Abstract

This study investigates how voluntary International Financial Reporting Standards (IFRS) adoption impacts information asymmetry in the stock market. We analyze data from firms in Japan, where local accounting standards have substantially converged with IFRS but are more rules-based than principles-based IFRS. These different implementation approaches allow us to investigate how increased accounting flexibility influences recognition and measurement practices, attenuating the effects of accounting standard differences on the level of accounting rule existence or non-existence. We find that information asymmetry increases after voluntary IFRS adoption, which is driven by small- and medium-sized IFRS adopters’ decrease in earnings quality after voluntary IFRS adoption. Additional analyses reveal that firms’ reporting incentives and accounting resources influence these changes. These results suggest that IFRS’s increased accounting flexibility in recognition and measurement practices worsens the information environments of firms with weak incentives to commit to transparent financial reporting or fewer accounting resources (i.e., small- and medium-sized firms).

Introduction

As International Financial Reporting Standards (IFRS) are intended to provide investors with information that enables them to make better investment decisions, information asymmetry in stock markets is a critical potential consequence of IFRS adoption. IFRS adoption worldwide is either voluntary or mandatory. Since the 2005 mandatory adoption for firms listed in EU countries, extensive research has been conducted on the effects of mandatory IFRS adoption. However, voluntary IFRS adoption effects are relatively under-researched, especially in recent years (De George et al., 2016, Leuz and Wysocki, 2016). Understanding the effects of voluntary adoption is also important because a non-trivial number of countries either allow voluntary IFRS adoption or prohibit IFRS adoption (Song and Trimble, 2022). As of 2019, 11 % (22) of 195 countries/territories permit IFRS adoption and 6 % (11) presently prohibit it, although the latter could permit voluntary adoption in the future. This study attempts to further our understanding of how voluntary IFRS adoption affects information asymmetry in stock markets.

Prior research provides mixed evidence on the stock market consequences of voluntary IFRS adoption. Early studies on this topic focus on Germany, where local accounting standards are substantially different from IFRS. These studies provide evidence suggesting that voluntary adopters exhibit lower information asymmetry in the stock market compared to non-adopters (Gassen and Sellhorn, 2006, Leuz and Verrecchia, 2000). However, in international comparisons, Daske et al. (2008) use two proxies for differences between local accounting standards and IFRS and report inconsistent liquidity effect results. Specifically, they observe no change in liquidity after voluntary IFRS adoption in countries with small differences between local generally accepted accounting principles (GAAP) and IFRS, but a decrease in liquidity in countries with IFRS convergence processes. Therefore, how voluntary IFRS adoption affects information asymmetry in stock markets in countries whose accounting standard differences from IFRS are relatively small remains unclear. This study aims to extend this line of research.

Conclusions

Prior research suggests that voluntary IFRS adoption positively affects information asymmetry in stock markets when local accounting standards significantly differ from IFRS and is viewed as a commitment to transparent financial reporting. However, other research obtains inconsistent results for voluntary IFRS adoption effects on information asymmetry in countries with small accounting differences between local standards and IFRS. We looked closer at Japan, where local accounting standards have been converged to IFRS but have a different implementation approach (i.e., small accounting rule differences but large implementation approach differences). We investigated the effect of voluntary IFRS adoption on the stock market (i.e., changes in information asymmetry in the stock market proxied by bid-ask spread) as well as its potential reason (i.e., changes in earnings quality proxied by the absolute value of discretionary accruals). We also examined whether this effect varies according to firm size, which represents firms’ reporting incentives and accounting resources. Our study’s findings and incremental contributions to the literature are as follows.

First, we find an increase in the bid-ask spread after voluntary IFRS adoption. This result suggests that, on average, voluntary IFRS adoption worsens information asymmetry in Japan’s stock markets. Second, we observe deterioration in bid-ask spread only for smalland medium-sized voluntary adopters. Third, we examine changes in IFRS adopters’ earnings quality before and after IFRS adoption to explore a reason for the increased bid-ask spread. We find that small- and medium-sized firms report larger discretionary accruals after voluntary IFRS adoption. These results suggest that the increase in information asymmetry in the stock market results, at least partially, from the decreased earnings quality of small- and medium-sized firms after voluntary IFRS adoption. Finally, we provide some evidence suggesting that firms’ reporting incentives and limited accounting resources deteriorate earnings quality and information asymmetry at the time of voluntary IFRS adoption.

Abstract

We analyze stock market participation in 19 European countries, providing a composite view of the interplay and relative importance of established participation drivers. We jointly control for nearly all relevant drivers found in prior studies, which tend to introduce one novel variable at a time and often omit risk-aversion. Excellent full model predictive power decomposes into institutional (country) fixed effects (about 30% of total), traditional individual-level variables (50%), and more recently identified behavioral variables (20%). We sketch a hierarchical framework where factors’ effects vary by agents’ proneness to participate. We also challenge and complement existing interpretations given to sociability, IQ, trust, and life experiences.

Introduction

Limited stock market participation has been the quintessential topic in the emerging field of household finance.1 Differences in stockholding propensity across countries, and between people of comparable wealth, show that country factors related to economic or cultural institutions are important. On the individual level, wealth, income, and education are highly influential. Besides these traditional factors, more recent studies have uncovered an interesting set of behavioral factors that explain stock market participation. What is missing, however, is a composite view of the interplay and relative importance of the various drivers of participation.

In this paper, we take a novel, big-picture approach. Whereas the literature has tended to focus on testing the effect of a single new factor after controlling for traditional ones, we combine an extensive set of these variables in one model and explore the relative contribution of three types of factors – institutional, traditional, and new behavioral ones. We jointly estimate the effects of all variables while including a directly queried financial risk aversion measure known to predict actual financial risk-taking well (Dohmen et al., 2011; Halko et al., 2012; Guiso et al., 2018; Laudenbach et al., 2020). Then, based on the contribution of individual explanatory variables in various regulatory environments and subsamples of varying sophistication, we sketch a hierarchical model that describes the likely importance of each factor in different contexts.

Conclusion

The stock market participation literature has progressed steadily over the past few decades, but in step-by-step fashion, usually by adding one novel explanatory variable at a time. We take a consolidating approach by examining the roles of institutional, traditional, and behavioral factors, independently and jointly. We use data from SHARE, which comprehensively cover traditional drivers of participation, including risk aversion, and nearly all of the behavioral drivers from the recent stock market participation literature. We take full advantage of the data, exploiting cross-country variation in regulatory quality and varying levels of wealth and education across individuals.

Institutions clearly matter. Of the explanatory power, institutional factors captured by country fixed effects account for about a third. The traditional individual-level variables capture about 50%, and less than a fifth of the variation is explained by the new behavioral variables. We show that the effects of traditional variables and new behavioral variables change across levels of regulatory quality and across levels of wealth and education. In environments where participation rates are higher, such as high regulatory quality countries and among the wealthy and highly educated, variables reflecting values and attitudes have larger effects. In environments with lower levels of participation these variables have smaller or even non-significant effects. To complete our comprehensive approach, we also take a look at individual behavioral drivers and document complementing and contrasting results with previous literature.

Based partly on our results, and partly on prior research, we suggest a hierarchical model of participation drivers. In this model, similar in spirit to Maslow’s (1943) hierarchy of needs, the low-level factors, such as wealth, have to be on a sufficient level before high-level, e.g., psychological, factors can come into play. Further progress in explaining non-participation, particularly among the well-off, would likely benefit from a focus in the high end of the model.

Abstract

The Internet of Things (IoT) is becoming pervasive and with each new installation of IoT platforms new and legacy brokers have to be exploited. New internal brokers are those under the control of the platform, while legacy external brokers are those in place managed by third parties. The solution proposed addressed problems of (a) interoperability to reduce set up time to cope with unknown data structures (devices, entities) distributed via brokers; (b) performance by dimensioning both front-end and back-end processes to reach high rates in a broker-based platform, while preserving full capability features of the data warehouse. Interoperability aspects have been addressed by introducing our concepts and a reasoner into an IoT Directory tool to manage Internal and External brokers, automate device discovery and registration from both standard and customized data models. Despite the managed complexity, a broker-based solution turned out to provide high performance. To this end, a specific assessment and architecture tuning have been performed and reported in the paper to give evidence and validation. The proposed integrated IoT Directory has been developed in the context of the Herit-Data Project, and it is currently used in the whole Snap4City network of 18 tenants and billions of data. Snap4City is an open-source IoT platform for Smart Cities and Industry 4.0, which is an official FIWARE platform and solution, EOSC service and libs of Node-RED.

Introduction

The Internet of Things (IoT) defined a paradigm for the computation and communication amongst things, which is becoming every day more and more pervasive and adopted in many different domains [1,2]. It is partially due to the worldwide intense deployment campaign about Low-Power Wide Area Network technologies [3], as well as to many approaches and protocols for communications amongst devices (e.g., Message Queue Telemetry Transport or MQTT, Next Generation Service Interfaces or NGSI, Advanced Message Queuing Protocol or AMQP, Constrained Application Protocol or COAP). The approach is also covering the cloud and fog infrastructures [4,5]. Thus, IoT network infrastructures are becoming every day more complex to be managed due to the networks’ structure and existence of several protocols, formats, and concepts [1,6]. Hence, the complexity is growing in terms of data management, not only for huge amount of data but also for interoperability and abstraction levels needed to data managing. Relevant aspects to be considered are security and privacy [7] on specific data models and entities. To increase the complexity, there is a range of different owners and managers who may control different parts of such IoT networks [8].

In this context, the concept of Gateway is relevant for any segments of IoT networks connecting, as well as for the Web of Things, WoT [9]. Gateways may be integrated with one or more Brokers to send/receive data to/from Devices/entities. The Gateways and Brokers are typically compliant with a single protocol and may be managed by third parties with respect to data management platform. In some cases, they are provided as public services for several interested customers in the same area (for example: Proximus for LoraWAN services [10]). A Gateway may abstract from the IoT Broker level managing multiple brokers for multiple organizations/tenants (which can be regarded as customers of Gateway services to manage several Devices), via some API and/or Web user interface. Typical IoT Brokers can only manage one organization, and thus are single tenant, meaning they broker messages using topic/entity concepts (which can be regarded as the key for subscription on that specific device/entity) without any internal partition of services, but as a sort of family of devices and subscriptions. Some IoT Brokers can be multi-tenant, such as the FIWARE Orion Broker [11,12], which provides support for partitioning the served devices/entities/topics in groups, and each of them may have a dedicated service/path for a specific scope (or a specific customer). Furthermore, devices of different tenants could exist physically in different places (even having identical identifiers, IDs), and the subscription to the broker’s tenant may imply receiving all messages/services in the partition in push. That is feasible only if the subscriber knows the service/path identifier and, in the event of access control, the subscriber has the grant to access the broker’s services. Different IoT Devices connected to the same broker adopt the same protocol, may use different data structures/models and have the same semantic information.

Conclusions

The proliferation of IoT devices, brokers, networks, data models, operators and tenants, makes the harmonization and management of IoT Platform a hard goal. This paper offers an analysis and a comparison amongst relevant existing platforms, and it points out the basic requirements to achieve such aims. These identified requirements are in most cases not addressed by main platforms which prefer to stay on their own end-to-end solutions with limited interoperability and capacity of exploiting legacy IoT networks in place, in terms of performance. The proposed solution addressed problems of (a) interoperability by reducing set up time to efficiently detect and learn how to process unknown data structures (devices, entities) distributed via brokers; (b) performance by dimensioning the front-end and back-end processes to reach high rates in a broker-based platform, while preserving full capabilities features of data warehouse.

As to interoperability, the main identified and solved problems are those related to a large variety of Data Models coming from non-controllable External Brokers. The issue has been solved by designing and implementing a harvester and reasoner that is capable to automatically recognize/understand and map the new data models/types into those already known by Knowledge Base. This approach, together with the definition of a comprehensive meta model and dictionary, has allowed to speed up the process more than 800 times. The harvesting and comprehension process can be periodically performed to keep the platform updated with any newly defined data models by third party brokers. Furthermore, the process is helped by Km4City ontology and Data Dictionary to recognize the new data types and models according to the semantic domain.

Abstract

In this study, we use an extension of the heterogeneous autoregressive model to investigate the influence of time-varying risk aversion and macroeconomic, financial, and economic policy uncertainty measures on stock market volatility and correlation. Based on the findings, there is a stronger predictive ability of these variables at the monthly frequency than at the daily frequency. We also highlight the importance of risk aversion, which, alongside fundamental factors, reflects investor sentiment in predicting stock market volatility. Meanwhile, although uncertainty variables, such as economic uncertainty and financial uncertainty, are important, the widely used variable, economic policy uncertainty, is not helpful for predicting stock market volatility. Moreover, there is evidence of higher economic value and reduced portfolio risk when including risk aversion and economic uncertainty in international portfolio analysis.

Introduction

Understanding how uncertainty and risk aversion affect the volatility and correlation of financial markets is important for investors when planning their risk management and portfolio selection strategies as well as for policymakers when devising their economic policies.1 Thus, we conduct an empirical study on the relative importance of time-varying risk aversion and different sources of uncertainty in order to predict stock market volatility and correlation as well as determine whether this information is useful for international portfolio analysis.

As emphasized in previous research, risk aversion and economic uncertainty are key determinants of financial returns and risk premiums. Many studies have assumed constant risk aversion and focused on the time variation in economic uncertainty (e.g., Kandel and Stambaugh, 1990; Bansal et al., 2005, 2014). The importance of time variation in risk aversion has been highlighted in Campbell and Cochrane’s (1999) consumption-based asset pricing model. Recently, Bekaert et al. (2022) suggested an asset pricing model in which conditional volatility is driven by variation in both risk aversion and economic uncertainty.2 Economic uncertainty is primarily related to shocks from fundamental factors (e.g., consumption shocks in Bansal and Yaron, 2004), while risk aversion is driven by shocks from both fundamental and non-fundamental factors such as investor sentiment (e.g., Baker and Wurgler, 2006).3

Conclusion

This study contributes to the existing literature by comparing the predictive ability of risk aversion and various sources of economic uncertainty (macroeconomic, financial, and economic policy measures) for stock market volatility and correlation. Our results provide new insights into the relative importance of these predictor variables in stock market volatility and correlation.

In order to examine the influence of risk aversion and various uncertainty predictor variables on U.S. stock market volatility, we use the HAR model extended with exogenous predictor variables of Corsi (2009). As a benchmark model, we use the HAR model extended with the risk aversion and economic uncertainty measures of Bekaert et al. (2022). Our analysis shows the importance of the risk-aversion measure proposed by Bekaert et al. (2022). The results may indicate that non-fundamental factors, such as sentiment, are important for predicting variations in realized volatility. In addition, we find that the financial market uncertainty index from Ludvigson et al. (2021) is beneficial for predicting monthly realized volatility when added to the benchmark model.

In this study, the financial market uncertainty index is constructed from a large number of financial market variables, enabling it to capture the overall uncertainty of financial markets. In contrast to previous research, we find that EPU does not provide useful information for predicting stock market volatility once we account for RA and EU. Similar results are obtained for IP and CLI. Our analyses also show that exogenous predictor variables are more useful for modeling RV at the monthly frequency than at the daily frequency. This is partly because fluctuations in uncertainty follow a much smoother path than changes in stock market volatility, and partly because some of the predictor variables are not available at the daily frequency.

Abstract

     An essential issue of listed firms is adjusting their capital structure as stock market volatility increases. Our study examines this concern by using panel data of the Shanghai Stock Exchange for the period 2008–2018. We find that stock market volatility has immediate positive effects on both total market leverage and short-term market leverage but a negative influence on the long-term market leverage of Chinese listed firms. In this scenario, Chinese listed firms adjust their debt structure by using high bank debts and cutting trade credit due to lower debt costs. Further analyses confirm that the proportion of bank debts to total debts visibly increases while that of trade credit to total debts distinctly decreases. Furthermore, we implement robust tests regarding potential issues, such as sample selection, model selection, endogenous factors, and quantile regression to strengthen the robustness of the main findings. This study provides the first framework for investigating a link between the stock market volatility and capital structure decisions in a typical emerging market.

Introduction

     To date, determinants of capital structure have remained an incomprehensible subject in corporate finance. When firms are allowed to issue stocks on the equity market, equity issuance plays a vital role in the surging corporate capital. In terms of volatility, Welch (2004) documents that a critical matter of corporate finance is the volatility of stock price as a driver of notable changes in the behaviors and financial decisions of corporate managers. Figlewski and Wang (2000) demonstrate that the increase of the volatilities of stock market returns and individual stock returns leads to the decline in market values at the firm level. Following market timing theory, listed companies tend to issue equity when their market values are comparatively high relative to their book and past market values, and then repurchase their issued equity when their market values are low, which exerts a persistent effect on corporate capital structure (Baker & Wurgler, 2002). Chen et al. (2014) and Ahmed and Hla (2019) show that the volatility of individual stock returns significantly influences the capital structure of listed firms in the United States (US) and Pakistan markets, respectively. Most of their findings support the prediction of Tradeoff Theory (TOT) that companies diminish debts to balance and deal with the increasing prospect of default owing to high volatility risk; still, the practical results of Ahmed and Hla (2019) from the Pakistan market are pretty complicated. Similar to Pakistan, China is known as a developing equity market that is unstable, sensitive, and enormously volatile with unexpected shocks from the global market. In addition, the unique capital structure and debt structure of Chinese enterprises are inherently an exciting topic and thus widely explored in research (Fan et al., 2021; Zhang et al., 2015). To our best knowledge, the nexus between stock market volatility and corporate capital structure has not been explored. Surviving literature and evidence have powerfully prompted us to investigate the effect of stock market volatility on the capital structure choices of listed firms in the concrete context of the China stock market rather than imitating previous studies which focus on the volatility effect of individual stock returns on the corporate leverage.

Conclusion

     Choosing the capital structure as the increased stock market volatility is a vital concern of listed firms. This matter becomes more critical for listed firms activating in emerging markets, especially in China’s equity market, due to the following problems. First, emerging equity markets are immature and volatile. Simultaneously, listed firms in developing markets are quite sensitive to the equity market’s volatility. Second, thus far, corporate financing in China market essentially proceeds from internal funding and bank debts. In addition, Chinese firms universally have a high proportion of mature debts. Third, trade credit is an unofficial debt channel popularly used by Chinese firms when facing strict procedures from banks and credit institutions.

     Our study examines how the Chinese stock market volatility immediately affects the capital structure choices of Chinese listed firms from 2008 to 2018. The unique point of this research is the approach based on the capital use routines of Chinese firms instead of using two traditional capital structure theories to explain Chinese corporate capital structure, as in excellent previous literature (Chen, 2004; Huang & Song, 2006; Ni & Yu, 2008; Tong & Green, 2005; Zou & Xiao, 2006). We find that Chinese listed firms use the total market leverage and short-term market leverage at high levels, but long-term market leverage is used at low levels when the China stock market volatility increases and the lending interest simultaneously decreases. Empirical results point out that the positive correlation between corporate market leverage and stock market volatility is derived from the decline in the listed firm’s market value and the increase in bank debts. Trade credit significantly decreases while the bank debts tend to strongly increase in the debt structure of Chinese listed firms as the China stock market increases in volatility. Furthermore, we detect that short-term debts and bank debts are preferred in the capital structure of Chinese listed firms in response to the growing uncertainty of the China stock market and the change in lending interest rate.

Abstract

     Our study finds that momentum is a persistent phenomenon that exhibits great variability in its strength in the UK stock market. Inspired by psychological evidence that cognitive biases can shift overtime, we conjecture that there may be two stock market states, namely, the calm and the turbulent market state, and that the switch between these two market states is governed by market volatility. Using Bayesian estimation methods, our results confirm the role of market volatility as the critical switching variable, which is also found to have additional predictive power for momentum returns in the turbulent market state. Somewhat contradictory to the findings in cross-sectional studies, we find that past returns have a negative impact on momentum profits. We also find that both winners and losers tend to perform better in the turbulent market state than in the calm market state and that losers’ outperformance is responsible for large momentum losses in the turbulent market state. Investment strategies that take advantage of the predictability of momentum dynamics outperform momentum strategies. Our findings are not readily reconciled with risk-based explanations but can be loosely explained in a behavioural framework.

Introduction

     Momentum, first documented by Jegadeesh and Titman (1993), is a well-established phenomenon in the stock market. In the rational framework, momentum profits reflect variations in expected returns and are compensations for risks (Johnson 2002; Sagi and Seasholes 2007; Vayanos and Woolley 2013; Albuquerque and Miao 2014); whereas in the behavioural framework, momentum profits are the outcome of exploiting mispriced stocks, thanks to cognitive biases or bounded rationality (Barberis, Shleifer, and Vishny 1998; Daniel, Hirshleifer, and Subrahmanyam 1998; Hong and Stein 1999). Despite more than two decades of extensive studies, empirical findings on the sources of momentum profits are mixed and the question ‘what drives momentum?’ remains.

     Recent research work, including Barroso and Santa-Clara (2015), Daniel and Moskowitz (2016), and Dobrynskaya (2019), finds that momentum strategies that are profitable on average over time suffer occasional substantial losses and that these losses are predictable to some extent.1 These empirical findings on momentum dynamics and its predictability are intriguing and offer a new perspective for us to examine the causes of momentum and its time-varying characteristics. Daniel and Moskowitz (2016) conclude that none of the crash risk, volatility risk, and Fama and French (1993) factors, can fully explain their findings. They also point out that the existence of the same phenomena and option-like features for momentum strategies in other financial asset markets challenges explanations such as Merton (1974) story. Dobrynskaya (2019) investigates the explanatory power of 13 risk factors for the profitability of investment strategies based on the predictability of momentum crashes, and all factor betas except one are found to be close to zero and statistically insignificant.

Conclusion

     This study extends the investigation of momentum dynamics conditional on market state defined by market volatility. We investigate the momentum effect in the UK stock market and find that it is a significant phenomenon with great variability over time. In line with the recent studies on momentum effect in the US stock, the most striking features of its dynamics are the occasional sharp reversal and its association with market volatility. Informed by the patterns in momentum time series data and psychological evidence of time-varying cognitive biases and heuristics, we construct a two-regime switching model with lagged market volatility as the switching variable to capture the dominance of momentum effect and occasional reversals in the stock market in the short run and show that it performs well in explaining the facts both within and out of sample.

     We find a significant negative nonlinear relationship between the lagged market volatility and the momentum return, and a significant negative relationship between the past return and the momentum return in the calm market state. These relationships are robust across momentum strategies and over time. Momentum dynamics, especially the occasional sharp reversals, are highly predictable. Investment strategies that follow the prediction based on the two-regime switching model significantly outperform momentum strategies. The superior performance of the model-guided investment strategy in combination with the more favourable-to-investors distributional characteristics of its monthly holding period returns, particularly, the lower standard deviation and positive skewness, represents an anomaly which challenges the efficient market hypothesis.

Abstract

     This paper studies the US and global economic fundamentals that exacerbate emerging stock markets volatility and can be considered as systemic risk factors increasing financial stability vulnerabilities. We apply the bivariate HEAVY system of daily and intra-daily volatility equations enriched with powers, leverage, and macro-effects that improve its forecasting accuracy significantly. Our macro-augmented asymmetric power HEAVY model estimates the inflammatory effect of US uncertainty and infectious disease news impact on equities alongside global credit and commodity factors on emerging stock index realized volatility. Our study further demonstrates the power of the economic uncertainty channel, showing that higher US policy uncertainty levels increase the leverage effects and the impact from the common macro-financial proxies on emerging markets’ financial volatility. Lastly, we provide evidence on the crucial role of both financial and health crisis events (the 2008 global financial turmoil and the recent Covid-19 pandemic) in raising markets’ turbulence and amplifying the volatility macro-drivers impact, as well.

Introduction

     A common stylized fact about emerging economies is the high volatility of their stock markets (De Santis 1997; Aggarwal et al. 1999; Xu 1999; Cano-Berlanga and Giménez-Gómez 2018). The liberalization of the emerging world’s financial markets, which attracted a significant amount of capital flows by foreign institutional investors, has been the first step of an integration process with significant economies’ interdependence and asset markets’ synchronization. Given that emerging economies are characterized by critical vulnerabilities to external shocks, they exhibit higher equity market fluctuations than the developed markets and it is worth investigating how US and global common economic forces affect their intra-daily volatility. In general, modeling the volatility of financial returns has crucial implications for asset allocation, risk management practices, and financial stability oversight. Robust modeling and reliable forecasting of the volatility trajectory of financial instruments has been the main task and objective of financial economics applications for business operations, given that volatility constitutes one of the fundamental input variables in estimations and decision processes of any corporation on investing and funding choices. Financial volatility is also closely inspected by policymakers since it is perceived to constitute an early warning crisis signal, entailing critical destabilizing threats for the financial system (see, for example, Kürüm et al. 2018).

Conclusions

     Our study has examined the HEAVY model and its extension with leverage, power transformations, and macro-characteristics. For the realized measure, our empirical results favor the macro-augmented cross asymmetric power specification, where the lags of both powered variables—squared negative returns, and realized variance—move the dynamics of the power transformed conditional variance of the latter. Similarly, modeling the returns with a double asymmetric power process, we found that not only the powered realized measure asymmetry but the power transformed squared negative returns, as well, help to forecast the conditional variance of the latter. The macro-augmentation of the asymmetric power model ensures the superiority of our contribution, which can be implemented in several investment and risk management practices. We further demonstrated the forecasting dominance of the extended specifications over the benchmark HEAVY and standard volatility models through the out-of-sample forecasting across multiple short- and long-term horizons.

     Moreover, we demarcate our study from previous literature by estimating the significant US uncertainty effect on the power of leverage (Heavy and Arch), and the macro-determinants of emerging markets realized variance. The US-led uncertainty spillovers shed light on new evidence for volatility modeling and macro-financial linkages literature. Our findings’ novelty is twofold: Given higher (lower) daily US policy uncertainty levels, mostly associated with economic downturns (upturns), (i) heavy and leverage effects become more (less) acute in realized variance modeling, and (ii) US EPU volatility, financial uncertainty, credit conditions, commodity market benchmarks, and disease news impact on emerging financial volatility increases (decreases). Similarly, financial and health crisis events magnify further the Heavy, Arch, and Macro parameters of the bivariate system and the EPU indirect impact on the volatility drivers, as well.

Abstract

     This paper explores the effectiveness of predictors, including nine economic policy uncertainty indicators, four market sentiment indicators and two financial stress indices, in predicting the realized volatility of the S&P 500 index. We employ the MIDAS-RV framework and construct the MIDAS-LASSO model and its regime switching extension (namely, MS-MIDAS-LASSO). First, among all considered predictors, the economic policy uncertainty indices (especially the equity market volatility index) and the CBOE volatility index are the most noteworthy predictors. Although the CBOE volatility index has the best predictive ability for stock market volatility, its predictive ability has weakened during the COVID-19 epidemic, and the equity market volatility index is best during this period. Second, the MS-MIDAS-LASSO model has the best predictive performance compared to other competing models. The superior forecasting performance of this model is robust, even when distinguishing between high- and low-volatility periods. Finally, the prediction accuracy of the MS-MIDAS-LASSO model even outperforms the traditional LASSO strategy and its regime switching extension. Furthermore, the superior predictive performance of this model has not changed with the outbreak of the COVID-19 epidemic.

Introduction

     Financial market volatility is not only a key factor in assessing the risk of financial assets (see, e.g., Wang et al., 2015; Bee et al., 2016; Clements & Liao, 2017; Li & Wei, 2018; Ji et al., 2021) but also an important parameter in the pricing of financial derivatives and asset allocation (Graham & Harvey, 1996; Zhu & Ling, 2015), as well as an important factor in portfolio management (Ayub et al., 2015; Cederburg et al., 2020). Therefore, accurately predicting the volatility of the stock market is particularly important for investors when deciding on the sizes and timing of their investments.

     Despite the many benefits of accurately predicting stock market volatility, improving predictive accuracy has proven challenging. First, the diversity of potential predictors poses a great challenge to the selection of predictors. For example, studies have determined the predictive abilities of macroeconomic and financial variables for stock market volatility (Nonejad, 2017; Paye, 2012). In recent years, with the introduction of economic policy uncertainty indices, some studies have also determined the predictive power of some economic policy uncertainty indicators (e.g., the economic policy uncertainty index, trade policy uncertainty index, monetary policy uncertainty index, equity market volatility index and geopolitical risk index) relative to stock market volatility (Alqahtani et al., 2020a; Gupta & Wohar, 2019; Li et al., 2020a; Olasehinde-Williams, 2021; Paye, 2012; Yu et al., 2018). In addition, studies have found evidence that the stock market can be driven by investors’ psychology (Daniel et al., 2002; Tseng, 2006) and determined the predictive power of some market sentiment indicators (see, e.g., Gupta et al., 2014; Perez-Liston et al., 2014; Oliveira et al., 2017; Jin et al., 2020; Liang et al., 2020a; Wang et al., 2020a). Other indicators, such as financial stress indices, have also been proven to have potential forecasting ability (Gupta et al., 2014; Singh, 2016; Sum, 2014). The predictive abilities of these potential predictors tend to change with changes in various external factors, making it more difficult to find stable predictors.

Conclusions

     In this paper, we explore the effectiveness of 9 economic policy uncertainty indices, 4 market sentiment indicators and 2 financial stress indices in predicting the realized volatility of the S&P 500 index. We use several MIDAS-RV-X models to determine the predictive power of individual predictors and further construct PCA-, PLS- and LASSO-based MIDAS-RV extensions, a MIDAS-LASSO model with regime switching and various combination forecasts to find the optimal forecasting strategy. The out-of-sample forecasting performance of our forecasting models of interest is evaluated mainly by the MCS test and out-of-sample R2 statistics.

     The empirical results first show that the economic policy uncertainty indicators (especially the EMV) and the VIX are more likely to produce better forecasting accuracy than the three market sentiment indicators and two financial stress indices. Among these predictors, the VIX has the strongest predictive power, followed by the EMV. Second, the combination forecasts and MIDAS-LASSO models usually have statistically significant relative forecasting performance. Across all forecasting models of interest, the MIDAS-LASSO model with regime switching has the best forecasting performance, followed by the MIDAS-LASSO model, indicating that LASSO technology can capture more valuable information from a large set of predictors than other information integration methods. The forecasting accuracy of MIDAS-LASSO with regime switching is also superior to that of the traditional HAR-LASSO method and its regime switching extension. When distinguishing between high- and low-volatility regimes, we find that the VIX is the best predictor under both scenarios. In addition, the economic policy uncertainty indicators other than GEPU either help predict high stock market volatility or low stock market volatility, but the market sentiment indices other than the VIX and the financial stress index (OFRFSI) exhibit statistically significant relative predictive performance only during low-volatility regimes. Across all considered forecasting methods, the MS-MIDAS-LASSO model still exhibits the best predictive performance during both high- and low-volatility regimes, but the MIDAS-LASSO model can outperform other forecasting methods except for MS-MIDAS-LASSO only during high-volatility regimes. We finally consider the impact of the COVID-19 epidemic. During the COVID-19 epidemic, the predictive power of the VIX diminished, and it was no longer the best out-of-sample predictor, while the predictive power of the EMV increased, making it the best predictor during this special period. However, the MIDAS-LASSO model with regime switching still exhibited the best forecasting performance across all considered forecasting models during the COVID-19 epidemic.

Abstract

     This study examines the impact of the economic policy uncertainty index (EPU) and macroeconomic variables on the volatility of the Pakistan stock market using the GARCH-MIDAS (mixed data sampling) model. The model allows us to observe whether those variables contain valuable information to forecast stock market volatility. Our empirical findings show several outcomes. First, our out-of-sample results show economic policy uncertainty index has predictive power to forecast Pakistan stock market volatility. Second, among all variables, oil prices are the most powerful predictor of volatility with a higher out of sample R square value. Third, all macroeconomic variables including exchange rate, short-term interest rate, money supply M2, foreign direct investment, gold prices, inward remittances, industrial production, and consumer price index (proxy for inflation) contain useful information for stock market volatility forecasting. However, the long-run interest rate is an ineffective indicator of volatility during the sample period study. Finally, we find that the combination forecast information is also useful for volatility forecasting.

Introduction

     Volatility in financial markets is an issue of great concern. As a risk measure, volatility considers an important state variable for asset pricing and investment. Additionally, the volatility dynamics are associated with portfolio allocation, risk management, hedging, and options pricing (Christoffersen & Diebold, 2000; Chun et al., 2020; Mei et al., 2017; Morales and Callaghan, 2011; Naeem et al., 2019; Wang and Nishiyama, 2015; Zhang et al., 2020). The higher volatility creates widespread panic and results in disorderly market situations that reduce investors’ confidence and that lead to declines in business investments and economic growth. Therefore, understanding the mechanism of economic dynamics and accurately estimating future volatility is essential.

     However, it is prudently observed by financial analysts, policymakers, and market practitioners. Nonetheless, before estimation, it is hard to find the main economic drivers of volatility. The seminal work of SCHWERT and WILLIAM (1989), who posits a time-varying link between stock market uncertainty and macroeconomic variables. Moreover, Paye (2012) examines the strong linear connection between macroeconomic indicators and stock market volatility. Both theoretical and empirical studies confirm that institutional and macroeconomic determinants are potential drivers of financial markets volatility (Engle et al., 2013; Bahloul et al., 2017; Fang et al., 2018; Hsu et al., 2019). The literature shows that financial market volatility and macroeconomic indicators are intrinsically associated (Chen et al., 1986; Mele, 2007; Christiansen et al., 2012).

Conclusion

     This research examines the forecasting ability of macroeconomic variables and economic policy uncertainty index (EPU) for Pakistan stock market volatility. The macroeconomic variables include exchange rate, long-run interest rate, money supply (M2), short-term interest rate, remittances, consumer prices index (proxy for inflation), foreign direct investment, industrial production, global oil prices, and gold prices. We use the GARCH-MIDAS model for forecasting short-and long-run volatility. In our empirical analysis, we find some notable results. First, we find economic policy uncertainty index information is useful for Pakistan stock market volatility prediction. Second, we find global indicators oil prices are more powerful predictor of Pakistan market volatility. Moreover, all macroeconomic variables are effective indicators of Pakistan stock market volatility except long run interest rate. In addition, the combination information for all macroeconomic variables is also useful for forecasting volatility. These findings are in line with the literature (Asgharian et al., 2013; Liu & Pan, 2020). However, the abovementioned studies are not directly involved in forecasting Pakistan’s stock market volatility. In addition, we did not find consistent findings in a longer period, such as two months and three months. Overall, results indicate that macroeconomic variables are significant predictors of stock market volatility in Pakistan. Therefore, the government and policymakers should consider these factors in policymaking and stock market volatility estimation.

Abstract

Graphical abstract

JEL classification

Keywords

1. Introduction

2. Methodology

3. The model and results

4. Robustness

5. Conclusion

Author contributions

Declaration of Competing Interest

Acknowledgments

Appendix A

References

Abstract

In this study, we examine the hedging relationship between gold and US sectoral stocks during the COVID-19 pandemic. We employ a multivariate volatility framework, which accounts for salient features of the series in the computation of optimal weights and optimal hedging ratios. We find evidence of hedging effectiveness between gold and sectoral stocks, albeit with lower performance, during the pandemic. Overall, including gold in a stock portfolio could provide a valuable asset class that can improve the risk-adjusted performance of stocks during the COVID-19 pandemic. In addition, we find that the estimated portfolio weights and hedge ratios are sensitive to structural breaks, and ignoring the breaks can lead to overestimation of the hedging effectiveness of gold for US sectoral stocks. Since the analysis involves sectoral stock data, we believe that any investor in the US stock market that seeks to maximize risk-adjusted returns is likely to find the results useful when making investment decisions during the pandemic.

1. Introduction

The literature on the response of financial markets, including the stock market, to the COVID-19 pandemic is fast growing, owing to its grave consequences. Several studies have examined the impact of the pandemic on financial markets (see for example, Ali et al., 2020; Al-Awadhi et al., 2020; Baig et al., 2020; Haroon and Rizvi, 2020a,b; He et al., 2020; Li et al., 2020; Narayan et al., 2020a,b; Mishra et al., 2020; Phan and Narayan, 2020; Prabheesh et al., 2020; Salisu and Akanni, 2020; Salisu et al., 2020a,b; Salisu and Sikiru, 2020; Salisu and Vo, 2020; Sharma, 2020; Zhang et al., 2020; Sikiru and Salisu, 2021; among others)1 and the evidence suggests a negative impact; consequently, the need to hedge against the risk associated with the pandemic becomes crucial.2 This is the motivation for our study. In response to this challenge, we consider gold to be a potential asset that can serve as a good hedge for stocks against the pandemic risk. The pioneering studies on the gold-stocks nexus (see for instance, McDonald and Solnik, 1977; Sherman, 1982; Jaffe, 1989; Chua et al., 1990) indeed support the inclusion of gold in the equities portfolio for the benefits of portfolio diversification and downside risk management.3,4 This continues to be emphasised in the recent literature with pieces of evidence of negative or low correlation of gold in the investment portfolio containing stocks (see for example, Joy, 2011; Reboredo, 2013; Beckmann et al., 2015; Bredin et al., 2015). This strand of the literature also finds support in the conceptual arguments, which suggest stock market hedging qualities of gold when its return is shown to be negative or uncorrelated with the stocks prices (or returns) (see Baur and Lucey, 2010; Baur and McDermott, 2010; Zagaglia and Marzo, 2013).5 In the class of traditional hedging assets, Shahzad et al. (2020), while not particularly considering the COVID-19 pandemic, have shown that gold offers more effective hedging benefits for G7 stock indices.

1- Introduction

2- Literature review

3- Methodology

4- Data

5- Findings

6- Conclusion

References

Abstract

This paper investigates the effects of regulation pertaining to information disclosure on the Vietnam stock market. Using the event study methodology, we examine sectoral reactions, in terms of risk and return, following the announcements on information disclosure regulation in Vietnam. To validate the results, we also conduct several robustness tests such as the removal of firm-specific information and the use of a wide variety of ARCH models such as GARCH (1,1). We find evidence indicating that when the market anticipates a piece of regulation on information disclosure, most sectors experience negative reactions two and five days before the first announcement. Positive reactions are observed on the event date, as well as two and five days afterwards. Furthermore, we document a difference between the Ho Chi Minh Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX) in terms of market reaction. The results also show that the sectors experience changes in short-term systematic risk. Our contributions to the literature are threefold. First, we focus on a complete and more updated set of the Vietnam stock market’s information disclosure regulation. Second, our study examines the effects of a series of events on a single regulation at sectoral and firm levels in an emerging market. Third, in addition to sectoral analysis, we analyse the Vietnam stock market reaction at the firm level.

Introduction

The impact of regulatory announcements on stock market reaction has been examined extensively in the literature. In developed markets, for instance, Sawkins (1996); Antoniou and Pescetto (1997); Dnes et al. (1998); Robinson and Taylor (1998); Morana and Sawkins (2000) and Pescetto (2008) look at the UK market whilst Binder (1985) and Teets (1992) study the US market. In addition, many studies have examined the effects of a single regulation or a series of events pertaining to a single regulation in developed markets. In general, firms tend to face a higher level of risk when a new regulation is introduced (DeLong and Saunders, 2011; Bailey et al., 2006; Eleswarapu et al., 2004). One of the most important regulations on the stock market is information disclosure regulation as this type of regulation directly affects firms as well as investors in terms of information efficiency and transparency (i.e. reducing information asymmetry).

1- Introduction

2- Literature review

3- Methodology

4- Data

5- Empirical analysis

6- Conclusion and implications

References

Abstract

This study explores the issue of financial integration among stock markets of ASEAN5 economies, plus China (mainland China and Hong Kong), Japan and South Korea (referred to as ASEAN5+4). Using both graph theory and a Vector Autoregressive (VAR)-based method, together with a rolling window approach, we show that the level of interconnectedness among these markets is high but with clear time varying patterns. A large share of this seemingly high level of integration is shown to be driven by common global factors. After filtering these factors from each stock market, the magnitude of interconnectedness falls substantially. Our results therefore suggest that stock market integration in East and Southeast Asia is not as strong as it looks. Although governments in this region have been promoting financial market collaboration and integration, barriers remain significant. The overestimated interconnectedness is mainly a simple reflection of stronger global influences on individual markets, while their interconnectedness attributable to non-global factors shows a descending trend after the crisis.

Introduction

Asian stock markets are increasingly integrated in recent years (Chien, Lee, Hu, & Hu, 2015), accompanied by joint policy efforts on building up a regionally integrated market to promote capital mobility within the region, such as the AEC Blueprint series (ASEAN, 2015). According to the 2018 Asian Economic Integration Report (ADB, 2018), Asia’s share of global inward foreign direct investment has shown an increasing trend in recent years, for example, rising from 27.8% in 2016 to 36.2% in 2017. An upward trend is also seen in international holdings of Asian portfolio equity assets, rising by 1.3 trillion USD in 2017. This year also witnessed a surge of global remittances to Asia amounting to 272.5 billion USD, concurrent with strengthened global economy. Regional stock market integration was found reinforced during the turmoil of the global financial crisis (Gupta & Guidi, 2012). For the post-crisis period, while some studies find evidence of declining co-movement among certain Asian stock markets (Gupta & Guidi, 2012), others find strengthened linkages among major East Asian stock markets (Wang, 2014). Capital market liberalization and globalization largely contribute to Asian stock market integration (Arouri & Foulquier, 2012). With ongoing financial market deregulation and capital account liberalization, cross-border financial transactions are more prevalent (Chien et al., 2015; Singh, 2009). Massive inflows of foreign investment contribute substantially to the boom of local equity markets in this region. Market capitalization of major Ease and Southeast Asian stock markets grows substantially during the last two decades. For example, the market value rises 6.51 times for Hong Kong, 9.48 times for Indonesia, 6.06 times for the Philippines, and a whopping 61.13 times for China’s Ashare market, with its Shanghai stock Exchange being the fourth largest in the world by the end of 2018.1 East and Southeast Asian stock markets have become an important part of fund managers’ international portfolios for the purposes of increasing returns and reducing risks (Narayan, Sriananthakumar, & Islam, 2014). On the other hand, the global stock market exerts increasingly significant influences on Asian markets (Burdekin & Siklos, 2012; Huyghebaert & Wang, 2010). There is no doubt that the trend of globalization will inevitably lead to significantly increased stock market co-movement and stronger linkages in capital markets arising from potential spillover effects. It is, however, questionable how strong Asian equity markets are interlinked in the absence of common global factors.

This study examines the predictability of stock market implied volatility on stock volatility in five developed economies (the US, Japan, Germany, France, and the UK) using monthly volatility data for the period 2000 to 2017. We utilize a simple linear autoregressive model to capture predictive relationships between stock market implied volatility and stock volatility. Our insample results show there exists very significant Granger causality from stock market implied volatility to stock volatility. The out-of-sample results also indicate that stock market implied volatility is significantly more powerful for stock volatility than the oil price volatility in five developed economies.

Introduction

Stock market volatility is a crucial input for risk management, asset pricing and portfolio management. Therefore, modeling and forecasting stock market volatility remain a hot topic in financial econometrics. To improve the stock market volatility forecasting, some studies have constructed new and powerful predictors or factors. Schwert (1989) finds limited support for links between volatility and macroeconomic predictors, whereas more recent papers such as Christiansen, Schmeling, and Schrimpf (2012), Paye (2012), Engle, Ghysels, and Sohn (2013), Conrad and Loch (2015), and Nonejad (2017), Mohsen and Sujata (2019) arrive at somewhat more encouraging results by constructing the macroeconomic and financial variables. Very recently, Feng, Wang, and Yin (2017) find that oil volatility risk premium (oil VRP) does exhibit statistically and economically significant in-sample and out-of-sample forecasting power for stock market volatility in G7 countries. Wang, Wei, Wu, and Yin (2018) also show that the crude oil volatility is predictive of stock volatility in the short-term from both in-sample and out-ofsample perspectives. In addition, Bašta and Molnár (2018) study the comovement between volatility of the equity market and the oil market, both for implied and realized volatilities by using the wavelets method.