Financial Market Returns Research Articles

New parameterization of stochastic conditional range models for financial volatility modelling

The financial market returns and daily range data often exhibit asymmetry and peak around the mean with heavier tails than normal distribution. To analyze the volatility dynamics of these financial time series using stochastic conditional range (SCR) models, the choice of conditional distribution for innovations has posed challenges. A suitably chosen distribution, which can capture stylized facts inherent in these data, particularly excess kurtosis, has been proven to be very useful. This study introduces a new parameterization for SCR models using an extended generalized inverse Gaussian distribution to incorporate the flexibility of choice of various marginal distributions to the innovations of the model. We discuss the theoretical properties of the proposed models and develop a maximum likelihood method based on efficient importance sampling to estimate the parameters of the proposed SCR models. The simulation results showed that the proposed estimation methodology performs well. Finally, the proposed models were used to analyze real datasets.

Modeling interdependence between climatic factors, commodities, and financial markets

This paper introduces a comprehensive approach to studying the impact of climate-related factors on commodity and financial markets using network analysis. We utilize a Bayesian network Vector Autoregressive model to investigate whether climate risk significantly influ-ences commodity prices and financial market returns. Our findings provide evidence of a climate effect on major commodities and global financial markets. Specifically, we identify Crude oil, Cotton, and Sugar as the commodities most affected by climate risk, with Gold demonstrating the least susceptibility. Additionally, we observe that climate-related risk on commodities is likely propagated by patterns such as PNA, NN1, and AO. In terms of financial markets, we find that stock markets in Hong Kong, India, and Spain are the most susceptible to climate risk, while Switzerland’s market appears to be the least affected. Furthermore, we document evidence that climate-related risk capable of altering financial markets is likely propagated by factors like ENP, NN1, and WH. Overall, our study underscores the intricate relationship between climate factors and market dynamics, highlighting the importance of considering climate risk in assessing market behavior and performance.

Sovereign Green Bond Market: Drivers of Yields and Liquidity

The aim of this study is to analyse and assess the yields and liquidity of sovereign green bonds in selected countries and to compare the yields between sovereign green bonds and conventional bonds. Sovereign green bonds are issued by governments to finance environmental and social projects and represent a relatively new and growing asset class. This study seeks to analyse the financial performance of sovereign green bonds by examining yields and liquidity metrics, such as bid–ask spreads. The findings of this research suggest that the yield to maturity (YTM) of sovereign green bonds is influenced by conventional bond return, while conventional sovereign bonds are affected by the financial market return. Furthermore, the results confirm that the liquidity of sovereign green bonds can be explained by bond maturity.

Does financial stability communication affect financial asset prices? Evidence from the Bank of England's communication experiment

AbstractThis paper examines the Bank of England's (BoE) communication on financial stability between 2013 and 2018. We apply an event study to determine the communication effect on financial institutions' stock market returns. We find that the BoE's announcements generate negative average abnormal returns for non‐banking and banking systems, including the Global Systemically Important Banks. The same effect emerges when we consider the communication's tone. Furthermore, we construct a macroprudential decision communication index and show that the negative impact of the BoE's tone is significant only when the decision communication index value is above average. Moreover, we find evidence that negative abnormal returns tend to appear after the Brexit referendum, while we find positive abnormal returns before that date. Besides, we do not identify a noticeable effect related to communication practices.

Modeling the Nexus between European Carbon Emission Trading and Financial Market Returns: Practical Implications for Carbon Risk Reduction and Hedging

The carbon–financial nexus helps firms evaluate susceptibility to carbon risk more effectively. This is the first research article to model the short- and long-run co-integrating association between European financial markets, the CBOE oil price volatility index (OVZ) and the European carbon emission trading system (EU-ETS) by using the daily returns from 1 October 2013 to 1 October 2023. We utilize co-integration test followed by the ARDL framework with an error correction mechanism (ECM). Moreover, we utilize the DCC-GARCH-t copula framework to estimate the hedge ratio and to select an optimal portfolio weight for carbon risk hedging. Overall, the findings suggested that EU-ETS (OVZ) has a consistent positive (negative) short-term influence on all the equity returns of Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Netherlands, Spain and the stock indices of the whole Eurozone. However, in the long term, EU-ETS has a positive (negative) effect on the stock returns of France and the Eurozone (Belgium and Spain). Belgian and Spanish companies could implement long-term carbon reduction policies. Belgian and Spanish firms should focus on the utilization of green energy resources and the internalization of carbon emission-free mechanical processes as this may offer a safeguard against the additional pressure arising from escalating carbon prices. Finally, an optimal portfolio weight selection strategy based upon the DCC-GARCH-t copula approach aims for higher hedging effectiveness (HE) than the hedge ratio strategy when adopting short-term positions in Italian and Danish equity markets to reduce the risk of long-term EU-ETS volatility.

RESEARCH ON EMPIRICAL DISTRIBUTIONS OF INDEX RETURNS: EVIDENCE FROM INTERNATIONAL MARKETS

The paper reviews the currently held and quite persistent assumptions about the statistical distributions behind financial market returns used to make inferences for the purposes of risk management. We use data from 30 years of daily observations from the 15 largest and oldest stock price indices around the world and test the return data against the most popularly assumed distributions, breaking the data arrays into 100, 250, 750, and 250-day estimation windows at 0.95 and 0.99 confidence levels. We find returns in different estimation windows to be inconsistent with single distributions. When returns in short windows fit Gaussian distributions better, returns in longer estimation windows do not fit any suggested distributions at all. The normal distribution seems to be a better choice for 100-day windows, with longer window rejection rates for all suggested distributions being too high for reliable inference.

Behavioural explanations of Expectile VaR forecasting and dynamic hedging strategies for downside risk during the COVID‐19 pandemic: Insights from financial markets

AbstractIn this paper, we investigate the influence of confirmation bias on Expectile Value at Risk (EVaR) forecasting among fundamentalist, optimistic, and pessimistic investors in cryptocurrency, commodity, and stock markets before and during the COVID‐19 pandemic. Utilizing the DCC‐range GARCH model, we also explore the conditional minimum downside risk hedge ratios. Our findings demonstrate that confirmation bias leads to excessive EVaR for financial market returns, regardless of the period being before or during COVID‐19. Moreover, fundamentalists' expectations in all markets remain constant, while without confirmation bias, optimists' and pessimists' expectations tend to converge to zero over time but diverge significantly during turbulent periods. When confirmation bias is present, the average distance between these expectations widens. Analysing the hedge ratio results, with or without confirmation bias, also unveils the conditional minimum downside risk hedge ratios. These ratios indicate the optimal proportions for hedging downside risk in each financial market during different periods. We find that the conditional minimum downside risk hedge ratios are generally lower (higher) during the pre‐COVID‐19 (COVID‐19) period, implying that hedging costs are higher during the COVID‐19 period. These insightful findings offer valuable insights for traders and regulators in identifying and understanding the risk conditions of cryptocurrency, commodity, and stock markets. Additionally, the analysis of conditional minimum downside risk hedge ratios provides investors with essential information on how to strategically position their portfolios to mitigate and manage risk during both tranquil and turbulent market conditions, with and without confirmation bias.

The Interdependence of Financial Markets in Turbulent Periods: A Comparative Analysis of the China–US Cases

ABSTRACT: There has been a growing hypothesis linking the recent widespread declines in financial markets to the outbreaks of the COVID-19 pandemic. What is, however, unclear is whether the influence of the COVID-19 pandemic on the dynamics of the financial markets is as severe as those associated with traditional financial crises. Using the cases of the USA and China to represent developed and emerging economies, we examine the returns and volatility dynamics of financial market interdependence amid economic and financial crisis periods of divergent origins. Following careful consideration of relevant tests and model selection criteria, we find the VARMA-CCC-GARCH model to be the best approach for modelling financial market returns and volatility spillovers during a crisis. Employing daily financial market returns, we partitioned the study period into two sub-periods: the Great Recession (GFC) and the Great Lockdown (COVID-19), to arrive at the following empirical findings: First, we find that regardless of the type of crisis—the GFC or COVID-19—unanticipated events in the financial markets in the current period fuel high volatility in the market in the preceding period. Second, while financial markets in developed economies were by far the most affected by the 2008/2009 GFC, the great lockdown associated with COVID-19 appears to have left a permanent shock on both developed and emerging economies' financial markets. It then follows that the extent to which an economy is endowed with the ability to fend off external risks may be sensitive to the origin and nature of the crisis. In this light, it is necessary for policymakers to acknowledge that while some of the instruments used to mitigate the impact of external shocks on financial markets during the GFC may still be applicable during COVID-19 in the case of developed economies, the same appears to not be entirely true in the case of emerging financial markets during COVID-19. On the whole, financial experts and academics may want to be wary of the fact that the past realizations of both returns and volatilities matter for enhancing the forecastability of the future value of financial instruments during a crisis period.

Have Medical and Political Countermeasures Mitigated Investor Fears during the COVID-19 Pandemic?

Yes. We contrast the impacts of COVID-19 shocks and medical and political countermeasures in an otherwise standard model of financial market return determinants. During the pandemic, fears about COVID-19 can replace the economic sentiment to have a lasting negative impact on the stock market, especially in the first wave of the virus spreading. Medical research and government policies contribute to the stimulations of financial markets; however, efficient vaccines, when not developed, tend to negatively impact future stock returns and exacerbate the negative influence of COVID-19 sentiment on the stock market. Instead, such situations can be eased once medical research becomes advanced, helping the reconstruction of investor confidence. Public attention to the political support promoting economic and financial recovery has a positive impact on stock market returns and mitigates the shock of COVID-19 sentiment on the stock market.

The COVID-19 pandemic and financial markets in Central Europe: Macroeconomic measures and international policy spillovers

This paper investigates the effects of macroeconomic policy announcements on financial markets in three Central European economies: Czechia, Hungary, and Poland (CE–3). We focus on the unprecedented stabilisation policies implemented from March to December 2020 during the COVID-19 pandemic, including unconventional monetary measures and large stimulus programs. Detailed categories of monetary and fiscal measures are introduced into vector autoregressions with exogenous regressors and dynamic conditional correlations, which we estimate using daily data. This allows us to control for policy spillovers from abroad, as well as global risk factors and pandemic-related variables. We find that, in general, macroeconomic policy measures implemented in the CE–3 countries played an important role in stabilising financial markets during the pandemic. We uncover several notable patterns in the reaction of markets to anti-crisis measures across the region. The impact of the monetary policy announcements on 10-year sovereign bond yields was more substantial than on stock market returns and exchange rate returns. The communication of the unconventional tools proved effective in lowering the bond yields. Interestingly, we document that the effects of non-standard measures for some variables, such as the exchange rate, can be qualitatively different from those resulting from a conventional monetary expansion. Even though the domestic monetary events became more important than the fiscal ones, the latter proved relevant for financial market returns, especially when large-scale immediate fiscal measures and tax deferrals were introduced. We also show that the CE–3 economies were subject to the cross-border transmission of policy announcement effects from the Euro Area and the US, although the magnitude of these effects was smaller than expected and varied across the CE–3 countries.

Combining the financial development and stock market return for green economic recovery in selected developing economies

Combining the financial development and stock market return for green economic recovery in selected developing economies

Financial stabilization policy, market sentiment, and stock market returns

This study examines the responses of market sentiment and stock market returns in China during expansionary policy announcements released by the financial stability and development committee (FSDC). The results suggest that the expansionary financial stabilization policy has significant and positive impacts on stock markets in both a direct way and an indirect way through the market sentiment channel. The mediation analysis further confirms market sentiment as the significant mediator in transmitting the impact of the FSDC policy to stock markets. Moreover, the predictive power of market sentiment significantly increases during the FSDC policy announcements.

The transition of the global financial markets' connectedness during the COVID-19 pandemic

This study contributes to the literature on financial research under the presence of the COVID-19 pandemic. Fresh evidence emerges from using two novel approaches, namely network analysis and wavelet coherence, to examine the connectedness and comovement of financial markets consisting of stock, commodity, gold, real estate investment trust, US exchange, oil, and Cryptocurrency before and during the COVID-19 onset. Moreover, unlike the previous studies, we seek to fill a gap in the literature regarding the ex-post detection of COVID-19 crises and propose the Markov-switching autoregressive model to detect structural breaks in financial market returns. The first result shows that most financial markets entered the downtrend after January 30, 2020, coinciding with the date the World Health Organization (WHO) declared the COVID-19 pandemic as a Public Health Emergency of International Concern. Thus, it is reasonable to use this date as the break date due to COVID-19. The empirical result from network analysis indicates a similar connectedness, or the network structure, in other words, among global financial markets in both the pre-and during COVID-19 pandemic periods. Moreover, we find evidence of market differences as the MSCI stock market plays a central role while Cryptocurrency presents a weak role in the global financial markets. The findings from the wavelet coherence analysis are quite mixed and illustrate that the comovement of the financial markets varies over time across different frequencies. We also find the main and most significant period of coherence and comovement among financial markets to be between December 2019 and August 2020 at the low-frequency scale (>32 days) (middle and long terms). Among all market pairs, the oil and commodity market pair has the strongest comovement in both pre-and during the COVID-19 pandemic phases at all investment horizons.

Impact of COVID-19 on financial returns: a spatial dynamic panel data model with random effects

Using a dataset including financial market returns and volatility proxies for several countries, we analyzed the impact of Covid-19 deaths on the financial economy. From the modeling perspective, we consider a spatial panel data model for returns and a spatial dynamic panel data for volatilities. Proper marginal effects are calculated to exploit information on short- and long-term effects. A Chow test is used to identify the existence of a structural break in each series. Our empirical evidence shows that in the first weeks of the Covid-19 outbreak, until mid-March 2020, the identified break date, the spatial effect of Covid-19 deaths was statistically significant, leading to a contraction in returns and an increase in risk. Moreover, the effects disappeared in the remaining months as the financial markets moved back to pre-crisis levels, causing a decrease in the overall risk. Our evidence supports the behavioral impact of the pandemic on financial markets.

Are stock market returns affected by financial market variables? Evidence from Bursa Malaysia by panel generalized method of moments

PurposeThis study aims to find the relationship of stock market returns and selected financial market variables (market capitalization, earnings per share, price-earnings multiples, dividend yield and trading volume) of Malaysia grounded by the arbitrage pricing theories.Design/methodology/approachThis study empirically examines the effects of selected financial market variables on stock market returns using 64 companies listed in Malaysia's stock market with data spanning from 2005 to 2018. A systematic empirical study based on the Generalized Method of Moments following Arellano and Bond (1991) has been taken to estimate the effect.FindingsThe regression result of the financial market variables and stock market return shows that, except for trading volume, all selected financial market variables play significant roles in the stock market returns. Furthermore, market capitalization, earnings per share, price-earnings ratio, dividend yield and trading volume have a positive impact on stock market returns.Research limitations/implicationsThe outcome of this study can contribute by helping domestic and global investors devise strategies to minimize their risks. Also, policy administrators can use the outcomes of this study to inform the micro- and macro-level policy formulation.Originality/valueThis study will contribute to filling the gap in knowledge concerning the new release of factors affecting the stock market returns of Malaysia.

Penalties for industrial accidents: The impact of the Deepwater Horizon accident on BP's reputation and stock market returns.

Do visible industrial accidents damage firms' reputations and depress their stock market returns, and do these penalties spill over to other firms in the industry? On April 20, 2010, the Deepwater Horizon offshore oil rig in the Gulf of Mexico leased by BP exploded and sank, causing 11 deaths and the largest marine oil spill in US history. We examine the impact of this accident on BP's reputation and stock market performance using data from YouGov's BrandIndex and Capital IQ's financial data for the period 2007-2017. We employ a synthetic control analysis to examine the extent and duration of these penalties. We find that in the aftermath of the Deepwater accident, BP's reputation declined by approximately 50% relative to the synthetic control, and this decline persisted through the end of 2017. Yet, in terms of financial market returns, though the stock price dropped drastically in the first two months, we do not find a statistically significant decline in the stock market returns either in the mid-term (1-2 years) or the long term (2-7 years). In terms of spillover effects, we find no evidence of reputational damage or a decline in stock market returns for other oil and gas firms. These findings suggest that while environmental accidents invite swift and lasting reputational penalties, they might not depress the stock market performance in the long run. Moreover, the impact either on reputation or stock market returns does not necessarily spill over to other firms in the same industry.

The impact of COVID-19 on the portfolio optimization

PurposeThis article unveils first the lead–lag structure between the confirmed cases of COVID-19 and financial markets, including the stock (DJI), cryptocurrency (Bitcoin) and commodities (crude oil, gold, copper and brent oil) compared to the financial stress index. Second, this paper assesses the role of Bitcoin as a hedge or diversifier by determining the efficient frontier with and without including Bitcoin before and during the COVID-19 pandemic.Design/methodology/approachThe authors examine the lead–lag relationship between COVID-19 and financial market returns compared to the financial stress index and between all markets returns using the thermal optimal path model. Moreover, the authors estimate the efficient frontier of the portfolio with and without Bitcoin using the Bayesian approach.FindingsEmploying thermal optimal path model, the authors find that COVID-19 confirmed cases are leading returns prices of DJI, Bitcoin and crude oil, gold, copper and brent oil. Moreover, the authors find a strong lead–lag relationship between all financial market returns. By relying on the Bayesian approach, findings show when Bitcoin was included in the portfolio optimization before or during COVID-19 period; the Bayesian efficient frontier shifts to the left giving the investor a better risk return trade-off. Consequently, Bitcoin serves as a safe haven asset for the two sub-periods: pre-COVID-19 period and COVID-19 period.Practical implicationsBased on the above research conclusions, investors can use the number of COVID-19 confirmed cases to predict financial market dynamics. Similarly, the work is helpful for decision-makers who search for portfolio diversification opportunities, especially during health crisis. In addition, the results support the fact that Bitcoin is a safe haven asset that should be combined with commodities and stocks for better performance in portfolio optimization and hedging before and during COVID-19 periods.Originality/valueThis research thus adds value to the existing literature along four directions. First, the novelty of this study lies in the analysis of several financial markets (stock, cryptocurrencies and commodities)’ response to different pandemics and epidemics events, financial crises and natural disasters (Correia et al., 2020; Ma et al., 2020). Second, to the best of the authors' knowledge, this is the first study that examine the lead–lag relationship between COVID-19 and financial markets compared to financial stress index by employing the Thermal Optimal Path method. Third, it is a first endeavor to analyze the lead–lag interplay between the financial markets within a thermal optimal path method that can provide useful insights for the spillover effect studies in all countries and regions around the world. To check the robustness of our findings, the authors have employed financial stress index compared to COVID-19 confirmed cases. Fourth, this study tests whether Bitcoin is a hedge or diversifier given this current pandemic situation using the Bayesian approach.

The Effect of Financial Market Factors on House Prices: An Expected Utility Three-Asset Approach

This study aimed to theoretically identify the impact factors of the financial market on house prices. Developed upon the two-asset model and with the consideration of risky financial assets, our three-asset model reveals a new derivation of house prices. Compared with the two-asset model, the newly emerged term is similar to the Sharpe β; therefore, it is a risk premium term. Based on China’s 2001–2018 panel data, theoretical derivations are examined. However, the short-term effect of this risk term on house prices is practically small. Given the nonlinear pattern, the long-term effect of the risk term is checked by repeated stochastic simulation. The results imply the following: (i) real house prices are nonlinearly affected by three financial market factors, namely, the expected financial market return, financial market volatility, and the correlation between housing and financial markets; (ii) the correlation determines the signs and the significance of the effects of the other two factors; and (iii) the naturally changed correlation causes periodic house price fluctuations. Therefore, to stabilize real house prices, it is recommended that the government control the money flow between the two markets.

The ability of U.S. macroeconomic variables to predict Asian financial market returns

AbstractThis study conducts an extensive empirical investigation of whether 20 U.S. macroeconomic variables influence Asian financial market returns. The in‐sample results show that certain U.S. macroeconomic variables, such as the commercial paper‐to‐Treasury‐bill spread or the manufacturing Purchasing Managers' Index, possess powerful abilities to predict Asian market returns. The out‐of‐sample results show that other macroeconomic variables, such as change in the inflation rate, also show predictive power. We also use combination methods that pool information from U.S. macroeconomic variables to improve their predictive ability, and find that doing so produces superior out‐of‐sample results. We therefore find that the 20 U.S. macroeconomic variables contain information that can be used to predict Asian market returns.

Financial markets and the phase transition between water and steam

A lattice gas model of financial markets is presented that has the potential to explain previous empirical observations of the interplay of trends and reversion in the markets, including a Langevin equation for the time evolution of trends. In this model, the shares of an asset correspond to gas molecules that are distributed across a hidden social network of investors. Neighbors in the network tend to align their positions due to herding behavior, corresponding to an attractive force of the gas molecules.This model is equivalent to the Ising model on this network, with the magnetization in the role of the deviation of the market price of an asset from its long-term value. Moreover, in efficient markets the system should naturally drive itself to its critical temperature, where it undergoes a second-order phase transition. There, it is characterized by long-range correlations and universal critical exponents, in analogy with the phase transition between water and steam.Applying scalar field theory and the renormalization group, we show that these critical exponents imply predictions for the auto-correlations of financial market returns and for Hurst exponents. For a network topology of RD, consistency with observation implies a fractal dimension of the network of D≈3, and a correlation time of at least the length of the economic cycle. However, while this simplest model agrees well with market data on very long and on short time scales, it does not explain the observed market trends on intermediate time horizons from one month to one year.In a next step, the approach should therefore be extended to other models of critical dynamics, to general network topologies, and to the neighbourhood of the critical point. It allows us to indirectly measure universal properties of the hidden social network of investors from the empirically observable scaling behaviour of financial markets.