Implications For Portfolio Management Research Articles

When the tide wanes: A study of post systemic collapse portfolio management

This study applies the Black–Litterman model to portfolio management in a post-crisis scenario, where standard parametric models often fail due to market irrationality and investor overreactions. The research focuses on the performance of surviving firms, identifying that over-performing stocks tend to outperform in the medium- to long-term, while under-performing stocks exhibit stronger short-term returns as the market corrects its initial overreaction. The study specifically adjusts views within the Black–Litterman framework using only the top and bottom quartiles of stocks, which experience the most significant shifts during a crisis. By controlling for firm size and book-to-market ratios, consistent with the Fama-French three-factor model, the research demonstrates that adjusting portfolio weights with the Black–Litterman model can achieve substantially higher returns with lower downside risk during recovery periods. These findings have significant implications for portfolio managers seeking to optimize returns in volatile, post-crisis markets.

Quantile-frequency connectedness among climate change stocks: The roles of climate change attention and global uncertainties

Concerns regarding climate change and the environment have heightened interest in climate-conscious investing, necessitating a deeper understanding of climate-friendly financial products. This study investigates climate change stock market connectedness and spillover effects, focusing on quantile-frequency dependency. The study utilizes a novel quantile-frequency connectedness with seven MSCI climate change market indices and daily data from January 2014 to December 2022. The empirical analysis reveals that climate change stocks are highly interconnected across various market conditions, including bearish and bullish periods. Spillover effects have amplified during increased stock market turbulence, regardless of market directions. Short-term spillover effects have a stronger impact than long-term effects, regardless of market conditions. Climate change stock markets in Japan and the Asia Pacific consistently experience net shocks, while the United States, Europe, Emerging Markets (EM), North America, and the European Economic and Monetary Union (EMU) exhibit variations based on market conditions and timeframes. The study identifies events including the 2015 Paris Agreement, the 2016 US withdrawal from the Paris Agreement, the COVID-19 pandemic, the 2021 US rejoining of the Paris Agreement, and the 2022 Russian-Ukraine War that have influenced the dynamic connectedness and spillover of climate change markets. The analysis also reveals that increased investor attention to climate change leads to higher interconnectedness among climate change stock markets across various market scenarios. Moreover, global financial stress, economic policy uncertainty, geopolitical risk, global carbon emission futures, oil market uncertainty, and stock market uncertainty significantly impact the interdependence of climate change markets. The findings have implications for portfolio managers, emphasizing the need for hedging techniques to mitigate risks in climate change markets.

Bitcoin, Fintech stocks and Asian Pacific equity markets: a dependence analysis with implications for portfolio management

Bitcoin, Fintech stocks and Asian Pacific equity markets: a dependence analysis with implications for portfolio management

The Dissimilar Market Volatility in Neighboring Financial Markets: An Empirical Study Using A Multivariate Garch Model

ABSTRACT: Conventional knowledge of market volatility represents that two proximate financial markets operated in the same country would display a similar pattern in their time-varying volatility. In this paper, we propose that the heterogeneity in financial policy orientation of stock markets creates significant inter-market differences in terms of the volatility of stock returns, even if they are culturally and geographically linked to each other. For the empirical investigation of our proposition, the volatility data from two major stock exchanges in the Middle East, the Abu Dhabi Securities Exchange (ADX) and the Dubai Financial Market (DFM) are used. Both the ADX and DFM stock exchanges operate in the same country, the United Arab Emirates (UAE). The ADX is based in Abu Dhabi, the administrative capital, while the DFM is based in Dubai, the commercial and business hub of the UAE. They were both established in the year 2000, and their operational headquarters are located about 150 km from each other. This paper uses a multivariate GARCH model, in particular a diagonal VECH GARCH (1, 1) model to estimate volatility measures for stock returns between ADX-listed and DFM-listed stocks. The paper finds that the stock returns are significantly less volatile in the ADX compared to those in the DFM suggesting that the volatility transmission is incomplete between these two neighboring financial markets. This difference in volatility can potentially be attributed to the relatively conservative financial policies adopted by the oil-rich emirate of Abu Dhabi compared to the more market-oriented economic policies of Dubai, the country’s financial and commercial hub. Our findings have considerable implications for portfolio managers in ascertaining risk premiums when allocating investments across the two stock exchanges. For instance, this disparity in volatility should be taken into account by investors and policymakers when designing risk management strategies because it suggests that investors in the DFM may be exposed to higher levels of risk and uncertainty compared to their counterparts in the ADX. Investors operating in the DFM may require more robust risk management strategies, such as asset diversification and hedging, compared to their ASX counterparts.

Contagion effect between fuel fossil energies and agricultural commodity markets and portfolio management implications

This study investigates the dynamic spillover effects between fossil fuels (Brent oil and natural gas) and major agricultural markets (maize, soybeans, and wheat) using a time-varying parameter vector autoregression (TVP-VAR) model. Additionally, we quantify the cumulative return of the investment portfolio, optimal weights, time-varying risk coverage, and hedging effectiveness of bivariate commodity portfolios using the Minimum Variance Portfolio (MVP), Minimum Correlation Portfolio (MCP), Minimum Connectedness Portfolio (MCoP) under bearish, tranquil, and bullish market conditions. The results show that maize and natural gas are the main net receivers of spillovers, whereas wheat and Brent oil are the main net transmitters of shocks to the network, respectively. Our findings of optimal weights in multivariate portfolio suggest more weights for agri-commodities against fossil fuels in all three modes of market. In addition, the optimal weights of bivariate portfolio analysis show that the most optimal portfolio which has the highest HE value is related to natural gas pairs with agri-commodities. Moreover, depending on the status of the market; in an average and bearish modes, the highest optimal weight is related to maize/natural gas. However, in the bullish market, the highest optimal weights are related to natural gas/Brent oil pair. Furthermore, the results of optimal bivariate hedge ratios show that natural gas/wheat (wheat/maize and vice versa) portfolio is the cheapest (the most expensive) investment strategy in all modes of market and based on MVP and MCoP approaches.

Risk-Based Asset Allocation in Factor Investing: Exploring the Inverse Factor Volatility Strategy

This study evaluates the effectiveness of the Inverse Factor Volatility strategy within the context of factor investing, comparing its performance to the conventional Risk Parity strategy. Using quantitative techniques, including portfolio construction and performance metrics analysis, this research employs data from five individual equities spanning the years 2000 to 2022. The methodology involves constructing portfolios based on Inverse Factor Volatility and Risk Parity principles and analyzing performance metrics, including mean returns, risk-adjusted returns, and drawdowns. The findings indicate that, compared to Risk Parity, Inverse Factor Volatility offers superior drawdowns, risk-adjusted returns, and mean returns. These results suggest that Inverse Factor Volatility may be a more effective strategy for portfolio management and could represent an advancement over traditional factor investing methods. The conclusions of this study hold significant implications for portfolio managers seeking to optimize their investment strategies.

Is the Russian Green Bond Market Strong Enough to Hedge in the Crisis Times?

The scope of this research has two facets. First, we study the spillover effects between the Russian green bonds and the leading capital market’s ‘indexes before and after the February 2022 events. Second, the identified level of asset connectedness permits to identify portfolio management implications for the analyzed assets. To reveal the spillover effects, we applied the vector autoregressive model and created a synthetic index to capture the dynamics of the green bonds market which included 14 green bond issues between 2021 and 2023 in Russia. We analyze oil & gas, electrical utilities, metals & extraction, chemical sectors collectively referred to as “pollution intensive indexes”. The paper contributes by discoveringthat the total connectedness index (TCI) between Russian green bond market and pollution intensive indexes changed over time and increased after the outbreak of the conflict. Additionally, the paper is novel on revealing the relationship between low hedging effectiveness and hedging ratio of green bond and energy, metals and extraction, sustainability and oil and gas indexes which indicate no need for hedging after February events. The optimal bivariate portfolio weights analysis shows that Russian green bonds market is an outstanding instrument for assets portfolio management during geopolitical conflict. These findings have implications for the government and other stakeholders to manage both the contagion and climate risks during the military conflict.

Tail risk spillovers between Islamic sectoral equities and bond markets: a time-frequency domain approach

ABSTRACT This study examines the tail risk spillover between Islamic sectoral stock indices and country specific investable Islamic bonds in time and frequency domain and provides useful implications for portfolio management. For the analyses, Conditional Autoregressive ValueatRisk (CAViaR), Quantile Connectedness, and DCCGARCH t-Copula models are estimated utilizing daily data from 15 countries. The findings show that the connectedness and spillover of risks are much stronger at tails than at median, and in the short-term than in long-term. Whereas median risk connectedness surges during COVID-19 pandemic, the connectedness between tail risks is usually elevated even before the COVID-19 pandemic with industrial sector being consistently a significant net transmitter of shocks. Moreover, all the sectoral stock market indices are significant and consistent transmitter of left tail shocks indicating a much stronger role of sectoral stock markets in transmitting large negative shocks. A heightened connectedness is also observed during Russia-Ukraine war mainly in the short-term frequency. The portfolio analysis shows that long positions in sectoral stocks can usually be hedged by taking short positions in Islamic bonds. Hedging effectiveness and optimal portfolio weights are also calculated to provide market participants with further information.

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.

Risk dynamics in energy transition: Evaluating downside risks and interconnectedness in fossil fuel and renewable energy markets

Amidst rising concerns over climate change, the global shift from oil-powered vehicles to lithium-powered electric vehicles marks a critical pivot in the energy sector. This transition holds profound implications for the traditional dominance of oil in the energy market. This study aims to investigate the risk dynamics between the fossil fuel sector, represented by the crude oil, and the renewable energy sector, symbolized by lithium, focusing on the risk spillover and interconnectedness. The study employs a two-step approach that combines the conditional autoregressive value-at-risk (CAViaR) and time-varying parameters vector-autoregression (TVP-VAR). The study spans from January 2011 to August 2023, focusing on extreme market events like the COVID-19 pandemic and the Russia-Ukraine conflict. Findings reveal a strong tail-risk connectedness between the lithium stocks of the same country, and during extreme market events such as the COVID-19 pandemic and the Russia-Ukraine conflict, there is a strong transmission of tail risk across the lithium stocks of different countries. Moreover, a strong bidirectional tail-risk connectedness is observed between crude oil and lithium stocks. The study has important implications for portfolio managers, fund managers and investors with respect to portfolio optimization and asset allocation. Also, the findings have important implications for financial risk management with respect to energy transition from fossil fuels to renewable energy.

Extreme downside risk connectedness and portfolio hedging among the G10 currencies

This study investigates the frequency connectedness among foreign exchange markets of G10 countries, focusing on tail risk and its implications for portfolio management. To do so, we use a novel framework that combines the Conditional Autoregressive Value-at-Risk (CAViaR) model with the novel time-varying frequency and quantile connectedness method developed by Chatziantoniou et al. (2022) based on Baruník and Křehlík (2018) and Ando et al. (2018) approach. A key value of this paper to the literature is the provision of fresh empirical evidence on the extreme downside linkages among the markets examined. From the average connectedness measures, the top shock transmitters within the network were EUR, NOK, AUD, SEK, and NZD, while the main shock receivers emerged to be JPY and CHF, followed by CAD and GBP. We note that events in the major funding markets (the Eurozone, Japan) have a higher impact on the participants in these same markets than in relatively small markets (New Zealand, Norway). From the dynamic connectedness results, the magnitude of connectedness for the entire sample period increased during the COVID-19 era, compared to the magnitude before the COVID-19 outbreak. The cumulative spillover also reveals that USDNOK is the vastest net transmitter of spillovers to other markets, including SEK, CHF, and AUD. However, the EUR is the largest net beneficiary followed by JPY and CAD. Findings from the time-varying extreme downside analysis suggest that throughout the period, SEK and NOK are the other currencies' strongest and most frequent net spillover shock emitters for the short-, medium-, and long-term dynamics. Currency portfolio implications are discussed.

The Indirect Diversification Benefits of Investing in Japanese Firms: An Alternative Perspective

AbstractThis paper examines the role of firm-level multinationality in equity portfolio diversification for Japanese firms from 1998 to 2015. We use a unique multinationality dataset for constituents of the Nikkei 225 based on two measures of sales and subsidiaries. We employ an extended version of the traditional Capital Asset Pricing Model (CAPM) to analyse the exposure of firm returns to various geographical regions. There is evidence that firms are not influenced by the geographic regions where they report operations. The results also indicate that there are benefits from investing in Japanese multinationals but these benefits do not increase with increasing multinationality. A new category of firms is identified that may be beneficial to investors—firms that are influenced by a geographical region where they do not report sales or subsidiaries. This finding has far reaching implications for portfolio management. Investors must do more than analyse the international location of firm operations. They must analyse the geographical influences on firm returns. Existing studies fail to distinguish between these two criteria, assuming them to be the same. We find evidence to the contrary.

Intention to Use Robo Advisory Services in Malaysia

In Malaysia, robo-advisory services have become increasingly popular in the financial industry. This study examines the factors that determine the intention to use robo-advisory services in Malaysia, including the performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and trust. The target respondents of this study are individuals from the M40 income group in Malaysia. A total of 400 responses was collected and analyzed through Partial Least Squares Structural Equation Modelling (PLS-SEM) using SmartPLS software. The findings demonstrated that performance expectancy, social influence, hedonic motivation, price value, and trust are significant in affecting the behavioral intention to use robo-advisory services in Malaysia, except for effort expectancy and facilitating conditions, which showed an insignificant result. Moreover, this study would contribute to the understanding of the emergence of robo-advisory services in Malaysia and provide practical and theoretical implications for portfolio managers, robo-advisory firms, and researchers to aid in the future development of robo-advisory services and the financial industry in Malaysia.

From the pandemic to the Russia–Ukraine crisis: Dynamic behavior of connectedness between financial markets and implications for portfolio management

This study assesses the time-varying connectedness and portfolio implications among equity markets, commodities, and cryptocurrencies. To this end, we employ the DCC-GARCH model and time-frequency framework on daily data. The sample covers a period spanning from January 2018 to October 2023. The results confirm the existence of financial contagion evidence during the pandemic outbreak and the Russian invasion of Ukraine among the equity markets and alternative assets. Furthermore, from the perspective of portfolio management, we find that gold can be a suitable asset for diversifying specific risks, followed by soft commodities and Bitcoin during tranquil periods. We demonstrate that gold serves as a good long-term safe investment, whereas gold and Bitcoin lose their safe-haven properties in the short and mid term. Conversely, oil is the least effective diversification and safe-haven asset for equities. However, the hedging analysis indicates an increase in the hedge ratio during crisis periods across all pairs. The hedging performance of each asset is modest and varies due to critical market circumstances during the pandemic and the war. These findings suggest that investors should frequently rebalance the portfolio structure rather than adopting a static method such as buy and hold.

Multi-scale dependence and risk contagion among international financial markets based on VMD-Vine copula-CoVaR

ABSTRACT Considering the frequency domain and nonlinear characteristics of financial risks, we propose a VMD-Vine copula-CoVaR framework to study the dependence structures and risk spillovers among international financial markets at different time scales. Furthermore, a mean-variance optimization technique has been applied to evaluate the performance of the optimal cross-market portfolios. The empirical results show that: (1) The R-Vine copula is superior to the C- and D-Vines; (2) Compared with the Asian stock markets, the European stock markets are more correlated with each other. The correlations between the markets at the long-term scale are greater than those at the short- and medium-term ones; (3) The optimal pair copulas are combined with the CoVaR to estimate the systemic spillover risks among the markets. The downside and upside spillover risks across the markets are not always symmetric. Moreover, the ΔCoVaRs at the medium-term scale are the highest and the lowest at the short-term one; and (4) The optimal cross-market portfolio constructed by our framework perform better than the benchmarks by annualized return, Sharpe ratio, and maximal drawdown. The findings have direct implications for portfolio managers and investors to prevent extreme risks and take better international investment decisions based on the knowledge of optimum portfolio.

Dynamic correlations between Bitcoin, carbon emission, oil and gold markets: New implications for portfolio management

<abstract> <p>In this paper, we aim to uncover the dynamic spillover effects of Bitcoin environmental attention (EBEA) on major asset classes: Carbon emission, crude oil and gold futures, and analyze whether the integration of Bitcoin into portfolio allocation performance. In this study, we document the properties of futures assets and empirically investigate their dynamic correlation between Bitcoin, carbon emission, oil and gold futures. Overall, it is evident that the volatility of Bitcoin, as well as other prominent returns, exhibit an asymmetric response to good and bad news. Additionally, we evaluate the hedge potential benefits of these emerging futures assets for market participants. The evidence supports the idea that the leading cryptocurrency-Bitcoin can be a suitable hedge instrument after the COVID-19 pandemic outbreak. More importantly, our analysis of the portfolio's performance shows that carbon emission futures are diversification benefit products in most of the considered cases. Notably, incorporating carbon futures into portfolios may attract new investors to carbon markets for double goals of risk diversification. These findings also provide insightful evidence to investors, crypto traders, and portfolio managers in terms of hedging strategy, diversification and risk aversion <sup>[<xref ref-type="bibr" rid="b19">19</xref>,<xref ref-type="bibr" rid="b20">20</xref>,<xref ref-type="bibr" rid="b21">21</xref>,<xref ref-type="bibr" rid="b22">22</xref>,<xref ref-type="bibr" rid="b23">23</xref>,<xref ref-type="bibr" rid="b24">24</xref>,<xref ref-type="bibr" rid="b25">25</xref>]</sup>.</p> </abstract>

Loss Aversion, Overconfidence of Investors and their Impact on Market Performance: Evidence from the London Stock Exchange (FTSE100)

The Capital Asset Pricing Model (CAPM) has traditionally explained the relationship between risk and expected return, assuming efficient markets and rational investors. However, empirical evidence has increasingly challenged these assumptions, leading to the emergence of behavioural finance, which examines how psychological factors impact investor behaviour and market outcomes. This study explores the effects of two key behavioural biases—loss aversion and overconfidence—on the performance of FTSE100-listed companies. Using panel data models on a sample of six companies from industrial and service sectors between January 2004 and December 2022, our research finds that investor sentiment significantly influences market performance. Specifically, overconfidence correlates positively with the stock performance of industrial firms but does not affect service sector firms. Loss aversion, however, does not show a significant impact on performance in the UK market. These findings suggest that overconfidence may have a more pronounced effect on market performance compared to loss aversion. This has implications for portfolio managers and financial regulators, as understanding these psychological biases can enhance market predictions and stability. By incorporating investor sentiment into market analysis, stakeholders can better anticipate market fluctuations and mitigate potential risk.

Another look at the asymmetric relationship between stock returns and trading volume: evidence from the Markov-switching model

PurposeThe purpose of this paper is to bridge the gap between the existing theoretical and empirical studies by examining the asymmetric return–volume relationship. Indeed, the authors aim to shed light on the return–volume linkages for French-listed small and medium-sized enterprises (SMEs) compared to blue chips across different market regimes.Design/methodology/approachThis study includes both large capitalizations included in the CAC 40 index and listed SMEs included in the Euronext Growth All Share index. The Markov-switching (MS) approach is applied to understand the asymmetric relationship between trading volume and stock returns. The study investigates also the causal impact between stock returns and trading volume using regime-dependent Granger causality tests.FindingsAsymmetric contemporaneous and lagged relationships between stock returns and trading volume are found for both large capitalizations and listed SMEs. However, the causality investigation reveals some differences between large capitalizations and SMEs. Indeed, causal relationships depend on market conditions and the size of the market.Research limitations/implicationsThis paper explains the asymmetric return–volume relationship for both large capitalizations and listed SMEs by incorporating several psychological biases, such as the disposition effect, investor overconfidence and self-attribution bias. Future research needs to deepen the analysis especially for SMEs as most of the literature focuses on large capitalizations.Practical implicationsThis empirical study has fundamental implications for portfolio management. The findings provide a deeper understanding of how trading activity impact current returns and vice versa. The authors’ results constitute an important input to build and control trading strategies.Originality/valueThis paper fills the literature gap on the asymmetric return–volume relationship across different regimes. To the best of the authors’ knowledge, the present study is the first empirical attempt to test the asymmetric return–volume relationship for listed SMEs by using an accurate MS framework.

Connectivity and spillover during crises: Highlighting the prominent and growing role of green energy

How influential are green energy instruments? We examine how green- and carbon-energy assets differ regarding transmitting and receiving shocks between normal versus crises periods. Crises include the global financial crisis and Euro debt crisis, two waves of COVID-19, and the ongoing Russia-Ukrainian war. Our empirical illustration is based on volatility impulse function (VIRF) for dynamic conditional correlation–generalised autoregressive conditional heteroskedasticity (DCC-GARCH) method using daily data from January 03, 2007–March 31, 2023, of several green and brown energy instruments and market and energy controls, we evidence asymmetric connectedness that increases during crises. For specific green instruments, volatility transmissions can be transmitting or receiving. However, green instruments stand out overall as prominent transmitters, while brown energy instruments are prominent receivers. Results are consistent with green energy vehicles impacted by macroeconomic and market states and reflecting this to investors. Results are also consistent with green and brown interconnectivity. Further network analysis provides robustness to our study results and suggests this role is evolving. The study results are significant for policy intervention during the transition to alternative energy sectors and for risk and portfolio management implications.

Systemic Risk in the Global Energy Sector: Structure, Determinants and Portfolio Management Implications

We examine the dynamics of tail dependence across returns of 105 global energy firms from 26 countries covering the regions of America, Asia Pacific and Europe. A partial correlation-based approach is used to quantify the dependence structure and level of systemic risk under relatively stable and extremely bearish and bullish market conditions. The dependence network of energy stock returns is constructed based on the novel triangulated maximally filtered graph (TMFG). The results reveal a high degree of tail dependence and role played by geographical proximity. The strongest links are found under extreme bearish market conditions. American and European energy firms are more interconnected and contribute more to systemic risk than Asian-Pacific companies. The dependence intensifies during periods of market turmoil, especially during the COVID-19 pandemic. A higher instability in the dependence structure is observed during extremely bearish market circumstances. A simple portfolio trading strategy based on the dependence ranking of energy firms outperforms a naïve equally-weighted buy-and-hold portfolio strategy.