Search

This study employs the multivariate trend asymmetric GARCH-MIDAS (TAGM) model, an extension of the GARCH-MIDAS model, to explore the potential asymmetric impact of uncertainty shocks, including oil and infectious disease shocks, on the long-term volatility of China's listed tourism firms. Furthermore, we test the out-of-sample forecasting accuracy of uncertainty shocks to China's listed tourism firms' risk, which is measured by the volatility of tourism stocks after the outbreak of coronavirus disease 2019 (COVID-19). The results show that uncertainty shocks have a significant asymmetric effect on the long-run volatility of tourism stocks. The included uncertainty shocks improved accuracy in forecasting China's listed tourism firms' risk after the pandemic outbreak. The empirical results have important implications for tourism investment strategies in unstable environments. [ABSTRACT FROM AUTHOR]

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques based on machine learning can be employed when treating volatility as a univariate, daily time-series. Moreover, econometric studies have shown that increasing the number of daily observations with high-frequency intraday data helps to improve volatility predictions. In this work, we propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical findings demonstrate that dilated convolutional filters are highly effective at extracting relevant information from intraday financial time-series, proving that this architecture can effectively leverage predictive information present in high-frequency data that would otherwise be lost if realised measures were precomputed. Simultaneously, dilated convolutional filters trained with intraday high-frequency data help us avoid the limitations of models that use daily data, such as model misspecification or manually designed handcrafted features, whose devise involves optimising the trade-off between accuracy and computational efficiency and makes models prone to lack of adaptation into changing circumstances. In our analysis, we use two years of intraday data from NASDAQ-100 to evaluate the performance of DeepVol. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data, resulting in more accurate predictions compared to traditional methodologies and producing more accurate risk measures. [ABSTRACT FROM AUTHOR]

This study develops a novel approach for improving stock return volatility forecasts using volatility index information with the entropic tilting technique. Unlike traditional linear heteroskedasticity autoregressive methods with option‐implied information, we first derive predictive densities from traditional models, and then tilt using both the first and second moments of the risk‐neutral distribution, which enables us to capture the nonlinear effect in our specification. The empirical findings demonstrate a substantial enhancement in the forecasting accuracy of all models once the first‐ and second‐moment information is considered, where the improvement is both statistically and economically significant. These results have important implications for risk management in well‐established derivatives markets. [ABSTRACT FROM AUTHOR]

Based on empirical evidence of the Chinese commodity futures volatility dynamics, we propose a novel and flexible hybrid model, denoted as SAE‐HAR‐DL, which combines a supervised autoencoder (AE) with the deep learning‐based HAR model framework to capture essential common factor information and uses the reconstruction error of the AE component as a regularizer to enhance the generalization ability of the testing subsample. The empirical findings strongly support the effectiveness of this model in accurately forecasting crude oil futures volatility in the post‐COVID‐19 era, compared to the HAR, HAR‐PCA, and HAR‐DL models. Moreover, a robustness check also demonstrates the positive contribution of common factors to the volatility prediction of other commodity futures. Notably, we establish that these common factors act as effective regularizers, mitigating prediction losses within the HAR model in extreme risk events such as the COVID‐19 pandemic and the Russia–Ukraine conflict. [ABSTRACT FROM AUTHOR]

In this paper, the self‐monitoring learning model FinBERT is used to identify text emotions, and the sliding time window time‐lagged cross‐correlation (WTLCC) method is utilized to screen Baidu Index keywords for the Shanghai Stock Exchange Index and 18 A‐share listed companies. There are five different types of indicators constructed: news media sentiment, public attention, investor sentiment, investor sentiment disagreement, and media sentiment disagreement. To accurately describe the structure of sentimental contagion, this paper combines graph neural network to learn and output the sentimental contagion graph, and then constructs multivariable time series forecasting with graph neural networks (MTGNN) volatility forecasting model, which can extract the spatial–temporal dependence of variables in pairs. The results show that MTGNN model possesses the highest forecasting accuracy, which performs 30.30% lower on average across four evaluation indicators for Shanghai Stock Exchange Index than temporal pattern attention–long short‐term memory model, which ranks second. For all of the models considered in this paper, adding sentimental contagion mechanism can significantly improve the volatility forecasting accuracy. The error of MTGNN is reduced the most, with a 15.21% average reduction for the Shanghai Stock Exchange Index. The contagion relationship among media reports, investor sentiment, and attention can help provide new ideas for enhancing the precision of volatility forecasting from the public opinion environment in the financial market. [ABSTRACT FROM AUTHOR]

The contemporary world faces significant challenges in energy crises and climate change. To analyze the relationship between energy and climate, we explore the influence of the climate-related attention of G20 countries on renewable energy stock volatility forecasting under the framework of the extended GARCH-MIDAS model. In the context of COP26, we further adopt natural language processing technology and shrinkage approach to obtain Google search volume for 107 climate-related keywords and then construct new climate risk attention indicators. The in-sample parameter estimation results show that the climate attention of G20 countries has a remarkable positive effect on the renewable energy stock market volatility. The out-of-sample results demonstrate that the climate attention of different countries exerts varying influences on the volatility of the renewable energy stock market. Climate risk and energy issues are among the serious challenges facing the 21st century, and reducing greenhouse gas emissions and finding cleaner energy is an urgent task. As the response to climate change necessitates diverse strategies in various countries, our research can offer valuable guidance and serve as a reference for national energy transitions and the selection of alternative energy solutions. [ABSTRACT FROM AUTHOR]

This paper examines whether the information contained in geopolitical risk (GPR) can improve the forecasting power of price volatility for carbon futures traded in the EU Emission Trading System. We employ the GARCH-MIDAS model and its extended forms to estimate and forecast the price volatility of carbon futures using the most informative GPR indicators. The models are examined for both statistical and economic significance. According to the results of the Model Confidence Set tests for the full-sample and sub-sample data, we find that the extended model, which accounts for the threat of geopolitical risk, exhibits superior forecasting ability for the full-sample data, while the model that includes drastic changes in geopolitical risk in Phase II and the model that considers serious geopolitical risk in Phase III have the best predictive power. Moreover, all GPR-related variables we use contribute to increasing economic gains. In particular, the threat of geopolitical risk contains valuable information for future EUA futures volatility and can provide the highest economic gains. Therefore, carbon market investors and policymakers should pay great attention to geopolitical risk, especially its threat, in risk and portfolio management. [ABSTRACT FROM AUTHOR]

This paper explores the predictive role of speculative sentiment on gold market volatility and its economic implications. Utilizing high frequency data for gold futures and speculative sentiment proxies for the gold and stock markets, we show that incorporating speculative sentiment in volatility models can improve volatility forecasts both in- and out-of-sample. While gold market sentiment helps improve the predictive accuracy of volatility models primarily in the short run, we show that cross-market speculative sentiment from the stock market has predictive ability at relatively longer forecast horizons. More importantly, incorporating speculative sentiment proxies in volatility models yields improved cumulative returns as well as Sharpe ratios for investors who allocate part of their portfolios in this traditional safe haven asset. Finally, examining the predictability of asymmetric risk patterns, we show that speculative sentiment in the gold market matters relatively more for good volatility forecasts and in the short and intermediate forecast horizons, while cross-market sentiment effect from the stock market generally applies to longer term forecasts for both the good and bad volatility metrics. Overall, our findings show that speculative sentiment contains significant incremental information over subsequent volatility patterns in gold, offering sizeable economic gains for a mean-variance investor in a practical investment setting. [ABSTRACT FROM AUTHOR]

Building on the GJR-GARCH model, this paper uses the mixed-data sampling (MIDAS) approach to link monthly realized volatility of EU carbon future prices and macroeconomic variables to the volatility of EU carbon futures market and proposes the GJR-GARCH-MIDAS model incorporating macroeconomic variables including the economic sentiment indicator of the EU, the harmonized index of consumer prices of the EU, the European economic policy uncertainty index and ECB's marginal lending facility rate (GJR-GARCH-MIDAS-X models). An empirical analysis based on the monthly macroeconomic variables and daily EUA futures data shows that the above four low-frequency macroeconomic variables have significant positive or negative impacts on the long-term volatility of EUA future prices, respectively. The GJR-GARCH-MIDAS-X models significantly outperform other competing models, including the GJR-GARCH model, GARCH-MIDAS model and standard GJR-GARCH-MIDAS model, in terms of out-of-sample volatility forecasting, which suggests that macroeconomic variables contain important information for EUA future price volatility forecasts. In particular, the GJR-GARCH-MIDAS model with harmonized index of consumer prices (HICP) (GJR-GARCH-MIDAS-HICP model) performs best in out-of-sample volatility forecasting, and our findings are robust to different forecasting windows. [ABSTRACT FROM AUTHOR]

Volatility forecasting, a central issue in financial risk modelling and management, has attracted increasing attention after several major financial market crises. In this article, we draw upon the literature on volatility forecasting and hybrid models to construct the Hybrid‐long short‐term memory (LSTM) models to forecast the intraday realized volatility in three major US stock indexes. We construct the hybrid models by combining one or multiple traditional time series models with the LSTM model, and incorporating either the estimated parameters, or the predicted volatility, or both from the statistical models as additional input values into the LSTM model. We perform the out‐of‐sample test of our Hybrid‐LSTM models in volatility forecasting during the coronavirus disease 2019 (COVID‐19) period. Empirical results show that the Hybrid‐LSTM models can still significantly improve the volatility forecasting performance of the LSTM model during the COVID‐19 period. By analysing how the construction methods may influence the forecasting performance of the Hybrid‐LSTM models, we provide some suggestions on their design. Finally, we identify the optimal Hybrid‐LSTM model for each stock index and compare its performance with the LSTM model on each day during our sample period. We find that the Hybrid‐LSTM models' great capability of capturing market dynamics explains their good performance in forecasting. [ABSTRACT FROM AUTHOR]

The relevance of financial volatility forecasting in efficient decision making regarding risk-related assets cannot be subdued. In the financial world, asset price volatility plays a pivotal role in investment decision making and portfolio setups. The prediction of these volatilities usually deal with noisy and non-stationary data bearing heteroscedastic nature. This paper introduces the volatilityforecastingpackage for financial volatility modelling, forecasting and visualization using state-of-the art algorithms. This package allows recourse to algorithms through a user friendly interface supported by the Mathematica framework, that provides easy access to models for high and low frequency data, while accessibly generating forecasts, estimating errors and generating plots. The package also allows analysis of user data and based on the results, a set of models appropriate for the data is suggested for eventual use. [ABSTRACT FROM AUTHOR]

This study develops a novel periodic regime-switching model (the PRS model) to improve the forecasting of stock market volatility by accounting for the information from non-trading and trading periods, including regular trading and after-hour trading. Empirical analysis of the Taiwan Futures Exchange (TAIFEX) demonstrates the significant improvements of the PRS model in both in-sample and out-of-sample periods. Our results also show that the introduction of after-hour trading sessions has provided valuable information for volatility forecasting in subsequent regular trading sessions, emphasizing the importance of considering diverse information flows across different trading and non-trading times. The PRS model effectively captures the dynamics of non-trading and trading sessions and the influence of unusual news arrivals and jumps on market volatility, contributing to investment and risk management strategies. [ABSTRACT FROM AUTHOR]

During the last years, European intraday power markets have gained importance for balancing forecast errors due to the rising volumes of intermittent renewable generation. However, compared to day-ahead markets, the drivers for the intraday price process are still sparsely researched. In this paper, we propose a modelling strategy for the location, shape and scale parameters of the return distribution in intraday markets, based on fundamental variables. We consider wind and solar forecasts and their intraday updates, outages, price information and a novel measure for the shape of the merit-order, derived from spot auction curves as explanatory variables. We validate our modelling by simulating price paths and compare the probabilistic forecasting performance of our model to benchmark models in a forecasting study for the German market. The approach yields significant improvements in the forecasting performance, especially in the tails of the distribution. At the same time, we are able to derive the contribution of the driving variables. We find that, apart from the first lag of the price changes, none of our fundamental variables have explanatory power for the expected value of the intraday returns. This implies weak-form market efficiency as renewable forecast changes and outage information seems to be priced in by the market. We find that the volatility is driven by the merit-order regime, the time to delivery and the closure of cross-border order books. The tail of the distribution is mainly influenced by past price differences and trading activity. Our approach is directly transferable to other continuous intraday markets in Europe. [ABSTRACT FROM AUTHOR]

Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock return volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than the classical GARCH approach, as revealed by the the squared prediction errors. More importantly, the forecast comparison test reveals that the advantage of applying exogenous information related to climate risks in prediction of the South African stock return volatility is significant over a century of monthly data (February 1910–February 2023). Our findings have important implications for academics, investors, and policymakers. [ABSTRACT FROM AUTHOR]

The research study voyage commences with the foundational objective of fitting a suitable Generalized Autoregressive Conditional Heteroscedastic (GARCH) model to assess market volatility, a fundamental pillar of financial analysis. This research embarks on an ambitious quest to predict and understand stock market volatility within the realms of the DJIA and S&P 500 of USA and ATX index of Austria using different sophisticated GARCH models. The dataset used in this study comprises daily stock market data for two key indices: the S&P 500 Index, representing the USA stock market, and the ATX Index, representing the Austria stock market. Additionally, the DJIA Index, another representative of the USA stock market, was included. The dataset consists of 5967 daily observations over the specified time period from January 3, 2000, to September 21, 2023. The observation of results, analysis and discussion depicts that PARCH model shows most promising results and found suitable to model the volatility patterns of the selected indices. The findings and methodologies presented in this paper can be seen as a solid foundation upon which to build future investigations, refining our ability to anticipate market movements and make informed decisions in an uncertain financial landscape. In closing, this research not only contributes to the body of knowledge in financial econometrics but also underscores the importance of modeling long-term stock market behavior with precision and diligence.

AbstractCryptocurrencies have become a popular investment option and the Ethereum has become a mainstream cryptocurrency because of the additional functionality that can be accomplished with the backing of the powerful Ethereum network compared to Bitcoin. The high volatility of Ethereum offers both profits and risks, making it crucial to improve the forecasting ability for its price volatility. The results of this study could be useful for investors and policymakers who are interested in understanding and managing the risks associated with investing in Ethereum. Several studies have explored similar topics using heterogeneous autoregressive (HAR) models for cryptocurrencies, but this paper offers a more expansive approach. This paper employs five-minute high-frequency data to construct 4 HAR models to predict the volatility of Ethereum, taking into account the impact of structural breaks, Bitcoin, SP500 and VIX. The model that considers all factors outperforms other models for out-of-sample predictions for the 1-week forecasting. Due to the nature of the Ethereum price, the HAR-RV model has achieved a perfect fit in 1-day and 1-month forecasting. Therefore, other models have a very small improvement in fitness and prediction accuracy. This paper contributes to the understanding of Ethereum’s volatility and its impact on the cryptocurrency market.

This article attempts to examine the predictability of a significant number of uncertainty indices for the G7 stock market volatility based on a Heterogeneous AutoRegressive Realized Volatility (HARRV) model and a combination forecast framework during the global pandemic COVID-19. We include in our analysis the Infectious Disease Equity Market Volatility (IDEMV), the VIX, the Economic Policy Uncertainty (EPU), the Equity Market Volatility (EMV), the Geopolitical risk (GPR) as well as the crude oil futures' realized volatility. Out-of-sample evidence shows that models incorporating all uncertainty indices improve forecasting performance for most stock markets' volatility during a long out-of-sample period and also during the coronavirus period. The results are robust using an alternative volatility model, an alternative realized measure and a recursive window analysis. The predictability of the uncertainty indices is also evaluated through risk management and portfolio loss functions and results suggest that the LASSO combination and a HARRV model including all indices are the most profitable for all stock markets during the global pandemic. [ABSTRACT FROM AUTHOR]

In this paper, we find new evidence for the carbon futures volatility prediction by using the spillovers of fossil energy futures returns as a powerful predictor. The in‐sample results show that the spillovers have a significantly positive effect on carbon futures volatility. From the out‐of‐sample analysis with various loss functions, we find that fossil energy return spillovers significantly outperform the benchmark and show better forecasting performance than the competing models using dimension reduction, variable selection, and combination approaches. The predictive ability of the spillovers also holds in long‐term forecasting and does not derive from other carbon‐related variables. It can bring substantial economic gains in the portfolio exercise within carbon futures. Finally, we provide economic explanations on the predictive ability of the fossil energy return spillover by the channels of the carbon emission uncertainty and the investor sentiment on the warming climate. [ABSTRACT FROM AUTHOR]

Estimation windows, either rolling or expanding, are used for volatility forecasting. In this study, we propose a new approach relying on both estimation windows. Our method is based on how well these two windows performed in terms of prediction during a recent period of past time. We will continue to use whichever one has performed better in the past. Results show that our strategy significantly outperforms the individual and mean combination models. Whether the window is rolling or expanding, the relatively better performance is persistent. In other words, we document the existence of the momentum of predictability (MoP). A mean–variance investor can achieve the highest utility gains using our strategy for volatility forecasting. Moreover, the results pass a series of robustness tests. [ABSTRACT FROM AUTHOR]

By constructing a novel index, the oil security attention index, this paper uses the heterogeneous autoregressi (HAR)‐type and its extended models to study whether oil security attention can predict oil volatility. Based on the definition of the different dimensions of oil security and three‐pass regression filter (TPRF) dimension reduction technology, combined with Google search volume data of 23 keywords related to oil security, the oil security attention index is constructed. Considering the potential nonlinear relationship between attention and oil volatility, we incorporate asymmetric effects in the new extended HAR‐type models. The research findings show that the oil security attention index we propose can capture the volatility of West Texas Intermediate. The out‐of‐sample results indicate that the extended models have better predictive power, which confirms the asymmetric relationship between oil security attention and oil volatility. In the robustness analysis, we compare TPRF with traditional principal component analysis (PCA) and partial least squares (PLS), and show that the oil security attention index constructed using TPRF has more favourable information than PCA and PLS to capture the oil security attention of the public. [ABSTRACT FROM AUTHOR]

This paper attempts to extend the approach of quantitative investment and provide investors with suggestions about volatility timing. Based on the volatility-managed portfolios strategy, we propose two volatility–tail risk-managed portfolios strategies by combining the volatility and tail risk forecasting methods. We subject seven indices in China's stock market to verify the performance of these two strategies. The empirical results show that volatility–tail risk-managed portfolios yield better performance than buy-and-hold portfolios and volatility-managed portfolios in terms of annualized average returns, Sharpe ratio, maximum drawdown and Calmer ratio. Furthermore, we find that the effectiveness also depends on the volatility clustering of portfolios and the selection of investment periods. [ABSTRACT FROM AUTHOR]

Insurers and pension funds face the challenges of historically low-interest rates and high volatility in equity markets, that have been accentuated due to the COVID-19 pandemic. Recent advances in equity portfolio management with a target volatility have been shown to deliver improved on average risk-adjusted return, after transaction costs. This paper studies these targeted volatility portfolios in applications to equity, balanced, and target-date funds with varying constraints on leverage. Conservative leverage constraints are particularly relevant to pension funds and insurance companies, with more aggressive leverage levels appropriate for alternative investments. We show substantial improvements in fund performance for differing leverage levels, and of most interest to insurers and pension funds, we show that the highest Sharpe ratios and smallest drawdowns are in targeted volatility-balanced portfolios with equity and bond allocations. Furthermore, we demonstrate the outperformance of targeted volatility portfolios during major stock market crashes, including the crash from the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

• War attention is an important driver of stock volatility. • The war attention index uses NLP and dimensionality reduction. • New GARCH-MIDAS models, adding war-attention extreme effects, are used. • War attention index enhances S&P500 volatility predictability. This paper aims to explore the impact of war attention on stock volatility predictability by constructing a new war attention index and employing an extended GARCH-MIDAS-ES model. The war attention index is developed by incorporating the Google search volume data for 56 war-related keywords using natural language processing methods and dimensionality reduction techniques. Since war attention is considered an exogenous shock, we modify the new extended MIDAS model by incorporating the extreme effects caused by war attention into the GARCH-MIDAS-ES framework. Compelling evidence demonstrates that our proposed war attention index is a statistically significant driver of S&P 500 volatility, and our extended model exhibits higher out-of-sample predictive accuracy as it captures both the normal and extreme effects of war attention on stock volatility within the MIDAS framework. By examining how war attention affects stock volatility predictability during the ongoing Russia–Ukraine war, we observe that the extended model's forecasting performance deteriorates as the forecasting horizon increases to a relatively large extent, which is in line with the findings of Andrei and Hasler (2015). [ABSTRACT FROM AUTHOR]

This study investigates the potential effects of environmental factors on fluctuations in agricultural commodity futures markets, by constructing a new category of daily exogenous predictors related to air pollution, weather, climate change, and investor attention. The empirical results from out‐of‐sample analyses suggest that the heterogeneous autoregressive (HAR) model incorporating all these exogenous predictors is more likely to outperform other HAR‐type models. Additionally, economic evaluations demonstrate the superior performance of models incorporating investors' attention to climate change or extreme weather as predictors. While not all exogenous predictors are equally important for volatility forecasts, adopting appropriate variable selection methods to handle different sets of exogenous predictors can lead to better performance than the HAR benchmark. With the inclusion of air pollution or weather factors in the HAR model, a portfolio with an annualized average excess return of 16.2068% or a Sharpe ratio of 10.0431 can be achieved for the wheat futures, respectively. [ABSTRACT FROM AUTHOR]

Motivated by the puzzling null impact of high‐frequency‐based jumps on future volatility, this paper exploits the rich information content in high‐frequency jump intensity with a mark structure under the heterogeneous autoregressive framework. Our proposed model shows that harnessing jump intensity information from the marked Hawkes process leads to significantly superior in‐sample fit and out‐of‐sample forecasting accuracy. In addition to statistical significance evidence, we also illustrate the economic significance in terms of trading efficiency. Our findings hold for a variety of competing models and under different market conditions, underlying the robustness of our results. [ABSTRACT FROM AUTHOR]

We present the Beta-t-QVAR (quasi-vector autoregression) model for the joint modelling of score-driven location plus scale of strictly stationary and ergodic variables. Beta-t-QVAR is an extension of Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) and Beta-t-EGARCH-M (Beta-t-EGARCH-in-mean). We prove the asymptotic properties of the maximum likelihood (ML) estimator for correctly specified Beta-t-QVAR models. We use Dow Jones Industrial Average (DJIA) data for the period of 1985–2020. We find that the volatility forecasting accuracy of Beta-t-QVAR is superior to the volatility forecasting accuracies of Beta-t-EGARCH, Beta-t-EGARCH-M, A-PARCH (asymmetric power ARCH), and GARCH for the period of 2010–2020. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the dynamic volatility behavior of the daily Swedish Real Estate Sector Index and analyze the existence and degree of a long-range dependence or asymmetric news effect since 2003. More specifically, we give extra attention to the 2007–2008 financial crisis, the 2009–2012 European debt crisis, and the first two years of the global COVID-19 pandemic era (2020–2021). We examine changes in volatility during these extreme events. We apply standard GARCH models, asymmetric GARCH models, and long-memory GARCH models with various error distributions to identify the most accurate volatility models of the daily returns of the Swedish Real Estate Sector Index for the full sample period, January 2003 to June 2021. Our results show that the volatility of the Swedish Real Estate Sector Index is time-varying and highly volatile. The impacts of the global financial crisis, European debt crisis, and COVID-19 pandemic are noticeable. Moreover, the volatility pattern during COVID-19 displays significant time-varying long-range dependence and an asymmetrical news impact, which lead to market inefficiency. Finally, the volatility pattern shows a tendency towards increasing leverage effects and less persistent behavior, indicating that the market stakeholders are highly sensitive to negative returns and becoming quicker to respond to market changes. [ABSTRACT FROM AUTHOR]

This paper uses the generalized autoregressive conditional heteroscedasticity mixing data sampling (GARCH‐MIDAS) model to construct three types of extended models. Geopolitical risk uncertainty is included in the study as an introduced variable, and its impact on the Shanghai Stock Exchange (SSE) 50 index volatility is analyzed. The empirical analysis shows that the GARCH‐MIDAS‐RV‐EPU model with China's EPU is the best in predicting the volatility of China's stock market when the information of economic policy uncertainty (EPU) and geopolitical risk uncertainty (GPR) of other countries are included. When the common information model composed of China's economic policy uncertainty index and geopolitical uncertainty index is used to predict the volatility of the SSE, the model's prediction is better. Finally, when the model confidence set (MCS) and the interval length index that changes the forecast outside the sample are used to retest each conclusion, the results are very robust. [ABSTRACT FROM AUTHOR]

This paper discusses whether investor sentiment contains useful information to predict volatility by constructing investor pessimism index based on Baidu, which is the largest search engine in China. We discover that the investor pessimism index is significantly negatively correlated with contemporaneous returns but predicts following days' returns reversals. Furthermore, the heterogeneous autoregressive model of realized volatility (HAR‐RV) with investor pessimism index performs better than other HAR‐RV‐type benchmark models. We perform a series of robustness tests, which include alternative construction of investor pessimism index, crisis subsample, alternative predictive model, alternative measures of volatility, and considering the role of institutional investor. Our results remain robust. Taken together, investor pessimism index constructed in this paper seem to be a suitable proxy to measure investor sentiment in China and can enhance the accuracy of volatility forecasting in the Chinese stock market. [ABSTRACT FROM AUTHOR]

The research study voyage commences with the foundational objective of fitting a suitable Generalized Autoregressive Conditional Heteroscedastic (GARCH) model to assess market volatility, a fundamental pillar of financial analysis. This research embarks on an ambitious quest to predict and understand stock market volatility within the realms of the DJIA and S&P 500 of USA and ATX index of Austria using different sophisticated GARCH models. The dataset used in this study comprises daily stock market data for two key indices: the S&P 500 Index, representing the USA stock market, and the ATX Index, representing the Austria stock market. Additionally, the DJIA Index, another representative of the USA stock market, was included. The dataset consists of 5967 daily observations over the specified time period from January 3, 2000, to September 21, 2023. The observation of results, analysis and discussion depicts that PARCH model shows most promising results and found suitable to model the volatility patterns of the selected indices. The findings and methodologies presented in this paper can be seen as a solid foundation upon which to build future investigations, refining our ability to anticipate market movements and make informed decisions in an uncertain financial landscape. In closing, this research not only contributes to the body of knowledge in financial econometrics but also underscores the importance of modeling long-term stock market behavior with precision and diligence. [ABSTRACT FROM AUTHOR]

Forecasting salmon market volatility is crucial for reducing future uncertainty for market participants. This study explores the efficacy of the Long Short-term Memory (LSTM) network, a deep learning technique, in forecasting multi-step ahead salmon market volatility. The performance of the LSTM is assessed against a constructed volatility proxy and the Autoregressive Moving Average (ARMA) model, a traditional benchmark in time-series analysis. Evaluation is performed across various forecasting horizons using different forecast error measures. Our findings indicate that the ARMA model outperforms the LSTM in predicting salmon market volatility, suggesting that any non-linear patterns in the salmon market volatility might be too insignificant for an LSTM model to exploit effectively. However, we observed a significant discrepancy between the actual volatility values and the forecasts obtained by both models, indicating the complexity of accurately predicting salmon market volatility. [ABSTRACT FROM AUTHOR]

Volatility is usually a proxy indicator for market variation or tendency, containing essential information for investors and policy-makers. This paper proposes a novel hybrid deep neural network model (HDNN) with temporal embedding for volatility forecasting. The main idea of our HDNN is that it encodes one-dimensional time-series data as two-dimensional GAF images, which enables the follow-up convolution neural network (CNN) to learn volatility-related feature mappings automatically. Specifically, HDNN adopts an elegant end-to-end learning paradigm for volatility forecasting, which consists of feature embedding and regression components. The feature embedding component explores the volatility-related temporal information from GAF images via the elaborate CNN in an underlying temporal embedding space. Then, the regression component takes these embedding vectors as input for volatility forecasting tasks. Finally, we examine the feasibility of HDNN on four synthetic GBM datasets and five real-world Stock Index datasets in terms of five regression metrics. The results demonstrate that HDNN has better performance in most cases than the baseline forecasting models of GARCH, EGACH, SVR, and NN. It confirms that the volatility-related temporal features extracted by HDNN indeed improve the forecasting ability. Furthermore, the Friedman test verifies that HDNN is statistically superior to the compared forecasting models. [ABSTRACT FROM AUTHOR]

We introduce a new joint model of expected return and volatility forecasting, namely the two-component Beta-t-QVAR-M-lev (quasi-vector autoregression in-mean with leverage). The maximum likelihood estimator for the two-component Beta-t-QVAR-M-lev is an extension of theoretical results of the one-component Beta-t-QVAR-M. We compare the volatility forecasting performance of the two-component Beta-t-QVAR-M-lev and two-component GARCH-M (generalized autoregressive conditional heteroscedasticity), also considering their one-component frameworks. The results for G20 stock market indices indicate that the forecasting performance of the two-component Beta-t-QVAR-M-lev is superior compared with the two-component GARCH-M and their one-component versions. [ABSTRACT FROM AUTHOR]

This paper explores the forecasting power of some of the most informative indicators of economic uncertainty on carbon emission price volatility. We use one- and two-component GARCH-MIDAS models based on mixed frequency data and Model Confidence Set (MCS) testing with 'rolling scheme' forecast method to examine the forecasting performance of economic uncertainty indicators. We employ an economic policy uncertainty (EPU) indicator with China, US and global economic policy uncertainty constituents together with traditional uncertainty indicators, such as macroeconomic and financial market volatility. Our empirical findings show that generally economic policy uncertainty indicators contain more information of carbon emission price volatility than other indicators. Specifically, one-component GARCH-MIDAS model with the EPU indicator with China constituent and two-component model with EPU indicator have superior performance in forecasting carbon emission price volatility of the Guangdong pilot in China. Our study adds insights into factors that affect carbon emission price movements. [ABSTRACT FROM AUTHOR]

The shocks on certain market spread to other markets due to the financial linkages of global economy, which is known as volatility spillover effect. In this study, we propose a volatility forecasting model for global market indices using the spatial-temporal graph neural network (GNN). The volatility spillover between markets are reflected in the model by estimating the linkage between markets, which is the input of GNN, using the volatility spillover index. An empirical analysis is conducted on eight representative global market indices. From the out-of-sample results, we found the following features. First, the proposed spatial-temporal GNN spillover model outperforms the benchmark models in short- and mid-term forecasting. Second, the forecasting accuracy highly depends on the inclusion of the market index with a high volatility spillover effect. Including S&P500, which contains the highest net spillover index, effectively helps to forecast the volatilities of other markets. Third, the investor can gain economic gain by using predicted volatility from proposed model in the mean-variance framework. [ABSTRACT FROM AUTHOR]

The presence of volatility in residential property market prices helps investors generate substantial profit while also causing fear among investors since high volatility implies a high return with a high risk. In a financial time series, volatility refers to the degree to which the residential property market price increases or decreases during a particular period. The present study aims to forecast the volatility returns of real residential property prices (RRPP) in Malaysia using three different families of generalized autoregressive conditional heteroskedasticity (GARCH) models. The study compared the standard GARCH, EGARCH, and GJR-GARCH models to determine which model offers a better volatility forecasting ability. The results revealed that the GJR-GARCH (1,1) model is the most suitable to forecast the volatility of the Malaysian RRPP index based on the goodness-of-fit metric. Finally, the volatility forecast using the rolling window shows that the volatility of the quarterly index decreased in the third quarter (Q3) of 2021 and stabilized at the beginning of the first quarter (Q1) of 2023. Therefore, the best time to start investing in the purchase of real residential property in Malaysia would be the first quarter of 2023. The findings of this study can help Malaysian policymakers, developers, and investors understand the high and low volatility periods in the prices of residential properties to make better investment decisions.

Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock return volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than the classical GARCH approach, as revealed by the the squared prediction errors. More importantly, the forecast comparison test reveals that the advantage of applying exogenous information related to climate risks in prediction of the South African stock return volatility is significant over a century of monthly data (February 1910–February 2023). Our findings have important implications for academics, investors, and policymakers.

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.

Fluctuations in returns from investment in stocks make these risky. This factor should be kept in mind in stock investment decisions, which determines the relevance of this research. Through the study, the volatility in the stock returns of BRICS nations is analysed for inferring on the riskiness associated with investing in the respective nations, which is the aim of the research. For this study, the daily returns of five indexes representing each of the nation namely Ibovespa (Brazil), Moex (Russia), Nifty 50 (India), Hang Seng Index (HSI, China), and FTSE/JSE All Share Index (JALSH, South Africa) for a period of 14 years are collected and analysed. Both unconditional and conditional volatility in returns is analysed for each of the nations for imparting clearer and more comprehensive picture of the volatility in returns. Such an in-depth and long period analysis of volatility of the returns of the emerging BRICS economies is a novelty of the research that determined that no volatility model can be said as perfect for all economies for all time. The GARCH (1, 1) model was used to study for the returns of all the five indexes. The results of the study point out that the daily returns of all these indexes are heteroscedastic, implying presence of varying variance. Accordingly, the study м that the BRICS nations’ index returns are more volatile and riskier, and authors are recommended to invest in those indexes with lesser conditional volatility.

Recently, deep neural networks have been widely used to solve financial risk modeling and forecasting challenges. Following this hotspot, this paper presents a mixture model for conditional volatility probability forecasting based on the deep autoregressive network and the Gaussian mixture model under the GARCH framework. An efficient algorithm for the model is developed. Both simulation and empirical results show that our model predicts conditional volatilities with smaller errors than the classical GARCH and ANN-GARCH models.

We inspect how accurate machine learning (ML) is at forecasting realized variance of the Dow Jones Industrial Average index constituents. We compare several ML algorithms, including regularization, regression trees, and neural networks, to multiple heterogeneous autoregressive (HAR) models. ML is implemented with minimal hyperparameter tuning. In spite of this, ML is competitive and beats the HAR lineage, even when the only predictors are the daily, weekly, and monthly lags of realized variance. The forecast gains are more pronounced at longer horizons. We attribute this to higher persistence in the ML models, which helps to approximate the long memory of realized variance. ML also excels at locating incremental information about future volatility from additional predictors. Lastly, we propose an ML measure of variable importance based on accumulated local effects. This shows that while there is agreement about the most important predictors, there is disagreement on their ranking, helping to reconcile our results. [ABSTRACT FROM AUTHOR]

Assuming N available calendar days, each with M intraday returns, the realized volatility literature suggests creating N end-of-day estimators by summing the M squared returns from each particular date. Instead of this "Calendar" [realized variance (RV)] approach, we propose a "Rolling" [rolling RV (RRV)] approach that simply sums trailing M returns at each timestamp, regardless if all M returns belong to the same calendar date. When estimating an out-of-sample 1-day realized volatility model, the former results in an ordinary least squares (OLS) regression with N− 1 datapoints while the latter incorporates M (N − 2) + 1 datapoints, effectively lowering the standard errors, and potentially resulting in more accurate forecasts. We compare both models for the S&P 500 and 26 Dow Jones Industrial Average stocks; our results generally suggest that the Rolling approach yields both statistically and economically significant superior out-of-sample performance over the traditional Calendar approach. [ABSTRACT FROM AUTHOR]

In this study, we extend the zero-drift generalized autoregressive conditional heteroskedasticity (GARCH) model to incorporate the well-known asymmetric effects of shocks on financial volatility and propose an asymmetric zero-drift GARCH (AZD-GARCH) model. Relevant asymptotics of the new model, including those for the quasi-maximum-likelihood estimator and the powers of the stability test and the model misspecification test, are comprehensively discussed with simulation evidence. Our empirical studies focus on the daily Brent oil price, the AUD/USD exchange rate, and the S&P 500 returns covering the recent 2019–2020 period. The results demonstrate the usefulness of the AZD-GARCH model in understanding the volatility features of financial assets and the model's superiority to a range of competitors in precisely forecasting volatilities. Robustness checks on data for extended sample periods (2017–2020 and 2009–2020) further provide highly consistent results. Therefore, the proposed AZD-GARCH model can help policymakers and market participants in various applications, such as monitoring asset volatility and hedging relevant risks. [ABSTRACT FROM AUTHOR]

This paper employs a shrinkage method named Adaptive Lasso (ALasso) to predict the realized volatility (RV) of the China's stock market with numerous predictors. We observed from the out‐of‐sample predictions that the ALasso model exhibits better predictive power than its competitors, implying that the ALasso method can select stronger predictors in the forecasting process than competing models. In addition, the predictability of ALasso method is better in low volatility periods than in high volatility periods. Finally, several robustness check methods, including different forecasting windows, different low and high volatility division, and different volatility measures, supported our results. [ABSTRACT FROM AUTHOR]