Extreme Volatility Research Articles

The Implications of Contemporary Research on COVID-19 for Volatility and Portfolio Management

This article synthesizes recent and ongoing finance and economics research on pandemic and disaster risk related to COVID-19. Characterized by pronounced market movements and extreme volatility, the unprecedented disruption to the economy in early 2020 has inspired a rich, burgeoning literature on the financial and economic ramifications of pandemic risk. Financial economists have cultivated fresh perspectives regarding the transmission of pandemic-induced uncertainty to financial markets via channels related to the beliefs and behaviors of investors as well as corporate strategies and outcomes. These findings also highlight the imperative role of government policy responses in regulating the market volatility triggered by large-scale disasters such as the pandemic. In this article, the authors take stock of this emerging literature, focusing on the implications for volatility and risk management. In doing so, they discuss the unique nature of the uncertainty induced by COVID-19 relative to that of past crises. They also review cutting-edge studies that use innovative analytical approaches and novel sources of data, offering fruitful avenues for future research. <b>TOPICS:</b>Tail risks, financial crises and financial market history, big data/machine learning <b>Key Findings</b> ▪ We synthesize recent and ongoing research in the finance and economics literature on pandemic and disaster risk related to COVID-19 and discuss the implications for asset pricing, volatility, and risk management. ▪ Pandemic-induced uncertainty led to extreme market movements in early 2020. We explore possible channels, such as investor beliefs and behaviors and corporate strategies, through which this risk was transmitted to the financial markets. ▪ Lessons learned from the recent pandemic will continue to have implications for volatility and risk management, even after the pandemic ends.

Exploring technological instantiation of regulatory practices in entangled financial markets

The literature on the sociology of financial markets and institutional theory promotes concepts of field, networks, performativity, agencement and financial entropy. This study builds a conceptual model of technological instantiation of regulatory practices in financial markets. We observe how asset management firms instantiate technology as a material and social artefact to regulate the actions and behaviour of human agents. The structural-agency divide reveals the coercive role of regulators who impose stringent compliance practices on financial organizations by embedding formal rules and regulations in the software. Socio-technical conditions show how human agents interpret and apply these rules to circumvent formal regulatory policies and practices. Context-specific analysis shows technological performativity and agencement co-exist in financial fields that are becoming more entangled and fragmented. Regulators respond with more complex mandates to reduce entropy in financial markets characterized by extreme volatility and instability.

Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN

One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility are crucial for bitcoin investors’ decision-making analysis and risk management. However, most previous studies of bitcoin volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is still sparse. In this study, both conventional econometric models and a machine learning model are used to forecast the bitcoin’s return volatility and Value at Risk. The objective of this study is to compare their out-of-sample performance in forecasting accuracy and risk management efficiency. The results demonstrate that the RNN outperforms GARCH and EWMA in average forecasting performance. However, it is less efficient in capturing the bitcoin market’s extreme events. Moreover, the RNN shows poor performance in Value at Risk forecasting, indicating that it could not work well as the econometric models in explaining extreme volatility. This study proposes an alternative method of bitcoin volatility analysis and provides more motivation for economic researchers to apply machine learning methods to the less volatile financial market conditions. Meanwhile, it also shows that the machine learning approaches are not always more advanced than econometric models, contrary to common belief.

The Racialization of Drug Fakery and Pharmaceutical Markets

ABSTRACT In this article, I show that fake drugs circulate widely in markets that chronically experience non-equilibrium. Moreover, those who manufacture, buy, and sell low priced drugs for African markets are negotiating chronic market volatility and downward pricing pressures that are transnational in scope. I argue that global policy experts; state, regional, and global regulation agencies and organizations; and international NGOs that have stakes in eradicating fake drugs falsely assume that pharmaceutical traders work within market equilibrium conditions. These assumptions are racialized because they hold actors and markets accountable to a market equilibrium and rational actor standard that are characterized by a European imagination that came into being at the height of trans-Atlantic slavery and capitalist formation. Yorùbá and Ìgbò non-equilibrium theories of the market, in contrast, have characterized markets as volatile and precarious since the 15th century arrival of the Portuguese to West Africa. The implications are that preconceived ideas of equilibrium de facto racialize those who have little opportunity in a deeply precarious system – they are labeled as “fraudsters” rather than seen as rational actors working under conditions of extreme market volatility.

Preparing for market disruption: circuit breakers and discretionary trading halts

Purpose This article provides a resource for traders and other market participants by providing an overview of certain automatic circuit breaker mechanisms and discretionary powers that the U.S. Securities and Exchange Commission (SEC), Financial Industry Regulatory Authority (FINRA) and the U.S. president, as applicable, can exercise to pause or stop the trading of individual securities or trading activities across exchanges during extreme market volatility, each of which can cause interruptions to trading activity. Design/methodology/approach This article surveys automatic and discretionary mechanisms to halt trading activity under extreme market conditions. In particular, the article examines automatic cross-market circuit breakers, limit up-limit down pauses, the alternative uptick rule, as well as discretionary authority to stop short selling of particular securities and to stop trading across exchanges. Findings The article concludes that market participants must be cognizant not only of automatic cross-market circuit breakers, but also several other forms of potential market disruptions that may occur due to increased market volatility during the COVID-19 pandemic and beyond. Originality/value By exploring various mechanisms that respond to market disruption, this article provides a valuable resource for traders and other market participants looking to identify and respond to potential interruptions to their trading activity.

Predicting Extreme Returns of Bitcoin: Extreme Value Theory Approach

Extreme value theory(EVT) has been used to study the frequency and probability related to extreme situations in finance. This approach focuses on the extreme values and able to provide a better estimation for risk models. In this study, Generalized Pareto Distribution (GPD) is employed to model daily extreme returns in the Bitcoin market from 2017 to 2019. These periods have witnessed three phases of extreme volatility for the cryptocurrency market. The returns level for the Bitcoin range between 17.011 and 18.746. The results demonstrate heavy tail and finite tail distribution characteristics for the tails. The findings provide a better understanding of the tails’ behaviour in the cryptocurrency market and help investors to make a financial decision.

Deep Q-Learning for Trading Cryptocurrency

This article sets forth a framework for deep reinforcement learning as applied to trading cryptocurrencies. Specifically, the authors adopt Q-Learning, which is a model-free reinforcement learning algorithm, to implement a deep neural network to approximate the best possible states and actions to take in the cryptocurrency market. Bitcoin, Ethereum, and Litecoin were selected as representatives to test the model. The Deep Q trading agent generated an average portfolio return of 65.98%, although it showed extreme volatility over the 2,000 runs. Despite the high volatility of deep reinforcement learning, the experiment demonstrates that it has exceptionally high potential to be employed and provides a solid foundation on which to build further research. <b>TOPICS:</b>Currency, big data/machine learning, performance measurement <b>Key Findings</b> ▪ The authors use deep neural networks to create a Deep Q-Learning trading agent that approximates the best actions to take based on rewards to maximize returns from trading the three cryptocurrencies with the largest market capitalization. ▪ The Deep Q-Learning agent generates a return of 65.98% on average over the course of 2,000 episodes; however, the returns do exhibit a large standard deviation given the highly volatile nature of the cryptocurrencies. ▪ The authors introduce a framework on which future deep reinforcement learning and rewards-based trading agents can be built and improved.

A Markov-Switching VSTOXX Trading Algorithm for Enhancing EUR Stock Portfolio Performance

In the present paper, we test the benefit of using Markov-Switching models and volatility futures diversification in a Euro-based stock portfolio. With weekly data of the Eurostoxx 50 (ESTOXX50) stock index, we forecasted the smoothed regime-specific probabilities at T + 1 and used them as the weighting method of a diversified portfolio in ESTOXX50 and ESTOSS50 volatility index (VSTOXX) futures. With the estimated smoothed probabilities from 9 July 2009 to 29 September 2020, we simulated the performance of three theoretical investors who paid different trading costs and invested in ESTOXX50 during calm periods (low volatility regime) or VSTOXX futures and the three-month German treasury bills in distressed or highly distressed periods (high and extreme volatility regimes). Our results suggest that diversification benefits hold in the short-term, but if a given investor manages a two-asset portfolio with ESTOXX50 and our simulated portfolios, the stock portfolio’s performance is enhanced significantly, in the long term, with the presence of trading costs. These results are of use to practitioners for algorithmic and active trading applications in ESTOXX50 ETFs and VSTOXX futures.

Can Opening Up of Capital Markets Improve the Stability of Stock Prices? Empirical Evidence from the Shanghai–Hong Kong Stock Connect

Based on the monthly panel data of the Shanghai stock market from November 2012 to November 2016, this article studies the impact of the Shanghai–Hong Kong Stock Connect on the stability of stock prices in different periods and in varying degrees of market openness by using the difference-in-differences (DID) fixed effects model. The study finds that the implementation of the Shanghai–Hong Kong Stock Connect effectively reduces the volatility of underlying stocks and that the short-term increase in volatility is merely a temporary phenomenon. Under a comparatively higher degree of market openness, the Shanghai–Hong Kong Stock Connect will also have an impact on extreme volatility of underlying stocks, reducing the jump risk in stock prices and speculation on China's A-share market. However, it also increases the stock price crash risk and brings about market instability. Of particular interest, the observed increase in stock price crashes is related to firms with poor information disclosure practice.

Rescaled range permutation entropy: a method for quantifying the dynamical complexity of gas–liquid two-phase slug flow

Uncovering dynamic nonlinearity of slug flow with structural mutation constitutes a challenging problem of significant importance. Rescaled range permutation entropy has been recently introduced as a novel measure for the complexity of chaotic time series with extreme volatility by assigning the ratio of range and standard deviation as the weight for each extracted vector. However, previous work is only limited to the analysis of typical chaotic systems. The intrinsic advancement and the potentiality in the practical application of the algorithm still need to be investigated. Therefore, we firstly conduct simulations on characterizing impulsive signal to demonstrate that rescaled range permutation entropy is more appropriate to characterize the extreme volatility of system. In addition, a practical application in experimental signals analysis of gas–liquid two-phase slug flow is discussed in detail. The result shows that the rescaled range permutation entropy has a significant discernibility for the flow structure sudden change and paves a new way on investigating the dynamic complexity of slug flow.

Electricity price modelling with stochastic volatility and jumps: An empirical investigation

Over the past few years, the electricity derivatives market has experienced a substantial growth in the volume of trade and the diversity of available products. This has led to a rich data environment that requires more sophisticated and accurate modelling approaches for electricity spot prices. This paper deals with an analysis of continuous-time stochastic volatility jump-diffusion processes in the context of pricing of futures contracts written on electricity spots. We formulate a variety of models which aim to capture the most prominent characteristics and stylised facts of the electricity spot market including mean reversion, seasonality, extreme volatility, and spikes. The proposed modelling framework extends the existing models by incorporating mean reversion, stochastic volatility, and jumps in both the underlying spot price process and its volatility. The modelling parameters are estimated using the Markov Chain Monte Carlo (MCMC) technique for the Australian electricity market. We find that incorporating stochastic volatility and jumps in both the underlying electricity spot price and its volatility is absolutely essential to accurately fit the observed electricity spot prices. We derive futures prices in a semi-closed form and confirm flexibility of the proposed models by their ability to fit the observed spot and futures prices in the Australian electricity market.

Layoffs, inequity and COVID-19: A longitudinal study of the journalism jobs crisis in Australia from 2012 to 2020

In Australia and beyond, journalism is reportedly an industry in crisis, a crisis exacerbated by COVID-19. However, the evidence revealing the crisis is often anecdotal or limited in scope. In this unprecedented longitudinal research, we draw on data from the Australian journalism jobs market from January 2012 until March 2020. Using Data Science and Machine Learning techniques, we analyse two distinct data sets: job advertisements (ads) data comprising 3698 journalist job ads from a corpus of over 8 million Australian job ads; and official employment data from the Australian Bureau of Statistics. Having matched and analysed both sources, we address both the demand for and supply of journalists in Australia over this critical period. The data show that the crisis is real, but there are also surprises. Counter-intuitively, the number of journalism job ads in Australia rose from 2012 until 2016, before falling into decline. Less surprisingly, for the entire period studied the figures reveal extreme volatility, characterised by large and erratic fluctuations. The data also clearly show that COVID-19 has significantly worsened the crisis. We then tease out more granular findings, including: that there are now more women than men journalists in Australia, but that gender inequity is worsening, with women journalists getting younger and worse-paid just as men journalists are, on average, getting older and better-paid; that, despite the crisis besetting the industry, the demand for journalism skills has increased; and that, perhaps concerningly, the skills sought by journalism job ads increasingly include ‘social media’ and ‘generalist communications’ skills.

Measuring liquidity risk effects on carry trades across currencies and regimes

We study the effects of FX liquidity risk on carry trade returns using a novel low-frequency market-wide liquidity measure. We show conclusively that the vast majority of variation in carry trade returns can be explained by two risk factors (liquidity risk and market risk). Our results are further corroborated when the mimicking liquidity risk factor is replaced with a non-tradable innovations risk factor. Safe-haven currencies (SHC) provide insurance against crash risk by having negative liquidity betas, across all time periods. SHCs provide the highest levels of protection during periods of extreme volatility. We find that liquidity risk is priced in the cross-section of carry trade returns, and estimate the liquidity risk premium in the FX market to be around 412 basis points per annum.

Impact of COVID-19 pandemic on the energy markets

This article aims to uncover the effects of the COVID-19 pandemic on the energy markets in terms of energy stock indexes, energy futures, ETFs, and implied volatility indexes. We model the volatility of energy markets and demonstrate the effects of various phases of the pandemic outbreak (COVID-19) on the energy market. COVID-19-induced uncertainty indicators like the growth of the infection, economic policy uncertainty (EPU), and infectious diseases market volatility (IDsMV) have shown pronounced effects on energy markets’ historical volatility. The volatility of energy ETFs–stocks appears to be more resilient in line with S&P 500 energy stocks. WTI crude oil market has shown an unprecedented overreaction amid pandemic outbreaks and traded with an extreme volatility level. The investors’ sentiment in the energy market was factually higher on the tail events, indicating that fearful investors rushed toward put options and paid an excess premium to protect from unparalleled risk in the energy market.

COVID-19 and food security as catalyst of conflict among rural households in Nigeria: a study of Ilaje community, Ondo state

PurposeThe compulsory lockdown issued by the Nigeria Government to curb the spread of the virus has undoubtedly negatively affected the livelihoods of households. As a result, many have lost their sources of income and have no other means for survival. It is important to note that it has the potential of triggering violent conflicts and civil unrest as a popular African adage goes as thus: “A hungry man is an angry man”. Unfortunately, little attention has been given, neither has extant literature adequately covered the impact of the coronavirus (COVID-19) outbreak as it directly affects households in a rural community in Nigeria.Design/methodology/approachThis study proceeded from on both a qualitative and quantitative method and adopted a mixed-method and case study research design. Data for this study was collected through primary and secondary data. Primary data for this study was collected through a questionnaire and focus group discussions (FGD). The questionnaire used for this study was a validated instrument designed by the US Department of Agriculture (USDA) to measure food security amongst households. The USDA household’s instrument was used and then modified to access the food security status of households during the lockdown.FindingsFindings from this study revealed that households, in general, were categorised as having “very low food security” during the lockdown as they testified to all the food insecure conditions. In general, the food intake of household members reported irregular eating patterns as the household lacked the money to purchase food. This study further indicated that a high number of respondents see violence as a means of attaining food. Extreme volatility in their inability to access food as a result of food shortages and the inability to benefit from government palliatives has been found to trigger incidents of conflict and malnutrition on households.Research limitations/implicationsThis method was sampling method was adopted because of the compulsory and constitutional lockdown imposed by the government to curb the spread of the novel COVID-19 which limited the movement of the researcher to other households in the community which proved to be limited during the time this research was carried limiting the scope of the sample size.Originality/valueThis research adopted mixed-method research and was written by the researchers who did the study. This paper was conducted during the mandatory lockdown issued by the Federal Government in Nigeria with to understand the impact of the mandatory lockdown on rural households using Ilaje local of Ondo state as a case study while establishing a link between food security and conflict.

Impact of Tools on the Acquisition of RAM Memory

When responding to a security incident in a system, several basic principles must be followed regarding the collection of pieces of evidence from the system. The capture of these pieces of evidence has to be done according to its order of volatility. In this sense, RAM memory constitute the most important element to capture, given its extreme volatility. RAM memory must be acquired and analyzed because the data it holds, which may belong to the system itself or to any other device connected to it, can survive a certain amount of time in it. Since RAM is a constantly changing element, it must be stood out that any action carried on the system under analysis will modify the contents of the RAM. In this article a comparative and an objective analysis has been carried out, showing the impact that the execution of some tools for the capture of RAM has on the system. This comparative study details both the private shared workspaces, for each of the processes executed by each of the tools used.

Extreme volatility dependence in exchange rates

This paper aims to analyse asymmetric volatility dependence in the exchange rate between the British Pound, Japanese Yen, Euro, and Mexican Peso compared to the U.S. dollar during different periods of turmoil and calm sub-periods between (1994-2018). GARCH and TARCH models are employed to model conditional

Impact of Weather on Agriculture Commodity Market and the Price Discovery: An Empirical Analysis

This paper focused on studying the agricultural commodity prices in India and it's extreme volatility due to many reasons such as government interference, growth, market forces factors, regular floods and droughts, transport and warehousing problems, etc. These are contributing factors to demand fluctuations. In this case, the future market plays an important role in the economy. The demand for commodity futures has three particular economic functions: price discovery, price risk management, and price volatility. The future market plays a key role in the process of price discovery. The main aim of this system is to regulate prices to minimize uncertainty, to provide price signals to market traders for futures spot prices through the price discovery phase. So, this study emphasized the role of the derivative market in reducing the volatility of agricultural commodity prices in the Indian market. Keywords: volatility, future market, derivatives

Impact of Covid-19 on Islamic Stock Markets: An Investigation using Threshold Volatility and Event Study Models

The Covid-19 is an unexpected event in the world history with substantial socio-economic impact on the global economy. The global financial market was also badly affected as reflected by the extreme volatility as well as weak performances in the stock markets all over the world. How do the Islamic stock markets in various parts of the world behave during the Covid-19 shock? The objective of this study is to identify the impact of the Covid-19 pandemic as declared by the World Health Organization on the Islamic stock markets. Using the threshold volatility and event study models, the study analyses the impact of the Covid-19 announcement on the Islamic stock indices in the Indian Stock Exchange (represented by the Bombay Stock Exchange - BSE Shariah Index) and Indonesian Stock Exchange (represented by the Jakarta Islamic Indices - JII). With the date of event identified as 11th March 2020, the event window consists of 60, 30, and 20 days. The results show that the BSE Shariah and JII have positive coefficients, with the BSE Shariah Index shows negative response to the announcement of Covid-19 as global pandemic. On the other hand, the JII reacted positively to the event. The study shows the reaction of a stock exchange is dependent on other economic factors unique to the country, resulting in the events impact of the Covid-19 to vary from one country to another.

Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

In the present paper, we test the use of Markov-Switching (MS) models with time-fixed or Generalized Autoregressive Conditional Heteroskedasticity (GARCH) variances. This, to enhance the performance of a U.S. dollar-based portfolio that invest in the S&amp;P 500 (SP500) stock index, the 3-month U.S. Treasury-bill (T-BILL) or the 1-month volatility index (VIX) futures. For the investment algorithm, we propose the use of two and three-regime, Gaussian and t-Student, MS and MS-GARCH models. This is done to forecast the probability of high volatility episodes in the SP500 and to determine the investment level in each asset. To test the algorithm, we simulated 8 portfolios that invested in these three assets, in a weekly basis from 23 December 2005 to 14 August 2020. Our results suggest that the use of MS and MS-GARCH models and VIX futures leads the simulated portfolio to outperform a buy and hold strategy in the SP500. Also, we found that this result holds only in high and extreme volatility periods. As a recommendation for practitioners, we found that our investment algorithm must be used only by institutional investors, given the impact of stock trading fees.