Return Dynamics Research Articles

Modeling Ordinal First Return Dynamics from Time Series

We examine the family of ordinal First Return Maps (FRMs) of a dynamical system time series to model the system’s dynamics. We consider two distinct modeling approaches using the same modeling method and compare their outcomes to ascertain the prerequisites for each to function and which approach is more effective in different circumstances. The first approach involves short-term modeling of the entire flow, followed by identifying the ordinal sections on the predicted flow. The second approach relies on locating the ordinal sections within the reference time series and using the data from each section independently to build a model that can produce a long-term prediction for each section’s FRM. We demonstrate the effectiveness of these approaches on two well-known dynamical systems, the Lorenz and Rössler equations, emulating data deficiency by selecting a low sampling rate. We consider the effect of downsampling on the shape of the FRMs to choose suitable ordinal sequences to use in creating Poincaré sections from the time series. Following that, we propose a dissimilarity measure between the reference and predicted FRMs to assess the model quality. We then compare the reference and predicted FRMs using the introduced dissimilarity criterion to determine which modeling approach performs better in each situation. Ultimately, we classify the FRMs situation into continuous and piecewise continuous scenarios. Based on our observations from the Lorenz and Rössler dynamical systems, we found that when the system’s FRMs are piecewise continuous, it is more effective to model the entire flow. However, when the FRMs are continuous, our choice of modeling approach depends on the criterion that the predicted FRM is more similar to the reference.

Corporate ESG indices and stability during periods of deep concerns in financial markets

This study analyzes the dynamics of the high ESG-score companies' returns reflected in socially responsible indices selected from the FTSE4Good family across the European market during the COVID-19 Pandemic and war in Ukraine. The dynamics of these changes were assessed through the conditional variance derived from Markov Switching ARCH-type models with two regimes. The findings indicate that the effect of market disturbances on the high ESG-score companies from the FTSE4Good Europe index was slightly less persistent than the broad market. Meanwhile, the high ESG-score companies from the FTSE4Good UK index demonstrated similar behavior to their conventional peer, which may be attributed to the composition of the respective index.

Dynamic correlation among renewable energy, technology, and carbon markets: Evidence from a novel nonparametric time-frequency approach

In addressing the challenges of energy security and climate change, ongoing efforts involve the development of innovative technologies to support the shift from conventional to clean energy sources. Concurrently, the establishment of carbon markets aims to facilitate a decrease in carbon emissions by enabling the trade of carbon credits. This study adds to the energy-climate discussion by examining the evolving dynamics of returns on investments in renewable energy, technology, and carbon markets. Specifically, the time-varying correlation among renewable energy, technology, and carbon markets in the European Union is examined from September 18, 2017 to December 14, 2022. The techniques employed for empirical analyses are the nonparametric time-frequency, Benjamini-Hochberg, and Benjamini-Yekutieli correlation techniques, as well as the time-varying Granger causality. Highlights from the results are as follows. Renewable energy, technology, and carbon markets exhibit time and frequency variations in their relationships, with significant correlations notably present during periods marked by major shifts in energy policy. Secondly, renewable energy and carbon markets exhibit very weak or even absent correlation, indicating their suitability for portfolio diversification. Furthermore, investors could benefit significantly by including both technology and carbon markets in their portfolios to reduce overall risk. Several additional recommendations are also provided in this study.

The influence of European MiCa regulation on cryptocurrencies

This research investigates the relationship between impending regulatory measures, specifically the introduction of European MiCa regulations, and their influence on cryptocurrency markets. Analysing stock market responses, we highlight significant shifts in liquidity, variance, and return dynamics post-MiCa-related announcements. Specifically, such announcements are associated with significant negative cryptocurrency returns and elevated liquidity. The findings reveal defined heterogeneity across different cryptocurrency sub-classes, each uniquely affected by its inherent attributes and susceptibility to regulatory changes. This work offers insights into the nature of market responses to regulatory intervention, providing an invaluable perspective on the equilibrium between cryptocurrency market behaviour and the evolving European regulatory landscape.

Capital Investment, Equity Returns, and Aggregate Dynamics in Oligopolistic Production Economies

Abstract We analyze effects of tacit collusion in a dynamic general equilibrium model of oligopolistic sectors with capital investment and real frictions. Through their effects on equilibrium- and off- equilibrium stock prices, fundamental shocks affect incentives for defection from tacit collusion, amplifying the interaction between the real economy and financial markets as well as firms‘ risk exposure. The model implies ambiguous relationship between industry concentration and equity returns depending on operating leverage, which helps reconcile conflicting evidence in the literature. We find quantitative and empirical support for novel theoretical predictions regarding the effects of concentration on returns and investment. (JEL G12, G31, D43)

Dynamic spillover effects and interconnectedness of DeFi assets, commodities, and Islamic stock markets during crises

Decentralized Finance (DeFi) assets, commodities, and Islamic stock market cointegration are affected by technological innovations, market dynamics, investor behavior, and crises. This study investigates the dynamics of returns and volatility for three DeFi assets, six commodities, and three Islamic stock markets from December 2019, to March, 2023, and identifies higher spillover effects during crises. Links among the Cross-DeFi, commodity, and Islamic markets significantly influence returns and volatility during crises. Notably, the commodities index emerged as a pivotal and substantial transmitter of risk during the Russian-Ukraine war crisis, with Emerging Markets (EM) being a key recipient. However, during the COVID-19 pandemic, livestock indices assume the role of prominent risk-return spillover receivers. The findings indicate robust returns and volatility interconnected between DeFi assets and Islamic markets with a moderate level of connectivity among commodity groups. WDI, ACWI, and EM explained 75 % of the variance observed during crisis episodes. This study formulates strategic portfolio management within and between connectedness among return volatilities by highlighting the stability of DeFi assets, the diversification potential in commodities, and a balanced option in Islamic markets. Our study provides a deep and insightful understanding of the stakeholders across markets during crises.

Non‐causal and non‐invertible ARMA models: Identification, estimation and application in equity portfolios

The mixed causal‐non‐causal invertible‐non‐invertible autoregressive moving‐average (MARMA) models have the advantage of incorporating roots inside the unit circle, thus adjusting the dynamics of financial returns that depend on future expectations. This article introduces new techniques for estimating, identifying and simulating MARMA models. Although the estimation of the parameters is done using second‐order moments, the identification relies on the existence of high‐order dynamics, captured in the high‐order spectral densities and the correlation of the squared residuals. A comprehensive Monte Carlo study demonstrated the robust performance of our estimation and identification methods. We propose an empirical application to 24 portfolios from emerging markets based on the factors: size, book‐to‐market, profitability, investment and momentum. All portfolios exhibited forward‐looking behavior, showing significant non‐causal and non‐invertible dynamics. Moreover, we found the residuals to be uncorrelated and independent, with no trace of conditional volatility.

Conditional volatility targeting strategy considering jump effects: Evidence from sustainable ESG equity index

This paper considers the sustainable ESG equity index in the proposed conditional volatility targeting strategy. The research first detects jump risk using a jump test and then extends Bongaerts et al. (2020) by addressing jump risk and employing different volatility models to project volatilities under the conditional volatility targeting strategy. To capture consideration of the fact that index return dynamics, we propose an ARMA-GARCH jump model that can capture the characteristics of jump persistence, autocorrelation, and volatility clustering according to the return of the sustainable equity index. Our numerical analyses reveal that the portfolio allocation using a sustainable equity index to predict volatility, combined with a conditional volatility target strategy, can achieve higher performance. Furthermore, the proposed ARMA-GARCH jump model can enhance the performance with conditional volatility targeting strategy.

Dynamic mean-variance portfolio selection under factor models

Utilizing insights from financial literature and empirical financial data, we introduce a comprehensive system of factor models designed to capture both return and risk dynamics. Our focus extends to addressing the multi-period mean-variance portfolio selection challenge within the framework of these proposed factor models. Through rigorous analysis, we formulate a semi-analytical optimal portfolio policy, characterized by a linear relationship with the current wealth level. The coefficients of this optimal policy are intricately linked to a specific stochastic process known as the future investment opportunity (FIO), reflecting the investor's anticipation of future investment prospects. Furthermore, empirical examination within the U.S. market context underscores the efficacy of our approach. By incorporating the factor models for return and risk, our optimal portfolio policy exhibits superior out-of-sample Sharpe ratio compared to benchmark policies.

Blood donor return behavior in South Africa and the United States before and during the COVID-19 pandemic.

Studies preceding the COVID-19 pandemic found that slower time-to-return was associated with first-time, deferred, and mobile drive blood donors. How donor return dynamics changed during the COVID-19 pandemic is not well understood. We analyzed visits by whole blood donors from 2017 to 2022 in South Africa (SA) and the United States (US) stratified by mobile and fixed environment, first-time and repeat donor status, and pre-COVID19 (before March 2020) and intra-COVID19. We used Kaplan-Meier curves to characterize time-to-return, cumulative incidence functions to analyze switching between donation environments, and Cox proportional hazards models to analyze factors influencing time-to-return. Overall time-to-return was shorter in SA. Pre-COVID19, the proportion of donors returning within a year of becoming eligible was lower for deferred donors in both countries regardless of donation environment and deferral type. Intra-COVID19, the gap between deferred and non-deferred donors widened in the US but narrowed in SA, where efforts to schedule return visits from deferred donors were intensified, particularly for non-hemoglobin-related deferrals. Intra-COVID19, the proportion of donors returning within a year in SA was higher for deferred first-time donors (>81%) than for successful first-time donors (80% at fixed sites; 69% at mobile drives). The pandemic complicated efforts to recruit new donors and schedule returning visits after completed donations. Concerted efforts to improve time-to-return for deferred donors helped mitigate donation loss in SA during the public health emergency.

The nexus between mineral, renewable commodities, and regional stock sectors during health and military crises

We examine the return and volatility spillover dynamics and their portfolio implications among regional stock super-sectors, mineral (Energy, Industrial, and Precious Metals), and renewable (Agricultural and Livestock) commodities. To this end, we apply the time-varying parameter-vector autoregressive (TVP-VAR) model to analyze the intersectoral-commodity linkages separately in four major regions (Europe, Eastern Europe, Asia Pacific, and North America). The empirical results show that stock market sectors dominate the spillover transmission mechanism, while commodities emerge as net recipients of shocks. Moreover, we find heterogeneous connectedness patterns between stock sectors and commodities, predominantly running from the Energy and Basic Resources stocks to the Energy and Industrial Metals commodities. Thus, energy commodities play a more vital role as a spreader of volatility shocks amid the COVID-19 pandemic and the wake of the Russia-Ukraine war. The optimal portfolio weights reflect two episodes of “flight to safety” towards all commodities except Energy, triggered by the pandemic and the war, as investors decrease their fund allocation to stocks in favor of commodities.

Description of regression models for predicting the dynamics of pink salmon returns in the Kamchatka region based on climate-oceanological and population-genetic data

Several regression models for predicting returns of pink salmon in the Kamchatka region are presented. The data for 1990–2023 were analyzed. Among available climatic and oceanological indices, the most suitable for using as predictors for forecasting of pink salmon returns were the Pacific Decadal Oscillation (PDO) index, Western Pacific Cyclonic Index (WP), Arctic Oscillation (AO) index, and the sea surface temperature anomaly in the North Pacific. Multi-dimensional models of the «stock–recruitment» type were built on identified statistical patterns, which allowed to estimate potential abundance of the pink salmon returns to northeastern and western Kamchatka. Besides, methods for predicting the abundance of pink salmon returns on the data of fish counting in the sea are considered, using the materials of TINRO trawl surveys conducted in the Bering and Okhotsk Seas in the fall seasons of 2012–2023. To determine the abundance of pink salmon originated from West Kamchatka, genetic identification of regional composition of juveniles in mixed trawl catches was used. All tested methods have a high level of determination, but simpler regressive models are more prospective for practical forecasting of general trend in dynamics of pink salmon stocks in the Kamchatka region due to very weak generalization ability of more complicated models.

Enhancing Forecasting Accuracy in Commodity and Financial Markets: Insights from GARCH and SVR Models

The aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&P500. It provides a comprehensive overview of each model’s efficacy in capturing volatility clustering, asymmetry, and long-term memory effects in asset returns. By employing models like sGARCH, eGARCH, gjrGARCH, and FIGARCH, the research offers a nuanced understanding of volatility evolution and its impact on asset returns. Using the Skewed Generalized Error Distribution (SGED) in model optimization shows how important it is to understand asymmetry and fat-tailedness in return distributions, which are common in financial data. Key findings include the sGARCH model being the preferred choice for Gold Futures due to its lower AIC value and favorable parameter estimates, indicating significant volatility clustering and a slight positive skewness in return distribution. For Cocoa Futures, the FIGARCH model demonstrates superior performance in capturing long memory effects, as evidenced by its higher log-likelihood value and lower AIC value. For the S&P500 Index, the eGARCH model stands out for its ability to capture asymmetry in volatility responses, showing superior performance in both log-likelihood and AIC values. Overall, identifying superior modeling approaches like the FIGARCH model for long memory effects can enhance risk management strategies by providing more accurate estimates of Value-at-Risk (VaR) and Expected Shortfall (ES). Additionally, the out-of-sample evaluation reveals that Support Vector Regression (SVR) outperforms traditional GARCH models for short-term forecasting horizons, indicating its potential as an alternative forecasting tool in financial markets. These findings underscore the importance of selecting appropriate modeling techniques tailored to specific asset classes and forecasting horizons. Furthermore, the study highlights the potential of advanced techniques like SVR in enhancing forecasting accuracy, thus offering valuable implications for portfolio management and risk assessment in financial markets.

Interlinkage of an Emerging Market with Financial Assets: Evidence from Three Well-Known Econometric Models

This research seeks to offer a thorough investigation of the return and volatility spillover dynamics between DSEX and five financial assets from Gold, currency rates, energy, and stock indexes. Previous studies only used one econometric model to estimate the results, but this work used three well-known models for cross validation and also used portfolio weights and an optimal hedge to control risk. The DCC-GARCH technique is used to examine volatility spillovers, and the Quantile VAR and TVP-VAR frameworks are used to examine return spillovers. Surprisingly, three separate methodologies all provided equal results and arrived at the same conclusion. This study's findings corroborate the premise that these six components frequently exhibit considerable co-movement in futures trading. This paper illustrates that investors can greatly benefit by altering their portfolios based on the dynamic weights and hedge ratios, even if there is a trade-off amongst the degree of risk mitigation and portfolio revenue.

Correction to: “The effect of COVID‐19 pandemic on global stock markets: Return, volatility, and bad state probability dynamics”

Correction to: “The effect of <scp>COVID‐19</scp> pandemic on global stock markets: Return, volatility, and bad state probability dynamics”

The Effect of Sectoral Diversification on Return and Risk in Portfolio Management: An Application in Borsa Istanbul

This paper investigates sectoral diversification within Borsa Istanbul, aiming to elucidate its impact on portfolio risk and return dynamics. Spanning the timeframe from 2020 to 2022, the study meticulously analyzes stocks from pivotal sectors, including banking, energy, and iron and steel. Employing a robust methodology, the research harnesses Monte Carlo simulations to generate many hypothetical portfolios, subsequently evaluating them on the Efficient Frontier to identify optimal risk-return trade-offs. Key performance metrics, such as the Sharpe Ratio, Sortino Ratio, and Maximum Drawdown, further enrich the analysis, providing a granular view of portfolio behaviors. The significance of this study lies in its bridging of theoretical constructs of diversification with the tangible realities of an emerging market like Borsa Istanbul. Our main findings underscore the potential benefits of sectoral diversification while highlighting the complexities inherent in portfolio construction. The insights gleaned offer valuable guidance for investors, emphasizing the delicate balance between risk mitigation and return optimization in a diversified portfolio.

Geopolitical risk and the dynamics of REITs returns

The aim of this study is to analyze the relative importance of geopolitical risk (GPR), as introduced by Caldara and Iacoviello (2022), on the dynamics of U.S. REITs returns. Using an extended conditional version of Fama and French (1993)’s capital asset pricing model, we highlight the role played by GPR and its two components, geopolitical acts (GPA) and geopolitical threats (GPT), on the expected returns of securitized real estate. Our robust results report the level and the significant of the geopolitical risk metrics on the decomposition of REITs returns grouped into different portfolios (from CRSP/Ziman series). We shed light on the link between the characteristics of REITs and the relative importance of geopolitical risk during the last decades.

Capital Structure's Role in Shaping Stock Return Dynamics

This study examines the intricate relationships among capital structure, current ratio, return on assets (ROA), long-term debt-to-equity ratio, and stock returns within the property and real estate sector, utilizing a sample of 16 companies. Employing Structural Equation Modeling (SEM), capital structure is introduced as a mediating variable to elucidate its role in the association between the investigated financial metrics and stock returns. Results indicate that neither the current ratio nor ROA significantly influences stock returns, while the long-term debt-to-equity ratio negatively impacts capital structure. Surprisingly, the current ratio exhibits a positive effect on stock returns, while the long-term debt-to-equity ratio shows no significant impact. Notably, capital structure emerges as a positive determinant of stock returns. This study provides a novel perspective by introducing capital structure as a mediator, contributing to the existing body of knowledge. Practical implications suggest that property and real estate investors should consider financial ratios alongside external factors like exchange rates and economic conditions when making investment decisions. However, limitations associated with the study's focus on IDX-listed companies in this sector are acknowledged, necessitating careful interpretation of the findings.

Portfolio Analysis of Financial Institutions in Nepal

In this paper we empirically investigated risk return dynamics of the financial institutions that were selected from the Nepal Stock Exchange for the period of 2016-2022. The objective of this study is to analyze the portfolio of different FIs based on the return and risk parameters using ex-post returns data. This study employed financial and statistical tools to draw the conclusion. The beta values show that commercial banks’ share prices are more volatile than the prices of development banks and finance companies. The alpha coefficients reveal that share prices of all commercial banks and development banks are found overvalued and couple of finance companies is undervalued. The results of correlation coefficient offer a scope of diversification in the Nepalese stock market.

Overnight returns, daytime reversals, and anchoring bias

ABSTRACT This study explores the impact of anchoring bias on the daily tug-of-war in financial markets, specifically examining the reversal dynamics between overnight and daytime periods in relation to the 52-week high price. After analysing the interplay between anchoring bias and the abnormal intensity of the tug-of-war (AB_NR), our findings reveal a significant AB_NR premium for stocks far from their 52-week high prices. This sheds light on the important role of psychological barriers faced by noise investors during overnight periods in shaping return dynamics.