Trading Volatility Research Articles

Analysis of Different Trading Strategy Applicability to Cryptocurrency Market

Since cryptocurrency trading and regulatory mechanisms are not yet mature, and there are few studies on the applicability of various trading strategies, it is necessary to conduct research on cryptocurrency market trading strategies to promote its healthy development. To this end, using the commonly used historical price data of Bitcoin, Ethereum, and Litecoin, the strategies of pairs trading, momentum trading, and grid trading are applied to analyze their trading characteristics, explore their applicability, and put forward relevant suggestions. The research results show that each trading strategy has its applicable market environment, and the trading strategy should be reasonably selected according to the market situation. Pairs trading uses cryptocurrency pairs with strong correlations to select a reasonable trading range according to their residual swings trend. Its trading volatility and yield are low but relatively stable. Momentum trading is suitable for highly volatile cryptocurrencies. The Moving Average Convergence and Divergence (MACD) and the Relative Strength Index (RSI) can be comprehensively applied to effectively reduce the number of momentum transactions and improve the accuracy of price swings trend prediction. Grid trading can achieve stable and considerable returns in a market with long-term strong volatility, but transactions are more frequent. The number and size of grids should be dynamically adjusted according to the value and volatility of cryptocurrencies and combined with market changes.

Exploring the principal factors driving the volatility structure of stock trade levels in New Zealand: a Covid-19 perspective

This paper explores factors that drive the volatility structure of New Zealand stock trade levels, with the aim of understanding unusual volatility of stock trade levels that may pose a risk to the critical operations of a trading venue. Weekly number of trades from 2014–2024 for the sampled stocks were examined within principal axis factoring (PAF) and GARCH models. The results showed that three factors accounted for around 60% of volatility in the number of stock trades over the sample period; they were driven primarily by electricity generation stocks, consumer discretionary stocks, and commercial property stocks respectively. During the sample period, two subperiods of market shock showed unusual levels of volatility in all principal factors, with the second subperiod, which coincided with the Covid-19 outbreak, being particularly notable. An implication of the study is that given the volatilities of stock trade levels are little studied in the literature, the findings of this study could be a useful complement to existing studies examining stock price volatility for the purposes of mitigating unusual stock trading volatility.

Distance(s) and the volatility of international trade(s)

We show that distance matters for the volatility of international trade and financial transactions on top of its well-known impact for their levels. We conduct event studies on the global financial crisis and the Covid-19 pandemic with country-level and product-level data, and a longer panel data analysis. We consider measures of physical, virtual, and language distance jointly – the latter two proxying for ease of communication. We find evidence of larger trade declines in more distant country dyads and underscore the relevance of information frictions rather than shipment costs. Physical distance matters for trade volatility beyond goods, as do virtual and language distances. Physical and virtual distances amplify each other's effects at the country level, as do virtual and language distances at the product level. Distance effects are also weaker for homogenous products and foreign direct investment and banking activity entailing local presence, again pointing to the importance of information frictions.

Trading activity of VIX futures and options around FOMC announcements

This research investigates the information content of volatility trading in VIX derivatives under a high-frequency framework. We provide empirical evidence that the abnormal order imbalances of VIX futures and call (put) options are significantly negative (positive) during FOMC embargoes. Our results remain robust under various empirical approaches for examining FOMC announcements. We also find that the VIX return (trading volume) is negatively (positively) associated with FOMC announcements. Overall, we document short-lived informational advantages in the VIX derivatives market and provide evidence of potential information leakage during FOMC embargoes.

The price continuity, return and volatility spillover effects of regular and after-hours trading.

This study employs a bivariate EGARCH model to examine the Taiwan Futures Exchange's regular and after-hours trading, focusing on the critical aspects of spillover and expiration effects, as well as volatility clustering and asymmetry. The objective of this study is to observe the impact on the trading sessions in Taiwan by the influences of the European and American markets, focusing on the essential roles of the price discovery function and risk disclosure effectiveness of the regular hours trading. This research is imperative considering the increasing interconnectedness of global financial markets and the need for comprehensive risk assessment for investment strategies. It also examines the hedging behavior of after-hours traders, thereby aiming to contribute to pre-investment analysis by future investors. This examination is vital for understanding the dynamics of after-hours trading and its influence on market stability. Results indicate price continuity between both trading sessions, with regular trading often determining after-hours price ranges. Consequently, after-hours price changes can inform regular trading decisions. This finding highlights the importance of after-hours trading for shaping market expectations. Significant profit potential exists in after-hours trading open interest, which serves speculative and hedging purposes. While regular trading volatility influences after-hours trading, the reverse is not true. This suggests Taiwan market information poses a higher risk impact than European and American market data, emphasizing the unique position of the Taiwan market in the global financial ecosystem. After-hours trading volatility reflects the absorption of international market information and plays a crucial role in advance revelation of risks. This underscores the importance of after-hours trading in global risk management and strategy formulation.

Underpricing And Herding Behaviour Ipo Stock In Covid-19 Pandemic At Indonesian Stock Exchange

AbstractThis study aims to empirically test whether there is underpricing and herding behavior in companies that have just IPO on the Indonesia Stock Exchange in 2020. The selection in 2020 is because the stock exchange experiences high trading volatility due to the crisis impact of the Covid-19 pandemic. This study uses a market adjusted return approach in calculating underpricing, and uses the Chang, Cheng, and Khorana (CCK) method to indicate the occurrence of herding behavior in IPO shares on the Indonesia Stock Exchange. The significance level used in the study was 5%. The results show that there is an underpricing phenomenon in companies whose IPOs are on the IDX and there is no evidence of herding behavior by investors on IPO shares on the IDX. Investors are advised to invest in IPO shares, especially during the pandemic and make rational considerations in making investment decisions in the capital market. Keywords: Underpricing, Herding Behavior, IPO Shares, Pandemic, Capital Market

Improving the Machine Learning Stock Trading System: An N‐Period Volatility Labeling and Instance Selection Technique

Financial technology is crucial for the sustainable development of financial systems. Algorithmic trading, a key area in financial technology, involves automated trading based on predefined rules. However, investors cannot manually analyze all market patterns and establish rules, necessitating the development of supervised learning trading systems that can discover market patterns using machine or deep learning techniques. Many studies on supervised learning trading systems rely on up–down labeling based on price differences, which overlooks the issues of nonstationarity, complexity, and noise in stock data. Therefore, this study proposes an N‐period volatility trading system that addresses the limitations of up–down labeling systems. The N‐period volatility trading system measures price volatility to address uncertainty and enables the construction of a stable, long‐term trading system. Additionally, an instance‐selection technique is utilized to address the limitations of stock data, including noise, nonlinearity, and complexity, while effectively reducing the data size. The effectiveness of the proposed model is evaluated through trading simulations of stocks comprising the NASDAQ 100 index and compared with up–down labeling trading systems. The experimental results demonstrate that the proposed N‐period volatility trading system exhibits higher stability and profitability than other trading systems.

Volatility-Managed Volatility Trading

Volatility-Managed Volatility Trading

Bilateral trade of Turkey-US: Can euro explain this relationship?

This study applies Autoregressive Distributed Lag (ARDL) cointegration method to study the impact of exchange rate fluctuations on Turkey-US trade, considering the third country effect. We estimated bilateral exports and imports of 10 industries between Turkey and the US, collected trade data between Turkey and the US and related exchange rate fluctuation indicators, and established economic models. The results show a two-way interaction effect of exchange rate fluctuations on bilateral trade. At the same time, we find that the third-country effect plays a vital role in Turkey-US trade, and changes in demand and supply from other countries indirectly impact bilateral trade. This study helps to understand the complexity and volatility of trade between Turkey and the US and provides reference for relevant economic policies. These insights help not only address challenges posed by exchange rate fluctuations and third-country effects but also promote stable and sustainable bilateral trade in an evolving global economy.

Research on the Efficacy of the iVIX Index Based on VIX Pricing

ABSTRACT Amidst rising trading volumes, there’s evident demand for volatility trading. This study assists Chinese stakeholders in understanding the iVIX’s functionality, offering insights for investments and policy-making. Building on the VIX option pricing model, the research introduces a novel approach: the mean-reverting logarithmic stochastic volatility Hawkes jump model (MLSVHJ). Using VIX call option data from March 9, 2020, to March 18, 2021, the MLSVHJ model outperformed four others during 2020’s three US market crashes. Additionally, this model’s parameters aligned well with theoretical expectations. However, the positive correlation observed between the iVIX and the Shanghai Composite Index prompts a reevaluation of applying global VIX standards directly to China.

Islamic Stock Market Reaction To The Russia-Ukrainian War

This study aims to analyze stock market reaction events preceding and following the proclamation of the Russian invasion of Russia for companies listed on the Indonesia Stock Exchange and indexed by the Jakarta Islamic index. This investigation employs a quantitative approach with an event study methodology. The sample consists of thirty shares of companies listed on the Jakarta Islamic Index. The research sample was selected using the technique of purposive sampling. The research data were analyzed utilizing the paired sample t-test method. The findings of this study indicate that the anomalous return variable affects Jakarta Islamic Index stock prices differently before and after the announcement of the Russian invasion of Ukraine. Before and after the proclamation of the Russian invasion of Ukraine, there is a difference in the trading volume of stocks included in the Jakarta Islamic Index. Investors need to reallocate their assets strategically by developing a hedging strategy to avoid the risk of global uncertainty induced by unpredictability. Investors must invest long-term in anticipating speculators on the capital market, who can cause aberrant returns and abnormal stock trading volatility. To stabilize the stock market, regulators should consider the adverse effects of the financialization/globalization of the stock market in terms of war/conflict. Speculators prey on the uncertainty of global events, which leads to tyranny and abnormal rates of abnormal returns.

240 years of Swedish terms of trade – structure, volatility, and connection to economic growth

In this paper we present new foreign trade- and price data to analyze Swedish terms of trade in the very long run, from 1780 to 2018. We examine what influenced the trend in the terms of trade in the long run, particularly in how it related to the general trade structure. We also analyze the volatility of the terms of trade, and discuss how it might have affected economic growth in Sweden. The index improved slowly but substantially over the century from 1850 to 1950, which was likely connected to another poignant trend, a steadily increasing share of manufactures and finished goods in the export basket. In comparative perspective, Sweden had more industrial exports and lower export concentration than most of the rest of the European periphery, as well as much lower terms of trade volatility. We find that the growth trend and low volatility of Swedish terms of trade was positively connected with economic growth, but only really so from about 1850 to 1913. During this period the development of the terms of trade was particularly connected to increasing investment in new industries. The years from 1914 to 1960 were on the other hand characterized by high volatility in the terms of trade, and was still connected to high growth rates, such as during the interwar period. Hence, we conclude that while low volatility was connected to the onset of Swedish industrialization, it was also possible to maintain high growth rates during periods of high terms of trade volatility.

Testing for monotonicity, linearity and symmetry between trading volume and price returns in the futures markets of agricultural commodities: a discussion on the financial implications

PurposeThe aim of this study is to investigate for monotonicity, linearity and symmetry for the price volatility–trading volume relationship in the futures markets of agricultural commodities.Design/methodology/approachEmpirical findings are produced with the use of a highly flexible, nonparametric approach. Data are daily prices and volumes from the commodities of corn, hard red wheat, oats, rice and soybeans.FindingsResults reveal violations of monotonicity locally but not globally. Volume and price volatility have, in all markets, a nonlinear relationship to each other, indicating that the strength of the relationship does not remain constant over the entire joint distribution. Global symmetry is rejected for the markets of oats and hard red wheat but cannot be rejected for the remaining three markets. The latter suggests that large values of good volatility are likely to occur together with high trading volumes, as do large values of bad volatility in these markets.Originality/valueTo the best of the authors’ knowledge, this is the first empirical work to test simultaneously for monotonicity, linearity and symmetry between price volatility and trading volume in the futures markets of agricultural commodities.

Terms of trade volatility and tax revenue in Sub‐Saharan African countries

AbstractTerms of trade remain inherently volatile in the global market, exerting pressure on tax revenue with the resulting change in prices and trade (export or import) volumes. This study investigated the empirical relationship between terms of trade volatility from a regional perspective using a dynamic bias corrected model. A corrected dynamic fixed effect and one‐step diff‐GMM models are used to estimate the effect of terms of trade volatility using a set of panel data for 41 sub‐Saharan African (SSA) countries from 1990 to 2022. The findings show that terms of trade changes have a positive effect on tax to GDP ratio and its volatility has indirect negative effects. Informal economic activities tend to deteriorate tax revenue in SSA. Thus, terms of trade volatility depress growth of GDP per capital and tax base in SSA. The findings imply the need for hedging the risks associated with terms of trade volatility, intensifying agriculture production and increasing the speed of formalizing informal sectors in order to reduce associated risks of depressing tax base in SSA.

Beware of Extreme Investor Sentiments! Indian Evidence on the Performance of Neuro-specific Options Volatility Trading Strategies on the Facets of COVID-19

This study investigates the dynamic relationship between noise trader sentiment and excessive volatility in the Indian financial market during the COVID-19 outbreak. It proposes novel options trading strategies to ensure profitability in times of irrational exuberance and to satisfy diverse investment requirements of volatility traders, which arise from the varying levels of stimulating neurotransmitters, namely dopamine and serotonin in the human body. Empirical results show that the incremental information content of sentiment measures is vital in forecasting future volatility and the proposed options trading strategies effectively accomplish the neuro-specific intentions of the traders during extreme volatility in the Indian equity market. JEL Codes: G12, G13

Commodity terms of trade volatility and industry growth

The rising volatility of commodity terms of trade (CTOT) is associated with a high cost of capital and a low credit supply for producers in commodity-dependent countries. In this paper, we examine how volatile CTOT influences various industries’ growth performance based on sector-level panel data for countries specializing in commodity exports. We find robust evidence that CTOT volatility causes a more significant growth loss in manufacturing sectors facing tighter credit constraints. The adverse growth effects operate through lower total factor productivity in industries heavily reliant on external finance for long-term investments and through lower capital accumulation in industries with high liquidity needs for short-term working capital. Our findings offer a complementary explanation for the “resource curse” through the credit constraint channel.

Analytical Perspective of Trade Volatility Spillover Analysis During COVID Pandemic Times

No one in the world, particularly the economic community, could have predicted the manner in which the 2019 COVID pandemic erupted or shook the economies. Developing countries, particularly South Asian economies and the lower middle income (LMI) countries (India, Pakistan, Sri Lanka, Bangladesh, Bhutan, and Nepal), had also experienced similar turmoil. The COVID outbreak and trade-related risks were closely associated with exports, imports, international liquidity, and SDR exchange rates, posing time-varying risks. In this sphere, an analytical approach to generate meaningful trade-related time-varying volatility predictions with respect to India’s position is critical while considering the above-listed variables. The Markov-switching (endogenous switching) VAR model using the exponential moving average volatility model (EWMA) of the four macroeconomic variables was used. The author tried to use the EWMA volatility model to witness the impact of spillovers and shock persistence. The present study earmarked the behaviour of time-varying trade-related risks (for Regime 1, i.e., the Early COVID period versus Regime-2 during the COVID period) in terms of the top six economies (as per December 2021 data) when endogenous factors like international liquidity and exchange rate shocks are implemented.

Is the Global Economy Deglobalizing? If So, Why? And What Is Next?

ABSTRACT: Data on global trade as well as capital and labor flows indicate a slowdown, but not reversal, of globalization since the 2008–2009 financial crisis. Yet profound changes in the policy environment and public sentiment in the largest economies over the past five years suggest the beginning of a new era. Increasing anxiety about the labor market effects of import competition from low-wage countries, especially China, laid the groundwork but was not the catalyst for the reversal in attitudes toward globalization. Similarly, the COVID-19 pandemic provided novel arguments against free trade based on global supply chain resilience, but neither the pandemic nor short-run policy response had enduring effects on trade flows. We demonstrate that global trade was remarkably resilient during the pandemic and that supply shortages would likely have been more severe in the absence of international trade. After a temporary decline in 2020, global trade in goods and services increased sharply in 2021. Russia's invasion of Ukraine raised new concerns about national security and the exposure of supply chains to geopolitical risk. This was followed by demands to diversify away from "non-friendly" countries and toward the employment of trade policy, export restrictions in particular, to halt China's technological development. The future of globalization is highly uncertain at this point, but these new policies will likely slow global growth, innovation, and poverty reduction even if they benefit certain industries in certain countries. Regarding resilience, the main goal of recent trade policy changes, measures of trade volatility or concentration can be helpful, but resilience will be elusive as long as we lack benchmarks against which policy performance can be measured.

Factors impacting the price of the gold: an empirical study of EGARCH model

Purpose- The gold as a fundamental asset has been displaying a high and sharp price volatility in international financial markets. The price dynamics of gold are believed to be influenced by non-linear dependencies with stock market indices, exchange rates, and commodity prices. Therefore, it is rational to examine the factors contributing to the non linear inferences. As a financial asset for the portfolio investment and hedging, gold prices display multilateral and dynamic data patterns this study aims to focus the analysis of price volatility which will contribute to the volatility structure, persistence, and correlated behaviors of gold prices. Methodology- To display and analyze the persistence level of volatility in the financial markets, a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) specification is employed. GARCH class models are further applied to determine the relevance of the leverage effect of market news, the progress of spillover pattern, and the effects on the risk-premium. Findings- Volatility in gold price has become an important feature of the financial markets in terms of volatility trading in adjusting the international portfolio investments. The correlated structure of the financial markets and the contagion effects of the news have displayed the asymmetric and complex but time-fragmented portfolio returns in terms of short and long term volatility formation and forecasting. Conclusion- Since gold’s safe haven status have played a major stake in the determination of the gold price, it is undeniable that the speculators’ rising power, the artificial intelligence utilisation in robo-trading, global and regional geo-political tensions, the recent developments in the risk sentiment in investment portfolios have an increasing amount of influence on gold prices. Keywords: Gold returns, US Dollar Index, exponential generalized autoregressive conditional heteroskedasticity, volatility persistence. JEL Codes: C58, G12, C22, G17

Predicting abnormal trading behavior from internet rumor propagation: a machine learning approach

In 2021, the abnormal short-term price fluctuations of GameStop, which were triggered by internet stock discussions, drew the attention of academics, financial analysts, and stock trading commissions alike, prompting calls to address such events and maintain market stability. However, the impact of stock discussions on volatile trading behavior has received comparatively less attention than traditional fundamentals. Furthermore, data mining methods are less often used to predict stock trading despite their higher accuracy. This study adopts an innovative approach using social media data to obtain stock rumors, and then trains three decision trees to demonstrate the impact of rumor propagation on stock trading behavior. Our findings show that rumor propagation outperforms traditional fundamentals in predicting abnormal trading behavior. The study serves as an impetus for further research using data mining as a method of inquiry.