGARCH models compared with the Markov switching GARCH models in terms of out-of-sample forecasting and value at risk (VaR) performances. Moreover, we show that the proposed empirical strategy is robust to different data frequency. Practical implications of the results for market participants are proposed and discussed.     

Volatility and VaR forecasting in the Madrid Stock Exchange

This paper provides an empirical study to assess the forecasting performance of a wide range of models for predicting volatility and VaR in the Madrid Stock Exchange. The models performance was measured by using different loss functions and criteria. The results show that FIAPARCH processes capture and forecast more accurately the dynamics of IBEX-35 returns volatility. It is also observed that assuming a heavy-tailed distribution does not improve models ability for predicting volatility. However, when the aim is forecasting VaR, we find evidence of that the Student’s t FIAPARCH outperforms the models it nests the lower the target quantile.      

Options-based forecasts of futures prices in the presence of limit moves

The reported analysis examines a simultaneous estimation option-based approach to forecast futures prices in the presence of daily price limit moves. The procedure explicitly allows for changing implied volatilities by estimating the implied futures price and the implied volatility simultaneously. Using futures and futures options data for three agricultural commodities, it is found that the simultaneous estimation approach accounts for the abrupt changes in implied volatility associated with limit moves and generates more accurate price forecasts than conventional methods that rely on only one implied variable.