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.

Extreme learning with chemical reaction optimization for stock volatility prediction

Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.

Prediction of Henry Constants and Adsorption Mechanism of Volatile Organic Compounds on Multi-Walled Carbon Nanotubes by Using Support Vector Regression

Support vector regression （SVR） combined with particle swarm optimization for its parameter optimization is employed to establish a model for predicting the Henry constants of multi-walled carbon nanotubes （MWNTs） for adsorption of volatile organic compounds （VOCs）. The prediction performance of SVR is compared with those of the model of theoretical linear salvation energy relationship （TLSER）. By using leave-one-out cross validation of SVR test Henry constants for adsorption of 35 VOCs on MWNTs, the root mean square error is 0.080, the mean absolute percentage error is only 1.19~, and the correlation coefficient （R2） is as high as 0.997. Compared with the results of the TLSER model, it is shown that the estimated errors by SVR are ali smaller than those achieved by TLSER. It reveals that the generalization ability of SVR is superior to that of the TLSER model Meanwhile, multifactor analysis is adopted for investigation of the influences of each molecular structure descriptor on the Henry constants. According to the TLSER model, the adsorption mechanism of adsorption of carbon nanotubes of VOCs is mainly a result of van der Waals and interactions of hydrogen bonds. These can provide the theoretical support for the application of carbon nanotube adsorption of VOCs and can make up for the lack of experimental data.

Research on Prediction and Early Warning of A-Share Market Volatility Based on HAR-Type Models

Based on the different premium volatility characteristics of various systematic factors in the A-share market, this paper constructs six representative high-frequency volatility prediction models that consider multiple complex risk structures. On this basis, a detailed comparative analysis of the differences in volatility characteristics among various factors is conducted, and the optimal prediction and early warning framework for the A-share market is proposed. Research shows that: 1) The volatility research results only for individual market indexes are not universally representative. 2) The fluctuation characteristics among different systematic factors and their respective optimal prediction model frameworks generally have significant differences, that is, there is no single fixed combination of model parameters. 3) Complex risk characteristics such as long memory, measurement errors, and high-frequency jump fluctuations obviously exist in the A-share market. The optimal forecast and early warning framework for the A-share market can be constructed by a combination of models that consider one or more of the above risk characteristics. The above conclusions have important practical reference value for the risk warning and prevention of the A-share market and the formulation of related policies.

Timing Prediction Error Volatility and Dynamic Asset Allocation

We solve a portfolio selection,problem in which,return predictability,risk predictability and transaction cost are incorporated.In the problem,both expected return,prediction error volatility,and transaction cost are time-varying.Our optimal strategy suggests trading partially toward a dynamic aim portfolio,which is a weighted average of expected future tangency portfolio and is highly influenced by the common fluctuation of prediction error volatility(CPE).When CPE is high,the investor would invest less and trade less frequently to avoid risk and transaction cost.Moreover,the investor trades more closely to the aim portfolio with a more persistent CPE signal.We also conduct an empirical analysis based on the commodities futures in Chinese market.The results reveal that by timing prediction error volatility,our strategy outperforms alternative strategies.