An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.

Do U.S.economic conditions at the state level predict the realized volatility of oil‑price returns?A quantile machine‑learning approach

Because the U.S.is a major player in the international oil market,it is interesting to study whether aggregate and state-level economic conditions can predict the subse-quent realized volatility of oil price returns.To address this research question,we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility(HAR-RV)model.To estimate the models,we use quantile-regression and quantile machine learning(Lasso)estimators.Our estimation results highlights the dif-ferential effects of economic conditions on the quantiles of the conditional distribution of realized volatility.Using weekly data for the period April 1987 to December 2021,we document evidence of predictability at a biweekly and monthly horizon.

The predictive power of Bitcoin prices for the realized volatility of US stock sector returns

This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns,particularly at the sectoral level of data.We specifically assess Bitcoin prices’ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons,based on daily data from November 22,2017,to December,30,2021.The findings show that Bitcoin prices have significant predictive power for US stock volatility,with an inverse relationship between Bitcoin prices and stock sector volatility.Regardless of the stock sectors or number of forecast horizons,the model that includes Bitcoin prices consistently outperforms the benchmark historical average model.These findings are independent of the volatility measure used.Using Bitcoin prices as a predictor yields higher economic gains.These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors,which is important for practitioners and policymakers.

Can the Baidu Index predict realized volatility in the Chinese stock market?

This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index.Furthermore,the predictability of the Baidu Index is found to rise as the forecasting horizon increases.We also find that continuous components enhance predictive power across all horizons,but that increases are only sustained in the short and medium terms,as the long-term impact on volatility is less persistent.Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.

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.

Empirical Evidence of the Leverage Effect in a Stochastic Volatility Model： A Realized Volatility Approach

Increasing attention has been focused on the analysis of the realized volatil- ity, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged stochastic volatility model using the realized volatility computed from five popular methods across six sampling-frequency transaction data （from 1-min to 60- min） based on the trust region method. Availability of the realized volatility allows us to estimate the model parameters via the MLE and thus avoids computational challenge in the high dimensional integration. Six stock indices are considered in the empirical investigation. We discover some consistent findings and interesting patterns from the empirical results. In general, the significant leverage effect is consistently detected at each sampling frequency and the volatility persistence becomes weaker at the lower sampling frequency.

Effects of investor sentiment on stock volatility:new evidences from multi-source data in China’s green stock markets

The effect of investor sentiment on stock volatility is a highly attractive research question in both the academic field and the real financial industry.With the proposal of China’s"dual carbon"target,green stocks have gradually become an essential branch of Chinese stock markets.Focusing on 106 stocks from the new energy,environmental protection,and carbon–neutral sectors,we construct two investor sentiment proxies using Internet text and stock trading data,respectively.The Internet sentiment is based on posts from Eastmoney Guba,and the trading sentiment comes from a variety of trading indicators.In addition,we divide the realized volatility into continuous and jump parts,and then investigate the effects of investor sentiment on different types of volatilities.Our empirical findings show that both sentiment indices impose significant positive impacts on realized,continuous,and jump volatilities,where trading sentiment is the main factor.We further explore the mediating effect of information asymmetry,measured by the volume-synchronized probability of informed trading(VPIN),on the path of investor sentiment affecting stock volatility.It is evidenced that investor sentiments are positively correlated with the VPIN,and they can affect volatilities through the VPIN.We then divide the total sample around the coronavirus disease 2019(COVID-19)pandemic.The empirical results reveal that the market volatility after the COVID-19 pandemic is more susceptible to investor sentiments,especially to Internet sentiment.Our study is of great significance for maintaining the stability of green stock markets and reducing market volatility.

The Quadratic-Form Representation of the Pre-Averaging Estimator

Volatility forecasts are central to many financial issues, including empirical asset pricing finance and risk management. In this paper, I derive a new quadratic-form representation of the pre-averaging volatility estimator of Jacod et al. （2009）, which allows for the theoretical analysis of its forecasting performance.

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.

Covariance Estimation Using High-Frequency Data： An Analysis of Nord Pool Electricity Forward Data

The modeling of volatility and correlation is important in order to calculate hedge ratios, value at risk estimates, CAPM （Capital Asset Pricing Model betas）, derivate pricing and risk management in general. Recent access to intra-daily high-frequency data for two of the most liquid contracts at the Nord Pool exchange has made it possible to apply new and promising methods for analyzing volatility and correlation. The concepts of realized volatility and realized correlation are applied, and this study statistically describes the distribution （both distributional properties and temporal dependencies） of electricity forward data from 2005 to 2009. The main findings show that the logarithmic realized volatility is approximately normally distributed, while realized correlation seems not to be. Further, realized volatility and realized correlation have a long-memory feature. There also seems to be a high correlation between realized correlation and volatilities and positive relations between trading volume and realized volatility and between trading volume and realized correlation. These results are to a large extent consistent with earlier studies of stylized facts of other financial and commodity markets.

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.