Periods Of Market Turbulence Research Articles

Investing during a Fintech revolution: The hedge and safe haven properties of Bitcoin and Ethereum

Global financial markets have witnessed significant transformations in response to the worldwide spread of the Covid-19 pandemic, resulting in heightened risk and alterations in inter-market relationships. Both cryptocurrency markets and conventional equity markets have been influenced by these changes. This study aims to investigate the potential hedging and safe haven benefits of Bitcoin and Ethereum against risks in Asian emerging stock markets under various Covid-19 pandemic contexts including the onset of the pandemic, varying levels of panic, the extent of media attention, and sentiment analysis. Utilizing the dynamic correlation coefficients derived from the DCC-GARCH methodology, our analysis indicates that, both Bitcoin and Ethereum do not effectively serve as hedges or safe havens for major Asian emerging stock markets during the Covid-19 outbreak. These results remain robust during more extreme periods characterized by high panic, intense media attention, and negative sentiment. Nevertheless, an noteworthy exception is the case where Ethereum exhibits strong hedging properties specifically in the context of the Pakistan equity market. Recognizing these unique hedge and safe haven potential of cryptocurrencies can contribute to enhancing the effectiveness of investment strategies and risk management practices during periods of market turbulence.

Comparative Analysis of GARCH-Based Volatility Models of Financial Market Volatility: A Case of Nairobi Security Market PLC, Kenya

This paper conducted a comprehensive comparative analysis of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models to forecast financial market volatility, with a specific focus on the Nairobi Stock Exchange Market. The examined models include symmetric and asymmetric GARCH types, such as sGARCH, GJR-GARCH, AR (1) GJG-GARCH, among others. The primary objective is to identify the most suitable model for capturing the complex dynamics of financial market volatility. The study employs rigorous evaluation criteria, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Error (ME), and Root Mean Absolute Error (RMAE), to assess the performance of each model. These criteria facilitate the selection of the optimal model for volatility forecasting. The analysis reveals that the GJR-GARCH (1,1) model emerges as the best-fit model, with AIC and BIC values of −5.5008 and −5.4902, respectively. This selection aligns with the consensus in the literature, highlighting the superiority of asymmetric GARCH models in capturing volatility dynamics. The comparison also involves symmetric GARCH models, such as sGARCH (1,1), and other asymmetric models like AR (1) GJG-GARCH. While these models were considered, the GJR-GARCH (1,1) model demonstrated superior forecasting capabilities. The study emphasizes the importance of accurate model selection and the incorporation of asymmetry in volatility modeling. The research provides essential insights into financial market volatility modeling and forecasting using both asymmetric and symmetric GARCH models. These findings have significant implications for government policymakers, financial institutions, and investors, offering improved tools for risk assessment and decision-making during periods of market turbulence.

Does the dynamics between government bond and equity markets validate the adaptive market hypothesis? evidence from transfer entropy

ABSTRACT The study applies the effective transfer entropy (ETE) and Renyi transfer entropy (RTE) to quantify the information flow between government bonds and equity market of G7 countries. The magnitude and direction of information flow between these two markets are dynamic across the state of the markets and time thus confirming their adaptiveness to the evolution of cross-market information flow under different market conditions over time. Although information flow from the equity dominates the flow from the bond market, it is dynamic over the time and state of the markets. However, during the period of market turbulence, the bond dominates the equity market. This study is the first of its kind to validate the adaptive market hypothesis using the novel transfer entropy framework in the bond and equity market.

Ensembling two deep learning algorithms to efficiently solve the problem of predicting volatility in applied finance

Volatility is one of the most commonly used terms in the trading platform. In financial markets, volatility reflects the magnitude of price fluctuations. High volatility is associated with periods of market turbulence and sharp price fluctuations, while low volatility characterizes more relaxed pricing. When trading options, it is especially important for trading firms to accurately predict volatility values, since the price of options is directly related to the profit of a trading firm. A proactive artificial intelligence model that allows predicting volatility for future periods of time will be presented in this article.

Organizational culture and leadership as antecedents to organizational flexibility: implications for SME competitiveness

PurposeThis study aims to assess how innovative organizational culture and innovative leadership generate market flexibility for small and medium enterprises (SMEs) in the service sector to enhance their competitiveness. Both organizational culture and leadership are seen as firm-level resources capable of influencing the flexibility of the firm in periods of market turbulence. The study argues that SME service firms must use their internal resources to develop their flexibility capability which is more valuable, rare, inimitable and not substitutable.Design/methodology/approachSME service firms from Ghana are used to test the study’s hypotheses through robust standard regression analysis. A sampling frame was developed from an online database of small and medium enterprises operating in the service sector.FindingsThe findings suggest that although organizational culture and leadership may influence a service firm’s competitiveness, it is more viable to use these firm-level resources to create market flexibility capability to amplify the effect. This means, when culture and leadership propel the flexibility drive, the service firm is able to connect, coordinate and synchronize functional units to take advantage of new product and market opportunities. Additionally, market flexibility emanating from organizational culture and leadership wields enough power and resource support to tackle the turbulent market conditions better than firms with less support.Practical implicationsThe managerial implication from this study is that firms should use their organizational culture and leadership to create flexible organizations that afford them the opportunity to adapt to the environmental dynamics. If both leadership and culture work together, they are able to create strong market capabilities such as flexibility which determines how well the firm will respond to the competition, customer demand and all other external pressures. It is, therefore, the view of this paper that SMEs should use their organizational culture and leadership to build a market-flexible organization to create a competitive advantage.Originality/valueThis paper shows how internal resources/assets such as culture and leadership generate the needed flexibility to create a competitive advantage for SMEs. This paper explains the two dimensions of Volberda’s flexibility from a firm-level resource perspective and highlights flexibility as a second-order capability whose cultivation and effectiveness are dependent on a firm’s culture and leadership. Evidence of how a firm’s market flexibility is fuelled by organizational leadership and culture is demonstrated. Finally, this paper shows how resource-poor SMEs in emerging African economies can enhance their market competitiveness through internal systems and processes.

Carry and Time-Series Momentum: A Match Made in Heaven

This article introduces a novel approach to combining time-series momentum and carry trade by conditioning trading signals of time-series momentum on the sign of the basis, a key input for the carry trade. We find that this new technique applied to four major asset classes improved the Sharpe ratio of time-series momentum by approximately 0.17 net of fees. The improvement in performance is greater during recessions and, therefore, conditioning time-series momentum signals on the sign of the basis improves performance when it matters the most. Thus, the new approach has practical importance for investors and asset managers who attempt to improve their long-term performance without increasing downside risk during periods of market turbulence. <b>TOPICS:</b>Portfolio construction, performance measurement, statistical methods <b>Key Findings</b> • We introduce a new approach that conditions time-series momentum trading signals on the sign of the basis. • Empirical analysis across the four major asset classes of equities, fixed income, currencies, and commodities shows an improvement in the Sharpe ratio of approximately 0.17 net of fees. • The proposed approach is particularly relevant for investors because its performance improvement is greater during recessions.

Extreme downside risk and market turbulence

We investigate the dynamics of the relationship between returns and extreme downside risk in different states of the market by combining the framework of Bali et al. [Is there an intertemporal relation between downside risk and expected returns? Journal of Financial and Quantitative Analysis, 2009, 44, 883–909] with a Markov switching mechanism. We show that the risk-return relationship identified by Bali et al. (2009) is highly significant in the low volatility state but disappears during periods of market turbulence. This is puzzling since it is during such periods that downside risk should be most prominent. We show that the absence of the risk-return relationship in the high volatility state is due to leverage and volatility feedback effects arising from increased persistence in volatility. To better filter out these effects, we propose a simple modification that yields a positive tail risk-return relationship in all states of market volatility.

Forecasting Realized Correlations: A MIDAS Approach

Mixed data sampling (MIDAS) regression has received much attention in relation to modeling financial time series due to its flexibility. Previous work has mainly focused on forecasting of realized volatilities and has rarely been used to predict realized correlations. This paper considers a MIDAS approach to forecast realized correlation matrices. A MIDAS model is estimated via nonlinear least squares (NLS) using an analytical gradient-based optimization. Based on the model confidence set (MCS) procedure we discover that the introduced approach is superior compared to the established heterogeneous autoregressive (HAR) model in terms of out-of-sample forecasting accuracy. This preeminence is due to the flexible data-driven origin of the MIDAS model. The latter results in higher economic value with regard to portfolio management applications. The improvement is considerable for longer forecasting horizons both in calm times and during the periods of market turbulence.

Financial fluctuations anchored to economic fundamentals: A mesoscopic network approach

We demonstrate the existence of an empirical linkage between nominal financial networks and the underlying economic fundamentals, across countries. We construct the nominal return correlation networks from daily data to encapsulate sector-level dynamics and infer the relative importance of the sectors in the nominal network through measures of centrality and clustering algorithms. Eigenvector centrality robustly identifies the backbone of the minimum spanning tree defined on the return networks as well as the primary cluster in the multidimensional scaling map. We show that the sectors that are relatively large in size, defined with three metrics, viz., market capitalization, revenue and number of employees, constitute the core of the return networks, whereas the periphery is mostly populated by relatively smaller sectors. Therefore, sector-level nominal return dynamics are anchored to the real size effect, which ultimately shapes the optimal portfolios for risk management. Our results are reasonably robust across 27 countries of varying degrees of prosperity and across periods of market turbulence (2008–09) as well as periods of relative calmness (2012–13 and 2015–16).

PROFITABILITY OF A SIMPLE PAIRS TRADING STRATEGY: RECENT EVIDENCES FROM A GLOBAL CONTEXT

Pairs trading strategy is a popular investment strategy, where traders long one stock and short the other stock. The trading profits are expected to be “immune” to any market conditions: being uptrend, downtrend, or sideways, instead the performance is determined by the relative performance of the pair. Following Gatev et al. [(1999) Pairs Trading: Performance of a Relative-Value Arbitrage Rule. Working Paper, Yale School of Management; (2006) Pairs trading: Performance of a relative-value arbitrage rule, The Review of Financial Study, 19, 797–827] and Do &amp; Faff [(2010) Does simple pairs trading still work? Financial Analyst Journal, 66, 1–12], we examine whether the simple pairs trading rule is also profitable in markets outside of the US. We also examine whether the trading rule performs consistently during bull and bear markets, including the recent period of market turbulence. Our results show that in most countries, the strategy generates positive returns, without evidence of under performance during bear markets. Unlike prior research, we do not find that the trading profits diminish over recent years. The pairs trading strategy generates positive returns even after transaction costs. However, the returns deteriorate significantly at a higher level of transaction costs. It is also found that the correlation between the returns on our pairs trading portfolios and the returns on the corresponding stock market indexes is low, confirming its role as a diversifier to the traditional long only investments.

Executives’ professional ties along the supply chain: The impact on partnership sustainability and firm risk

This paper investigates the effect of management-level professional ties between suppliers and customers on the sustainability of business partnerships. We find that the presence of cross-firm professional ties between directors and senior executives along the supply chain significantly reduces the probability of relationship termination around customers’ industry negative shocks and during financial crises. The results are robust using professional-tie strength as an alternative measure. Exploring contingency effects, we find that, for suppliers who lack R&D, face high competition, are smaller in size, or are less important to customers in terms of sales, such professional ties are more helpful in sustaining such relationships. Furthermore, we find that professional ties also significantly reduce firm risk during periods of market turbulence. Taken together, our results suggest that professional ties along the supply chain can facilitate information flow and build mutual trust, which can lead to healthy long-term relationships and can help firms survive economic and industry downturns.

Extreme Downside Risk and Financial Crises

We investigate the dynamics of the relationship between returns and extreme downside risk in different states of the market by combining the framework of Bali, Demirtas, and Levy (2009) with a Markov switching mechanism. We show that the risk-return relationship identified by Bali, Demirtas, and Levy (2009) is highly significant in the low volatility state but disappears during periods of market turbulence. This is puzzling since it is during such periods that downside risk should be most prominent. We show that the absence of the risk-return relationship in the high-volatility state is due to leverage and volatility feedback effects arising from increased persistence in volatility. To better filter out these effects, we propose a simple modification that yields a positive tail risk-return relationship under all states of market volatility.

Better Investing Through Factors, Regimes and Sensitivity Analysis

Recent periods of market turbulence and stress have created considerable interest in credible alternatives to traditional asset allocation methodologies. It would be preferred if portfolios can be decomposed into components that can be directly connected to independent risks and individually rewarded by the market for their level of risk. This can be achieved through factor-based investing, which relies on the observation that most return and risk characteristics for all asset classes can be well explained by particular building blocks, or factors.We describe main features of factors, factor investing and factor models, with emphasis placed on practical topics such as selection of significant factors associated to specific asset classes, differentiating between factors, anomalies or stylized facts, and preference for composite portfolios based on combining factors. We have also analyzed implementation details and the factor risk parity strategy.Then we consider improvements to factor-based investing through regime switching and sensitivity analysis. We present theoretical and practical frameworks for Markov switching models and for sensitivity analysis, and rely on representative examples to illustrate the benefits of efficiently incorporating regimes and sensitivity analysis into portfolio management.The final section describes features of good testing procedures for portfolio behavior and performance, in contrasts with possible testing pitfalls.

Return and volatility transmission between gold and stock sectors: Application of portfolio management and hedging effectiveness

The paper investigates the first and second orders moment transmission between gold and Indian industrial sectors with an application of portfolio design and hedging effectiveness using generalised VAR-ADCC-BVGARCH model. Our findings indicate unidirectional significant return spillover from gold to stock sectors. The negative values of estimated time varying conditional correlations are mainly observed during periods of market turbulence and crisis indicating the scope of portfolio diversification and hedging during these periods. We also estimate optimal weights, hedge ratios, and hedging effectiveness for the stock-gold portfolios. Our findings suggest that stock-gold portfolio provides better diversification benefits than stock portfolios.

Volatility Threshold Dynamic Conditional Correlations: An International Analysis

This paper proposes a modeling framework for the study of changes in cross-market comovement conditional on volatility regimes. Methodologically, we extend the Dynamic Conditional Correlation multivariate GARCH model to allow the dynamics of correlations to depend on asset variances through a threshold structure. The empirical application of our model to a sample of international stock markets in 1994-2011 indicates that the periods of market turbulence are associated with an increase in cross-market comovement. The modeling framework proposed in the paper represents a useful tool for the study of market contagion.

Volatility Threshold Dynamic Conditional Correlations: An International Analysis

This article proposes a modeling framework for the study of changes in cross-market comovement conditional on volatility regimes. Methodologically, we extend the Dynamic Conditional Correlation multivariate GARCH model to allow the dynamics of correlations to depend on asset variances through a threshold structure. The empirical application of our model to a sample of international stock markets in 1994–2011 indicates that the periods of market turbulence are associated with an increase in cross-market comovement. The modeling framework proposed in the article represents a useful tool for the study of market contagion.

Investment and uncertainty in the international oil and gas industry

The standard theory of irreversible investments and real options suggests a negative relation between investment and uncertainty. Richer models with compound option structures open for a positive relationship. This paper presents a micro-econometric study of corporate investment and uncertainty in a period of market turbulence and restructuring in the international oil and gas industry. Based on data for 115 companies over the period 1992–2005, we estimate four different specifications of the q model of investment, with robust results for the uncertainty variables. The estimated models suggest that macroeconomic uncertainty creates a bottleneck for oil and gas investment and production, whereas industry-specific uncertainty has a stimulating effect.

Infrastructure developments for agile virtual enterprises

Agility is an important requirement for successful organizations in periods of market turbulence and largely unpredictable socio-economic changes. Agility in a virtual enterprise depends not only on the skills of its member enterprises but also on the characteristics of its supporting infrastructure. In this paper, various requirements and approaches for agility support during the full VE life cycle are presented and topics requiring further research are identified. Finally, two practical application scenarios are introduced and learned lessons presented.

Is the international propagation of financial shocks non-linear?: Evidence from the ERM

This paper tests for the presence of non-linearities in the propagation of devaluation expectations among the countries that were members of the Exchange Rate Mechanism of the EMS. We show that whenever it is possible to estimate a model for financial interdependence, a full-information technique to detect such non-linearities is more efficient than the limited-information estimator proposed, in a similar context, by Rigobon (2000). This happens, in particular, when the periods of market turbulence are relatively short. Our evidence suggests that non-linearities in the propagation of devaluation expectations were a general phenomenon in the ERM. Normally the non-linearity amounts to a stronger effect in the same direction, but sometimes, as in the Dutch case, it implies a significant effect in the opposite direction: evidence of flight-to-quality.

Synthetic Trades and Calendar Day Patterns The Case of the Dollar/Sterling Markets

AbstractSignificant day of the week patterns are shown to exist in the dollar/sterling market. These patterns are associated with the returns to synthetic and actual forward trades as well as to spot trades. These trading strategies, geared to buying or selling sterling, reflect different timing, if not valuation, considerations on the part of traders. Nevertheless, pronounced calendar patterns are observed on Wednesdays for all the trading strategies evaluated. This is attributable to significantly different risks on Wednesdays. The observed end‐of‐the‐week patterns in forward returns persist and reinforce the returns at the start of the next week of trading. Furthermore, the overall returns to forward speculation on Fridays and Mondays are of opposite sign. Our results on calendar day patterns are thus supported by both parametric and non‐parametric tests. We provide evidence that the frequency of synthetic trading opportunities is inversely related to maturity. We also find that the period of market turbulence analyzed did not trigger abnormal opportunities for covered interest arbitrage.