Extreme Volatility Research Articles

Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks.

Bitcoin is a volatile financial asset that runs on a decentralized peer-to-peer Blockchain network. Investors need accurate price forecasts to minimize losses and maximize profits. Extreme volatility, speculative nature, and dependence on intrinsic and external factors make Bitcoin price forecast challenging. This research proposes a reliable forecasting framework by reducing the inherent noise in Bitcoin time series and by examining the predictive power of three distinct types of predictors, namely fundamental indicators, technical indicators, and univariate lagged prices. We begin with a three-step hybrid feature selection procedure to identify the variables with the highest predictive ability, then use Hampel and Savitzky-Golay filters to impute outliers and remove signal noise from the Bitcoin time series. Next, we use several deep neural networks tuned by Bayesian Optimization to forecast short-term prices for the next day, three days, five days, and seven days ahead intervals. We found that the Deep Artificial Neural Network model created using technical indicators as input data outperformed other benchmark models like Long Short Term Memory, Bi-directional LSTM (BiLSTM), and Convolutional Neural Network (CNN)-BiLSTM. The presented results record a high accuracy and outperform all existing models available in the past literature with an absolute percentage error as low as 0.28% for the next day forecast and 2.25% for the seventh day for the latest out of sample period ranging from Jan 1, 2021, to Nov 1, 2021. With contributions in feature selection, data-preprocessing, and hybridizing deep learning models, this work contributes to researchers and traders in fundamental and technical domains.

Role of oil price volatility, energy efficiency, and financial stability on sustainable energy production

In the COVID-19 crisis, many economies suffered from sustainable energy production. The emergence of the COVID-19 crises, extreme volatility in oil prices, limited energy efficiency in energy systems, and weak form of financial stability were the key reasons for it. However, considering these issues, a recent study aims to analyze them. ASEAN countries’ energy efficiency and crude oil price volatility are examined as a solution to how financial conditions might be utilized to handle energy efficiency issues and crude oil price volatility. Extending it, the study aims to identify the influence of financial stability on crude oil price volatility and energy efficiency issues. To do this, GMM is used. According to the study’s findings, environmental mitigation was determined to be important at 18%, and financial stability and carbon risk significant at 21%. Global warming concerns have been raised due to the ASEAN nations’ 19.5% link between financial stability and emissions drift. A country’s financial stability is necessary for implementing green economic recovery strategies, among the most widely accepted measures to reduce energy efficiency and guarantee long-term financial potential on the national scale. The study on green economic growth also provides the associated stakeholders with sensible policy consequences on this importance.

What's the temperature tomorrow? Increasing trends in extreme volatility of daily maximum temperature in Central and Eastern United States (1950–2019)

Several studies have recently shown that rapid changes in temperature are linked to higher mortality and hospitalization rates, increased risk of respiratory epidemics and lower economic growth. Here, we present the first continental-scale analysis of day-to-day variability, defined as volatility, of daily maximum temperature across the contiguous United States for the period (1950–2019). We first outline spatial patterns of climatology, seasonality and directionality (increase vs. decrease) in volatility using data from 1,484 ground stations. Next, we investigate the existence of trends in the magnitude of extreme volatilities where it is found that most stations in Central US exhibit a statistically-significant increasing trend of approximately 0.2–0.3 °C/decade in magnitude of volatility. Furthermore, we compare these results with those obtained from two reanalysis data sets and one gridded (station-based) data set. Although reanalysis data sets adequately capture spatial variability of volatility, they underestimate the magnitude of volatility and fail to reproduce observed trends which might be attributed to deficiencies in numerical weather prediction models. Overall, these findings pinpoint changes in volatility of daily maximum temperature as a potential manifestation of climate change.

The influence of economic policy uncertainty shocks on art market

ABSTRACT We contribute to the literature by studying the impact of economic policy uncertainty shocks on returns in the global art market, the global paintings market and the U.S.A art market from 1998:Q1 to 2018:Q3. Based on the frequency domain Granger causality test and continuous wavelets analysis, the results show that an increase in policy uncertainty shocks significantly reduces returns on art and paintings and that the effect is stronger during extreme volatility periods. Policy implications are derived at the end of the article.

In search of time-varying jumps during the turmoil periods: Evidence from crude oil futures markets

Prior literature demonstrates that energy prices are characterized by time-varying jumps. However, earlier studies do not investigate if the intensity of such jumps appears to be higher amid periods of extreme volatility in comparison to normal periods. Employing the GARCH-jump model, this study examines whether jumps occurring in energy prices are an indicator of market crashes. To serve this purpose, we consider several downturns in oil markets spanning over the last few years. Our empirical analyses reveal that the conditional expected number of jumps in WTI and Brent oil futures prices increases significantly amid the depression periods, which is, however, not the case when the market functions normally. We, therefore, conclude that such clusters of jumps may contain predictive information for oil market crashes and thus provide early signals of future downturns. The findings further show that crude oil volatility, the US equity VIX, and economic policy uncertainty play a significant role in explaining the time-dependent jumps during the turmoil periods. The findings of our research could be useful for investors participating in global crude oil markets and for policymakers watching out for the impact of energy prices on the economy.

Measurements of Particulate Matter from Electronic and Conventional Cigarettes: A Comparative Analysis of Methods

Due to the growing popularity of electronic cigarettes (ECs) and heated tobacco products (HTPs) as alternatives to conventional cigarettes (CCs), there is an increasing need to monitor the emissions of these new devices. ECs generate significant concentrations of second-hand aerosol (ECSHA), which is visible in dense clouds and can be smelled. Particulate matter (PM) is an important component of CC, HTP and EC aerosols, and Optical Particle Counters (OPCs) enable its real-time measurement, which is expressed either as the number of particles or as mass. This study specifically addresses the limitations associated with EC mass measurement using OPC technology and identifies the strict necessity of the measurement of a corresponding density (k factor) not only for each specific PM source but also for the desired PM size. Therefore, a standard measurement requires the simultaneous operation of the OPC equipment and a certified reference instrument. Four different OPCs were used. Crucially, this study also proves that this setup may be inapplicable because the extreme volatility of EC-generated aerosols makes it impractical to gauge the correct EC k factor.

Behavioral finance and COVID-19

According to Ram, characterized by a market crash and excessive stock price volatility, the COVID-19 pandemic had striking financial impacts. Some of the phenomena that occurred through the catastrophe, like the extreme volatility and unshakable confidence of fiscal associations, are not sufficiently explicated by the conventional financial paradigm. In addition to the effect of COVID-19 on financial markets of the globe, this paper explores the decisions and actions of investors and financial institutions from a behavioral finance perspective. Relevant during and after the crisis, various cognitive biases and errors are also discussed. The current paper is a literature review of the existing research on behavioral finance’s effect on the COVID-19 epidemic. A cautious evaluation of these 13 articles divulged no material pertinent to behavioral finance. The remaining 27 articles were incorporated in this analysis. We explore these phenomena and evaluate their significance to financial markets and institutions and the global crisis stimulated by COVID-19.

Circuit Breaker Effects on Market Players Psychology and Behavior

Since the Black Monday of 1987, Stock Markets worldwide have adopted several financial regulatory measures to cease the free flow of market crashes during times of extreme volatility. Circuit Breakers, a.k.a trading curb is a regulatory instrument that halts trading and stabilizes the extreme volatility. This research paper analyses Circuit Breakers in the Stock market, their impacts, and their effects on market behavior. The study reviews the history of the circuit breaker implementation and the impact it causes on market behaviors such as volatility, the difference in return pre and post-circuit breaker events. This study considers Nifty 50 index data for a total period of 60 days, 30 days prior, and 30 days post-implementation of circuit breakers. The thesis explores the importance of the market-wide circuit breaker in India and the history of the circuit breaker. This research uses Bloomberg data on the closing price and volume of the Nifty 50 index on 12 separate windows, such as pre-and post-circuit breakers of respective six times as an industry-wide circuit breaker the Indian stock market. The sample determines the disparity in return and volatility between the pre and post-circuit breaker periods based on the mean and standard deviation. The impact of a circuit breaker lasts for up to 10 days in most cases and up to 20 days in some cases; however, the impact of a circuit breaker lasts for up to 30 days in some cases.

The euro area’s pandemic recession: A DSGE-based interpretation

This paper augments the European Commission’s open-economy DSGE model (GM) with COVID-specific shocks (‘forced savings’, labour hoarding) and financially-constrained investors to account for the extreme volatility of private domestic demand and hours worked during COVID-19, and it estimates the model on euro area data for the period 1998q4–2021q4. It takes a pragmatic approach of adapting the workhorse model of a policy institution to COVID-19 data. ‘Forced savings’ are central to explain quarterly real GDP growth during the pandemic, complemented by contributions from foreign demand and trade, and the negative impact of persistently higher savings after the first wave. We provide extensive model validation, including a comparison to off-model evidence for COVID-related restrictions, and a comparison of different model specifications.

Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach

This study proposes a new hybrid model that combines LSTM and BiLSTM neural networks with GARCH type model forecasts using an ensemble approach to forecast volatility for one-day ahead 95% and 99% Value-at-Risk (VaR) estimates using the Parametric (PAR) and Filtered Historical Simulation (FHS) method. The forecasting abilities of the standard GARCH (GARCH), exponential GARCH (eGARCH), and threshold GARCH (tGARCH) models are combined with the LSTM networks to capture different characteristics of the underlying volatility. We evaluate the model using log returns on Crude Oil during two periods of extreme volatility: the 2007-09 Financial Crisis and the Covid Recession of 2020–21. The performance of hybrid models is compared against several traditional VaR methods like the Historical Simulation, Bootstrap, Age weighted method, and the volatility-based VaR models using the GARCH, LSTM, and BiLSTM model forecasts. The unconditional and conditional coverage tests and a combination of regulator and firm loss functions are used to evaluate the quality of VaR forecasts. We find a significant improvement in the quality and accuracy of the VaR forecasts of the hybrid models over all the other models across all loss functions and coverage tests. The FHS-BiLSTM-HYBRID, a proposed FHS-based hybrid model, combining the BiLSTM model with three GARCH-type models, is the best performing, with the lowest values for both loss functions. The traditional and GARCH-type models do not efficiently model volatility during the crisis periods resulting in poor VaR forecasts. The FHS consistently performs as the best method for generating VaR compared to all other approaches.

Overreaction and Diagnostic Expectations in Macroeconomics

We present the case for the centrality of overreaction in expectations for addressing important challenges in finance and macroeconomics. First, non-rational expectations by market participants can be measured and modeled in ways that address some of the key challenges posed by the rational expectations revolution, most importantly the idea that economic agents are forward-looking. Second, belief overreaction can account for many long-standing empirical puzzles in macro and finance, which emphasize the extreme volatility and boom-bust dynamics of key time series, such as stock prices, credit, and investment. Third, overreaction relies on psychology and is disciplined by survey data on expectations. This suggests that relaxing the assumption of rational expectations is a promising strategy, helps theory and evidence go together, and promises a unified view of a great deal of data.

Portfolio diversification of MENA markets with cryptocurrencies: Mean-variance vs higher-order moments approach

The emergence of disruptive cryptocurrency platforms and decentralized finance (DEFI) has revolutionized the financial landscape over the last couple of years. Against this backdrop, and in view of the limited opportunities for diversification in the conventional assets, it becomes evident for the investors to look for better opportunities by searching for rather non-conventional assets like cryptocurrencies as a means of portfolio diversification. This study analyses the major cryptocurrencies based on their market capitalization and the major markets from MENA regions to evaluate the potential of cryptocurrencies in portfolio diversification. The study employs the mean-variance approach and then compares the results with the higher-order moments. We found that cryptocurrencies offer considerable potential for diversification for the MENA markets, but these exposures must be conservatively given the explosive price evolution and extreme volatility of the cryptocurrencies. The results also suggest that cryptocurrencies do not considerably contribute to the portfolio diversification in the uncertain market movements and crises, such as that witnessed during Fed's regime shift, accentuated by the Ukraine crisis.

Do financial volatilities mitigate the risk of cryptocurrency indexes?

Cryptocurrencies are renowned for their extreme volatilities, while financial volatilities exhibit inherent uncertainty in stock, oil, gold, and currency markets. Given these concerns, the current study endeavors to answer the question of whether financial volatilities can mitigate the risk of cryptocurrency indexes. We employ the time-varying parameters vector autoregression approach and find distinct spillover patterns between financial volatilities and cryptocurrency indexes. In this way, we stress the diversification benefits of financial volatilities to mitigate the risk of cryptocurrency indexes. Time-varying trends demonstrated extreme risk spillovers during turmoil periods. We outlined useful implications for policymakers, financial market participants, investors, and portfolio managers.

Modelling Financial Markets during Times of Extreme Volatility: Evidence from the GameStop Short Squeeze

Ever since the start of the coronavirus pandemic, lockdowns to curb the spread of the virus have resulted in an increased interest of retail investors in the stock market, due to more free time, capital, and commission-free trading brokerages. This interest culminated in the January 2021 short squeeze wave, caused in no small part due to the coordinated trading moves of the r/WallStreetBets subreddit, which has rapidly grown in user base since the event. In this paper, we attempt to discover if coordinated trading by retail investors can make them a market moving force and attempt to identify proactive signals of such movements in the post activity of the forum, to be used as a part of a trading strategy. Data about the most mentioned stocks is collected, aggregated, combined with price data for the respective stock and analysed. Additionally, we utilise predictive modelling to be able to better classify trading signals. It is discovered that despite the considerable capital that retail investors can direct by coordinating their trading moves, additional factors, such as very high short interest, need to be present to achieve the volatility seen in the short squeeze wave. Furthermore, we find that autoregressive models are better suited to identifying signals correctly, with best results achieved by a Random Forest classifier. However, it became apparent that even the best performing model in our experimentation cannot make accurate predictions in extreme volatility, evidenced by the negative returns shown by conducted back-tests.

A two level ensemble classification approach to forecast bitcoin prices

PurposeBitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model.Design/methodology/approachIn this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm.FindingsThe proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively.Originality/valueIn this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.

Risk spread in multiple energy markets: Extreme volatility spillover network analysis before and during the COVID-19 pandemic

Financial events in global energy markets could trigger extreme volatility spillovers and even become financial crises without effective risk management. To analyze extreme volatility spillovers in energy markets and demonstrate how risks are spread under extreme market conditions, this paper first measures the extreme volatility spillovers in the main energy markets from 2011 to 2019 using the vine Copula model then constructs the extreme volatility spillover networks based on their tail dependence correlations. Furthermore, the extreme risk spread is investigated by applying clique analysis. In consideration of the recent shock namely the COVID-19 pandemic, we further analyze the energy markets' extreme volatility spillovers in 2020. We find that extreme volatility spillover effects are stronger in renewable energy markets, especially during market booms and it also reflects an asymmetry feature of the energy markets’ extreme volatility spillovers. The water energy market is influential in the extreme volatility spillover networks. More significantly, there are strong volatility spillover effects among renewable energy markets, while risks are less likely to spread among non-renewable energy markets under extreme circumstances. We make contributions to reveal the volatility spillover under extreme conditions, then comprehensively analyze the risk spread in the extreme volatility spillover networks. By comparing how risks spread before and during the COVID-19, and between renewable and non-renewable energy markets, our findings could be of great help for policymakers and other stakeholders to cope with the uncertainties, especially in the extreme environment.

Influence of risk propensity, behavioural biases and demographic factors on equity investors' risk perception

PurposeInvestor risk perception is a personalized judgement on the uncertainty of returns pertaining to a financial instrument. This study identifies key psychological and demographic factors that influence risk perception. It also unravels the complex relationship between demographic attributes and investor's risk attitude towards equity investment.Design/methodology/approachExploratory factor analysis is used to identify factors that define investor risk perception. Multiple regression is used to assess the relationship between demographic traits and factor groups. Kruskal–Wallis test is used to ascertain whether the factors extracted differ across demographic categories. A risk perception framework based on these findings is developed to provide deeper insight.FindingsThere is evidence of the relationship and influence of demographic factors on risk propensity and behavioural bias. From this study, it is apparent that return expectation, time horizon and loss aversion, which define the risk propensity construct, vary significantly based on demographic traits. Familiarity, overconfidence, anchoring and experiential biases which define the behavioural bias construct differ across demographic categories. These factors influence the risk perception of an individual with respect to equity investments.Research limitations/implicationsThe reference for the framework of this study is limited as there has been no precedence of similar work in academia.Practical implicationsThis paper establishes that information seekers make rational decisions. The paper iterates the need for portfolio managers to develop and align investment strategies after evaluation of investors' risk by including these behavioural factors, this can particularly be advantageous during extreme volatility in markets that concedes the possibility of irrational decision making.Social implicationsThis study highlights that regulators need to acknowledge the investor's affective, cognitive and demographic impact on equity markets and align risk control measures that are conducive to market evolution. It also creates awareness among market participants that psychological factors and behavioural biases can have an impact on investment decisions.Originality/valueThis is the only study that looks at a three-dimensional perspective of the investor risk perception framework. The study presents the relationship between risk propensity, behavioural bias and demographic factors in the backdrop of “information” being the mediating variable. This paper covers five characteristics of risk propensity and eight behavioural biases, such a vast coverage has not been attempted within the academic realm earlier with the aforesaid perspective.

Quantile Spectral Beta: A Tale of Tail Risks, Investment Horizons, and Asset Prices

Abstract This article investigates how two important sources of risk—market tail risk (TR) and extreme market volatility risk—are priced into the cross-section of asset returns across various investment horizons. To identify such risks, we propose a quantile spectral (QS) beta representation of risk based on the decomposition of covariance between indicator functions that capture fluctuations over various frequencies. We study the asymptotic behavior of the proposed estimators of such risk. Empirically, we find that TR is a short-term phenomenon, whereas extreme volatility risk is priced by investors in the long term when pricing a cross-section of individual stocks. In addition, we study popular industry, size and value, profit, investment, or book-to-market portfolios, as well as portfolios constructed from various asset classes, portfolios sorted on cash flow duration, and other strategies. These results reveal that tail-dependent and horizon-specific risks are priced heterogeneously across datasets and are important sources of risk for investors.

Estimating tail-risk using semiparametric conditional variance with an application to meme stocks

In this paper, we propose using Mishra, Su, Ullah’s (2010) semiparametric variance to estimate Value at Risk (VaR) and Expected Shortfall (ES). Although the variance estimate is established in the literature, it has not been applied to VaR and ES estimation. Here the returns’ variance is estimated using Mishra, Su, Ullah’s (2010) conditional semiparametric estimator, and the standardized residuals’ distribution is fitted nonparametrically. In order to reduce possible misspecification bias, the proposed estimators decouple the assumption of the returns’ distribution and the variance estimation. Empirical and simulated data are used to compare the performance of the new estimators against existing parametric and nonparametric VaR and ES models, both conditional and unconditional. The estimators are further applied to daily returns of certain meme stocks to estimate tail risk and test the performance of the estimators during periods of extreme volatility. Compared to the models studied, the proposed conditional, semiparametric VaR model produces fewer violations, and violations without a recognizable pattern - upholding the regulatory requirements. The expected shortfall estimated by the conditional, semiparametric model is also closest to the observed mean of the violations.

Cryptocurrency: Not far from equilibrium

The rapid growth and expanding applications of cryptocurrencies are the main factors that have led them to be considered potential assets to investment portfolios. However, the fierce ups-and-downs that Bitcoins have experienced recently, coupled with the extreme volatility in the Bitcoin prices, have cast doubts on the eligibility of cryptocurrencies to be classified as assets. To investigate the characteristics of cryptocurrencies, this study compares Bitcoin, one of the most popular cryptocurrencies, with other major investment assets. Our analysis focuses on the efficient market hypothesis measured by the Hurst exponent, together with long-term market equilibrium measured by Shannon entropy. The findings suggest that although the Bitcoin market is less efficient than other markets, it is not significantly different from the other markets in terms of its long-term market equilibrium. To elucidate these properties, we probe the Fokker–Planck and Schrödinger equations and derive a probability density function, considering the speed of mean reversion and dispersion.