Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH‑MIDAS model

This study investigates the impact of economic policy uncertainty(EPU)on the volatility of European Union(EU)carbon futures prices and whether it has predictive power for the volatility of carbon futures prices.The GARCH-MIDAS model is applied for evaluating the impact of different EPU indexes on the price volatility of European Union Allowance(EUA)futures.We then compare the predictive power for the volatility of the two GARCH-MIDAS models based on different EPU indexes and six GARCH-type models.Our empirical results show that the GARCH-MIDAS models,which exhibit superior out-of-sample predictive ability,outperform GARCH-type models.The results also indicate that EPU has noticeable effect on the volatility of EUA futures.Specifically,the forecast accuracy of the EU EPU index is significantly higher than that of the global EPU index.Robustness checks further confirm that the EPU index(especially the EPU index of the EU)has strong predictive power for EUA futures prices.Additionally,using the volatility forecasting methods that GARCH-MIDAS models combine with the EPU index,investors can construct their portfolios to realize economic returns.

Volatility forecasting in Chinese nonferrous metals futures market

This paper seeks to model and forecast the Chinese nonferrous metals futures market volatility and allows new insights into the time-varying volatility of realized volatility and leverage effects using high-frequency data.The LHAR-CJ model is extended and the empirical research on copper and aluminum futures in Shanghai Futures Exchange suggests the dynamic dependencies and time-varying volatility of realized volatility,which are captured by long memory HAR-GARCH model.Besides,the findings also show the significant weekly leverage effects in Chinese nonferrous metals futures market volatility.Finally,in-sample and out-of-sample forecasts are investigated,and the results show that the LHAR-CJ-G model,considering time-varyingvolatility of realized volatility and leverage effects,effectively improves the explanatory power as well as out-of sample predictive performance.

Comparative Study of Volatility Forecasting Models： The Case of Malaysia, Indonesia, Hong Kong and Japan Stock Markets

This paper aims to investigate the effectiveness of four volatility forecasting models, i.e. Exponential Weighted Moving Average （EWMA）, Autoregressive Integrated Moving Average （ARIMA） and Generalized Auto-Regressive Conditional Heteroscedastic （GARCH）, in four stock markets Indonesia, Malaysia, Japan and Hong Kong. Using monthly closing stock index prices collected from 1 st January 1998 to 31 st December 2015 for the four selected countries, results obtained confirm that volatility in developed markets is not necessarily always lower than the volatility in emerging markets. Among all the three models, GARCH （1, l） model is found to be the best forecasting model for stock markets in Malaysia, Indonesia, and Japan, while EWMA model is found to be the best forecasting model for Hong Kong stock market. The outperformance of GARCH （1, 1） found supports again what is found in Minkah （2007）.

To jump or not to jump:momentum of jumps in crude oil price volatility prediction

A well-documented finding is that explicitly using jumps cannot efficiently enhance the predictability of crude oil price volatility.To address this issue,we find a phenomenon,“momentum of jumps”(MoJ),that the predictive ability of the jump component is persistent when forecasting the oil futures market volatility.Specifically,we propose a strategy that allows the predictive model to switch between a benchmark model without jumps and an alternative model with a jump component according to their recent past forecasting performance.The volatility data are based on the intraday prices of West Texas Intermediate.Our results indicate that this simple strategy significantly outperforms the individual models and a series of competing strategies such as forecast combinations and shrinkage methods.A mean–variance investor who targets a constant Sharpe ratio can realize the highest economic gains using the MoJ-based volatility forecasts.Our findings survive a wide variety of robustness tests,including different jump measures,alternative volatility measures,various financial markets,and extensive model specifications.

Forecasting China′s Stock Market Volatility Using Non-Linear GARCH Models

This paper studies the performance of the GARCH model and two of its non linear modifications to forecast China′s weekly stock market volatility. The models are the Quadratic GARCH and the Glosten, Jagannathan and Runkle models which have proposed to describe the often observed negative skewness in stock market indices. We find that the QGARCH model is best when the estimation sample does not contain extreme observations and that the GJR model cannot be recommended for forecasting.

A hybrid econometrics and machine learning based modeling of realized volatility of natural gas

Determining which variables affect price realized volatility has always been challenging.This paper proposes to explain how financial assets influence realized volatility by developing an optimal day-to-day forecast.The methodological proposal is based on using the best econometric and machine learning models to forecast realized volatility.In particular,the best forecasting from heterogeneous autoregressive and long short-term memory models are used to determine the influence of the Standard and Poor’s 500 index,euro-US dollar exchange rate,price of gold,and price of Brent crude oil on the realized volatility of natural gas.These financial assets influenced the realized volatility of natural gas in 87.4% of the days analyzed;the euro-US dollar exchange rate was the primary financial asset and explained 40.1% of the influence.The results of the proposed daily analysis differed from those of the methodology used to study the entire period.The traditional model,which studies the entire period,cannot determine temporal effects,whereas the proposed methodology can.The proposed methodology allows us to distinguish the effects for each day,week,or month rather than averages for entire periods,with the flexibility to analyze different frequencies and periods.This methodological capability is key to analyzing influences and making decisions about realized volatility.

A MULTISCALE MODELING APPROACH INCORPORATING ARIMA AND ANNS FOR FINANCIAL MARKET VOLATILITY FORECASTING

The financial market volatility forecasting is regarded as a challenging task because of irreg ularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original financial series are decomposed firstly different scale components （i.e., approximation and details） using the maximum overlap discrete wavelet transform （MODWT）. The approximation is pre- dicted by a hybrid forecasting model that combines autoregressive integrated moving average （ARIMA） with feedforward neural network （FNN）. ARIMA model is used to generate a linear forecast, and then FNN is developed as a tool for nonlinear pattern recognition to correct the estimation error in ARIMA forecast. Moreover, details are predicted by Elman neural networks. Three weekly exchange rates data are collected to establish and validate the forecasting model. Empirical results demonstrate consistent better performance of the proposed approach.

Adding dummy variables: A simple approach for improved volatility forecasting in electricity market

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility.Considering day-of-the-week effects,structural breaks,or both,we propose three classes of HAR models to forecast electricity volatility based on existing HAR models.The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon,whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily,weekly,and monthly horizons.The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility,and in most cases,dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects.The out-of-sample results were robust across three different methods.More importantly,we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.

A Study on the Volatility of the Bangladesh Stock Market--Based on GARCH Type Models

The generalized autoregressive conditional heteroskedasticity(GARCH) type models are used to investigate the volatility of Bangladesh stock market. The findings of the study demonstrate that the index volatility characteristics changes over time. The article shows that the data are divided into three sub-periods: pre crisis, crisis, and post crisis. Accordingly, the results of the findings indicate changes in the GARCH-type models parameter, risk premium and persistence of volatility in different periods. A significant "low-yield associated with high-risk" phenomenon is detected in the crisis period and the "leverage effect" occurs in each periods. The investors are irrational which is based on assumption of risk and return characteristics of assets. Consequently, the market is not as mature as developed market. It is found in the article that the threshold generalized autoregressive conditional heteroskedasticity(TGARCH) model is more accurate for the model accuracy. Additionally, statistic error measurements indicate that GARCH model is more efficient than others and it has also more forecasting ability.

Realized volatility forecast of financial futures using timevarying HAR latent factor models

We forecast realized volatilities by developing a time-varying heterogeneous autoregressive(HAR)latent factor model with dynamic model average(DMA)and dynamic model selection(DMS)approaches.The number of latent factors is determined using Chan and Grant's(2016)deviation information criteria.The predictors in our model include lagged daily,weekly,and monthly volatility variables,the corresponding volatility factors,and a speculation variable.In addition,the time-varying properties of the best-performing DMA(DMS)-HAR-2FX models,including size,inclusion probabilities,and coefficients,are examined.We find that the proposed DMA(DMS)-HAR-2FX model outperforms the competing models for both in-sample and out-of-sample forecasts.Furthermore,the speculation variable displays strong predictability for forecasting the realized volatility of financial futures in China.

The role of oil futures intraday information on predicting US stock market volatility

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework.In-sample results indicate that oil futures intraday information is helpful to increase the predictability.Moreover,compared to the benchmark model,the proposed models improve their predictive ability with the help of oil futures realized volatility.In particular,the multivariate HAR model outperforms the univariate model.Accordingly,considering the contemporaneous connection is useful to predict the US stock market volatility.Furthermore,these findings are consistent across a variety of robust checks.