Predicting Volatility Index According to Technical Index and Economic Indicators on the Basis of Deep Learning Algorithm

The Volatility Index (VIX) is a real-time index that has been used as the first measure to quantify market expectations for volatility, which affects the financial market as a main actor of the overall economy that is sensitive to the environmental and social aspects of investors and companies. The VIX is calculated using option prices for the S&P 500 Index (SPX) and is expressed as a percentage. Taking into account that VIX only shows the implicit volatility of the S&P 500 for the next 30 days, the authors develop a model for a near-optimal state trying to avoid uncertainty and insufficient accuracy. The researchers are trying to make a contribution to the theory of socially responsible portfolio management. The developed approach allows potential investments to make decisions regarding such important topics as ethical investing, performance analysis, as well as sustainable investment strategies. The approach of this research allows to use deep probabilistic convolutional neural networks based on conditional variance as a linear function of errors with the aim of estimating and predicting the VIX. For this purpose, the use of technical indicators and economic indexes such as Chicago Board Options Exchange (CBOE) VIX and S&P 500 is considered. The results of estimating and predicting the VIX with the proposed method indicate high precision and create a certainty in modeling to achieve the goals.

The epidemic volatility index, a novel early warning tool for identifying new waves in an epidemic

Early warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Data on the daily confirmed cases of COVID-19 are used to demonstrate the use of EVI. Results from the COVID-19 epidemic in Italy and New York State are presented here, based on the number of confirmed cases of COVID-19, from January 22, 2020, until April 13, 2021. Live daily updated predictions for all world countries and each of the United States of America are publicly available online. For Italy, the overall sensitivity for EVI was 0.82 (95% Confidence Intervals: 0.75; 0.89) and the specificity was 0.91 (0.88; 0.94). For New York, the corresponding values were 0.55 (0.47; 0.64) and 0.88 (0.84; 0.91). Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting new waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act swiftly and thereby enhance containment of outbreaks.

The Relationship Between Share Prices and DUPONT Model Components: Evidence from Mexican Stock Market

This article presents the possible association between the three components (profit generation, asset efficiency and financial leverage) of the DUPONT ratio and share prices. The generalized method of moments (GMM) estimation was used with a sample of 23 companies traded on the Mexican stock exchange between 2008 and 2016, considering a period of three days before and three days after the presentation of the quarterly results. It is noted that the generation of profit and efficiency are the components of the DUPONT model that are strongly associated with stock prices, while the leverage effect is the component with the least impact. This empirical work is intended to help understand the relationship between accounting information and stock prices. The study identifies variables that influence decision-making and does not seek to be a predictive model of the value of actions in the future. This research differs from previous studies because it considers the volatility index (VIMEX) as a control variable.

Financial Fear Index in the Digital Financial Assets Market

The relevance of the research topic is due to the increasing role of non-traditional financial instruments that contribute to financial instability. Therefore, various indicators are required to reflect the situation in the digital financial assets market, the volatility quotes, and the level of investor confidence. The aim of the study is to develop and test on empirical data a generalized indicator of financial instability (financial fear index) in the digital financial assets market. The novelty of the research lies in the adaptation of the classic model of building the volatility index to the cryptocurrency market.The authors use statistical methods for collecting and processing data, analyzing time series, weighing, designing economic indicators. The paper summarizes the results of modern research on the correlation between digitalization and financial instability. The authors conclude that at certain short periods of 2020 the ruble-dollar volatility was comparable or even higher than the ruble-bitcoin one. In addition, there is much less fear and uncertainty in the cryptocurrency market today than there was at the end of 2018. The main result of the study is the financial fear index model based on the method of calculating the weighted average option price of the underlying asset and hedging of price risks. The model has been tested using data on the bid and ask prices of cryptocurrencies at a specific point in time. Estimates have been obtained indicating the growing instability in the digital financial asset market. The authors offer recommendations regarding the index threshold values, which indicate the level of investors’ fear.

Cryptocurrency volatility markets

By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market’s expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.