Using the high-frequency data of Bitcoin, this study aims to model the time-varying volatility identified in the residuals of the heterogeneous autoregressive (HAR) model of realized volatility using the symmetric, asymmetric and long-memory generalized autoregressive conditional heteroscedastic models (GARCH) models. We further extended these models by incorporating jumps and continuous components in the realized volatility estimators and investigating the impact of the inverse leverage effect. The Diebold Mariano and model confidence set test confirm that the forecasting performance of HAR-type models can be effectively improved by these innovations. The long memory HAR-GARCH model with jumps and continuous components provided better forecasting accuracy for Bitcoin volatility as compared to other realized volatility models. The findings of this study may benefit individual investors and risk managers who wish to minimize risks and diversify their portfolios to maximize profits in Bitcoin’s investment.
The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility.
Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies.
Nutrition (Nutritional) education is a kind of education that is important to improve the health status of people. School children are undergoing rapid mental and physical development. Therefore, an appropriate diet is critical throughout this stage of life to ensure normal and healthy growth. As a result, nutrition education should begin at a young age for children Objective: To evaluate the impact of nutrition education of mothers on the dietary habits of school-going children aged 3-6 years Methods: A Quasi-Experimental study was executed on 77 children of class playgroup to class 1 studying in the Government girl’s school of Garhi Shahu, Lahore. Firstly, anthropometric data were collected through the children and secondly socio-demographic and dietary knowledge of mothers of selected children were noted through pre-designed questionnaires and interview method. The mothers were then given 45 minutes of nutrition education and a dietary change course, and some informative dietary guidelines leaflets and a weekly healthy kid’s school lunch planner were given to the mothers which they were requested to implement in the daily routine of children. After three months again the anthropometrics and questionnaires were assessed and compared with the initial findings Results: After nutrition education, the percentage of children who followed the servings of healthy food groups and avoided consumption of harmful meals (fast, fried, processed foods), unhealthy beverages (carbonated drinks), and intake of fruits and vegetables increased dramatically. After the intervention, the percentage of children who followed recommended nutritional, lifestyle, and physical activity guidelines, as well as healthy school lunch practices, improved statistically significantly (P<0.005). The percentage of children who skipped meals on daily basis was 20% decreased to 8%. Before the intervention, 63% of mothers say that their children consumed breakfast regularly and after the intervention, it increases to 75%. The children's anthropometric status improved significantly, with a P<0.005 significance level Conclusions: In this study, nutrition education had a significant impact on the school-going children in their anthropometry measurements, healthy school lunch boxes, and awareness of their mothers about healthy eating practices. Seminars and camps should be arranged in schools to educate the mothers and the students at a young age regarding their health and healthy eating to reduce the nutritional deficiencies and diseases
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