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Changes in the propagation of shocks along firm networks are important to understanding aggregate and cross-sectional features of stock returns. When calibrated to match key characteristics of supplier–customer networks in the United States, a model in which firms are interlinked via enduring relationships generates long-run consumption risks, high and volatile risk premiums, and a small and stable risk-free rate. The model also matches cross-sectional patterns of portfolio returns sorted by firm centrality, a feature unaccounted for by standard asset pricing models. (JEL C67, E30, G12, L14) [ABSTRACT FROM AUTHOR]

Contrary to leading asset pricing theories, recent empirical evidence indicates that financial markets compensate only short-term equity variance risk. An equilibrium model with generalized disappointment aversion risk preferences and rare events reconciles salient features of the variance term structure. In addition, a calibration explains the variance and skew risk premiums in equity returns and the implied volatility skew of index options while capturing standard moments of fundamentals, equity returns, and the risk-free rate. The key intuition for the results stems from substantial countercyclical risk aversion induced by endogenous variation in the probability of disappointing events in consumption growth. [ABSTRACT FROM AUTHOR]

We show that a common component governs volatility dynamics across a wide range of traded equity factors. This "common factor volatility" (CFV) exists even among orthogonal factors. CFV occurs in both cash-flow and discount-rate components of factor returns and derives from market responses to fundamental news rather than underlying commonality in news volatility. Incorporating CFV improves factor volatility forecasts relative to models that include only own-factor volatility. CFV allows us to characterize stochastic discount factor (SDF) volatility dynamics in a very general sense and we show that many popular models imply SDFs with time-varying volatility that correlates strongly with CFV. [ABSTRACT FROM AUTHOR]

Credit default swaps (CDSs) are a major financial innovation related to debt contracting. Because CDS markets facilitate bad news being incorporated into equity prices via cross-market information spillover, CDS availability may curb firms' information hoarding. We find that CDS trading on a firm's debt reduces the future stock price crash risk. This effect is stronger in active CDS markets, when the main lenders are CDS market dealers with securities trading subsidiaries, or when managers have more motivation to hoard information. Our findings suggest that debt market financial innovations curtail the negative equity market effects of firms withholding bad news. [ABSTRACT FROM AUTHOR]

Nowadays, the manufacturing sector needs higher levels of flexibility to confront the extremely volatile market. Accordingly, exploiting both machine and workforce reconfigurability as two critical sources of flexibility is advantageous. In this regard, for the first time, this paper explores an integrated production scheduling and workforce planning problem in a Reconfigurable Manufacturing System (RMS) benefiting from reconfigurable machines and human-robot collaboration. A new Mixed-Integer Linear Programming (MILP) model and an efficient Constraint Programming (CP) model are developed to formulate the problem, minimising the makespan as the performance metric. Due to the high complexity of the problem, the MILP model cannot handle large-sized instances. Hence, to evaluate the performance of the CP model in large-sized instances, a lower bound is derived based on the relaxation of the problem. Finally, extensive computational experiments are carried out to assess the performance of the devised MILP and CP models and provide general recommendations for managers dealing with such a complex problem. The results reveal the superiority of the CP model over the MILP model in small- and medium-sized instances. Moreover, the CP model can find high-quality solutions for large-sized instances within a reasonable computational time. [ABSTRACT FROM AUTHOR]

We develop a dynamic model of costly stock short-selling and lending market and obtain implications that simultaneously support many empirical regularities related to short-selling. In our model, investors' belief disagreement leads to shorting demand, whereby short-sellers pay shorting fees to borrow stocks from lenders. Our main novel results are as follows. Short interest is positively related to shorting fee and predicts stock returns negatively. Higher short-selling risk can be associated with lower stock returns and less short-selling activity. Stock volatility is increased under costly short-selling. An application to GameStop episode yields implications consistent with observed patterns. [ABSTRACT FROM AUTHOR]

The availability of cyber-physical systems (CPS) in modern manufacturing networks provides a multitude of interesting opportunities from a manufacturing control perspective. Providing sensors, data gathering, local computation and communication capabilities modern CPS fulfil the technical requirements to act completely autonomously in a manufacturing network. While the distribution of decision-making authority to autonomous entities is feasible given such requirements, practice often sees the monopolisation of decision-making authority for centralised control. However, distributed production control approaches might be better suited given current manufacturing challenges, ranging from unreliable supply chains over highly volatile markets, to the demand for increasingly efficient and highly customisable production. In this article, we extend an existing scheduling complexity framework which enables practitioners and researchers alike to assess the aptitude of given manufacturing networks for both centralised and distributed control. In particular, we study the influence of a manufacturing network's topology ranging from assembly line to job shops on the aforementioned aptitude, with total production costs as objective. We utilise a multi-agent-based discrete-event simulation comparing an MILP-based centralised control approach and an autonomy based distributed control approach with weighted costs as decision function to evaluate this framework. Our results provide novel insights regarding the influence of manufacturing network topologies on the scheduling complexity of manufacturing networks. [ABSTRACT FROM AUTHOR]

To face market volatility, reconfigurable manufacturing systems (RMS) aim to efficiently and cost-effectively react to changes. We focus on one characteristic of RMS: the scalability (ability to adapt the volume of throughput). In the literature, the only few indicators for scalability are not always formally defined and usually only consider a partial view of scalability. Moreover, most of them are actually more suited for the configuration planning rather than for the design. However, the design of the RMS has a high impact on its scalability. We propose the first combinatorial definition of this problem and a new measure to fully assess the scalability of a system at the design phase. This measure, based on a multi-objective approach, can assess the scalability of single-product manufacturing systems, analysing all configurations that it can implement. We present numerical experiments to compare this indicator with a state-of-the-art scalability indicator and with some classical production line design indicators, and we show that future research should focus on scalability as a specific criterion to optimise during the design of an RMS. In addition, the results obtained allow us to infer some managerial insights on the best levers to use when performing a reconfiguration for scalability purpose. [ABSTRACT FROM AUTHOR]

Consistent with the empirical properties of the consumption data, I develop a model in which consumption and dividend growth follow regime-switching dynamics. I show that regime-shift risk is priced in the model. Regime-shift risk exhibits dominant influence on asset prices: It generates a high equity premium and also induces time-varying risk premiums. The model explains major business cycle-dependent asset market phenomena and, in particular, the stronger predictability of stock returns during recessions. [ABSTRACT FROM AUTHOR]

We present evidence that several popular low-frequency measures of effective spread suffer from a volatility-induced bias and that volatility is the primary driver of the variation of these liquidity proxies. Using data for U.S. equities and major foreign exchange rates, we show that the bias arises when the effective spread is small relative to volatility. We document that the bias has become more acute over time and show that volatility-biased measures fail to replicate some well-known results in empirical finance. We conclude by providing guidance on the choice of low-frequency measures in empirical applications. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Volatility in the cryptocurrency market is an extremely important indicator for investors, as it allows them to manage their investment risk and define strategies that will result in profit maximization. Thus, this study focuses on determine which of the GARCH, EGARCH, and TGARCH models is the optimal model that best describes the daily returns' volatility of MATIC, SOL, BTT, and VET – four cryptocurrencies with relatively limited presence in the market compared to Bitcoin. The optimal model is selected by using the AIC statistical quality criterion. For the chosen sample period, empirical evidence suggests that EGARCH(1,1) and GARCH(1,1) are the most suitable models for describing the returns' volatility of MATIC and VET, respectively. In the cases of SOL and BTT, the lack of success in validating all the assumptions needed to apply GARCH models reveals that these models are not the most adequate for describing the cryptocurrencies under study. [ABSTRACT FROM AUTHOR]

Purpose: Expanding on the real-world financial market framework and considering the current market turmoil, with cryptocurrencies (where contracts for difference (CFDs) are extremely common) (Hasso et al., 2019) displaying unprecedented volatility, the authors aim to test in an online laboratory setting whether displaying a risk warning message is truly effective in reducing the level of risk taken and whether the placement of this method makes a difference. Design/methodology/approach: To explore the impact of risk disclosure framing on risk-taking behavior, the authors conducted an online pair-wise lottery choice experiment. In addition to manipulating risk awareness through the presence or absence of risk warning messages of varying intensity, the authors also considered dynamic inconsistency, cognitive ability and questionnaire-based financial risk tolerance (FRT) scores. The authors aimed to identify potential relationships between these variables and experimentally elicited risk aversion. The authors' study offers valuable insights into the complex nature of risky decision-making and sheds light on the importance of considering dynamic inconsistency in addition to risk awareness and aversion. Findings: The authors' results provide statistical evidence for the efficacy of informative and very salient messages in mitigating risky decision, hinting at several policy implications. The authors also provide some statistical evidence in support of the relationship between cognitive abilities and risk preferences. The authors detect that individual with low cognitive abilities scores display great risk aversion. Originality/value: This study investigates the impact of risk warning messages on investment decisions in an online laboratory setting – a unique approach. However, the authors go beyond this and also examine the potential influence of dynamic inconsistency on decision-making, adding further value to the literature on this topic. To ensure a comprehensive understanding of the participants, the authors collect data on cognitive ability and FRT using questionnaires. This study provides a simple and cost-effective framework that can be easily replicated in future research – a valuable contribution to the field. [ABSTRACT FROM AUTHOR]

The value strategy and technical analysis strategy have existed in the financial market for a long time, and the impact of these two types of strategies on the financial market has also been debated for a long time. This paper studies the impact of trading strategies on market volatility by constructing a market ecology model including the simple technical strategy and value strategy. The results show that both the nature and the population size of a trading strategy can affect market volatility. In a market composed of the trend-following strategy and the value strategy, when the populations of the two strategies match, market volatility is low; when either of the two strategies has too much population, market volatility is high. However, in a market composed of the trend-reversal strategy and the value strategy, there is a positive correlation between market volatility and the population size of each strategy. The comparison of these results suggests that substantial diversification of trading strategies may be a fundamental force for market stability. [ABSTRACT FROM AUTHOR]

In today's rapidly changing production landscape with increasingly complex manufacturing processes and shortening product life cycles, a company's competitiveness depends on its ability to design flexible and resilient production processes. On the shop-floor, in particular, the production control plays a crucial role in coping with disruptions and maintaining system stability and resilience. To address challenges arising from volatile sales markets or other factors, deep learning algorithms have been increasingly applied in production to facilitate fast-paced operations. In particular deep reinforcement learning frequently surpassed conventional and intelligent approaches in terms of performance and computational efficiency and revealed high levels of control adaptability. However, existing approaches were often limited in scope and scenario-specific, which hinders a seamless transition to other control optimization problems. In this paper, we propose a flexible framework that integrates a deep learning based hyper-heuristic into modular production to optimize pre-defined performance indicators. The framework deploys a module recognition and agent experience sharing, enabling a fast initiation of multi-level production systems as well as resilient control strategies. To minimize computational and re-training efforts, a stack of trained policies is utilized to facilitate an efficient reuse of previously trained agents. Benchmark results reveal that our approach outperforms conventional rules in terms of multi-objective optimization. The simulation framework further encourages research in deep-learning-based control approaches to leverage explainability. [ABSTRACT FROM AUTHOR]

We study systemic risk drivers of FinTech and traditional financial institutions under normal and extreme market conditions. We use machine learning (ML) techniques (i.e. random forest and gradient boosted regression trees) to evaluate the role of macroeconomic variables, firm characteristics, and network topologies as systemic risk drivers and perform the ML-based interpretation by Shapley individual and interaction values. We find that (i) the feature importance in driving systemic risk depends on market conditions; namely, market volatility (MVOL), individual stock volatility (IVOL), and market capitalization (MC) are positive drivers of systemic risk under extreme (downside and upside) market conditions, while under normal market conditions, institutions with high price-earnings ratio, large MC, and low IVOL play an essential role in stabilizing markets; (ii) macroeconomic variables are the most important extreme systemic risk drivers, while firm characteristics are more important under normal market conditions; and (iii) the interaction between IVOL and MC or MVOL is the significant source of extreme systemic risk, and MC is the most crucial interaction attribute under normal market conditions. The interactions between macroeconomic variables are the most prominent in systemic risk under different market conditions. [ABSTRACT FROM AUTHOR]

This study explores whether frequent trading is profitable to investors in an emerging stock index futures market. Our analyses, based on long‐term data from 2010 to 2023, indicate that the effect of trading frequency differs across investor types and market conditions. Only some domestic institutions gain additional profits from more frequent trading, and such a tendency is apparent when the futures price falls and when the futures market volatility is low. Foreign investors experience losses as they trade more when the market is bearish and are frequently net long. The performance of domestic individuals does not depend on their trading frequency in general; however, they lose more from trading when the market is bearish and when the market is less volatile. [ABSTRACT FROM AUTHOR]

This paper examines the co‐jump transmission in 20 commodity futures returns in the United States using co‐jump network models. Specifically, it reveals co‐jumping behavior in both static and time‐varying settings, considering the overall commodity markets and various commodity groups separately, which helps us understand the dynamic changes in co‐jump dependencies at the overall and sector levels. The main results reveal that co‐jump heterogeneity exists among commodities but is generally more apparent within each commodity group, and co‐jumps vary over time. Gold exerts the strongest influence, with many commodity futures being influenced by the jumps behavior in gold returns. During the COVID‐19 outbreak and the Russia–Ukraine war, the energy group ranks highest in terms of co‐jump network centrality. Our empirical analysis highlights the portfolio performance and risk reduction, and an additional examination shows that centrality information from the co‐jump network contains a highly and statistically forecasting power for US stock market volatility. [ABSTRACT FROM AUTHOR]

Purpose: This study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023. Design/methodology/approach: The study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing. Findings: The study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes. Practical implications: These findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market. Originality/value: The Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants. Research highlights: This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes. We highlight how factors like volatility, futures volume, and open interest vary in their impact. The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing. We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities. [ABSTRACT FROM AUTHOR]

The study examines the relationship and linkages between spot and future prices of European Union Emission Trading Systems (EU-ETS) during 2019–2021. Dynamic conditional correlation specification of multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model was employed to examine the time-varying correlation between spot and future prices. Also, vector autoregression mean model and MGARCH-Baba-Engle-Kroner-Kraft volatility model was jointly estimated to model the spillover between EU ETS spot and future prices in the first and second moments. Lastly, we utilize the variance-covariance matrix of joint mean-variance model estimation to derive the optimal conditional hedge ratio as well as the hedge effectiveness of EU ETS future contracts. Our findings reveal a high conditional correlation and significant spillover between carbon spot and future markets in EU. Further, our study uncovers a high degree of hedge effectiveness for EU ETS future contracts. This is possibly the first study that examines the linkages between EU ETS spot and future prices pertaining to the recent transition stage of phase III and the initial stage of ongoing phase IV of the ETS market. Our findings pinpoint to ETS markets becoming more complete and in turn offering optimal hedging avenues. JEL Codes: G15, C58, Q38 [ABSTRACT FROM AUTHOR]

This study is directed at gauging the equity price risk of the Indian commercial banks for the period 2003–2020. Parametric value-at-risk (VaR) is employed to estimate the downside risk. Further, the univariate exponential generalized auto regressive conditional heteroskedasticity (EGARCH) model is also used to find out the existence of stylised aspects of volatility. The outcomes point towards the existence of volatility clustering, persistence and asymmetry, but differ from bank to bank. Furthermore, the parametric VaR model that assumes normal distribution and student's t -distribution is not found to be an accurate model for all the banks. Tail risk is also found to be significant, and thus, justifies the Basel Committee's decision to shift towards an expected shortfall. However, these conventional VaR models should be supplemented by internal models, taking into consideration, bank-specific characteristics. JEL: G01, G15, I15, G17, G28 [ABSTRACT FROM AUTHOR]

The effect of real exchange rate volatility on real exports is explored empirically in this article. The study uses disaggregated Indian manufacturing sector data consisting of seven categories: leather & leather manufactures, chemicals and related products, engineering goods, electronic goods, textiles (excluding readymade garments), readymade garments and other manufactured goods. The analysis is focused on India's quarterly data from 2004Q2 to 2018Q2 using the ARDL bound test procedure. The ARDL bound test results show that real exports are co-integrated with fluctuations in real exchange rates and world real GDP. The study also reveals that exchange rate volatility has mixed effects on manufacturing exports both in the long run and short run. The impact of real-world GDP (WGDP) on real exports has been found positive and significant in the long run except for leather and leather manufacturers where it is negative and significant. In the case of the short-run, real-WGDP has mixed effects on exports. JEL Classification: C22, F14, F31, F1 [ABSTRACT FROM AUTHOR]

Migrant workforces and remittance inflow have drawn considerable attention in global economic research. During the past few decades, remittance inflow became a crucial economic growth component in many emerging nations. The current study, employing a time–frequency-based wavelet technique, investigates the dynamic causal relationship between migration, foreign remittance, and economic growth in an emerging country such as Bangladesh. The findings reveal the dynamic causal among the variables; however, the results present time-varying movement under the time–frequency spaces framework. Notably, the evidence shows a significant relationship among the variables in the long run. A strong correlation appeared on the 5–16 band scales among the pair of migration and foreign remittance (MIG-REM), migration and economic growth (MIG-GDP), and foreign remittance and economic growth (REM-GDP). The outcomes suggest that Bangladesh's economic indicators are highly interrelated in the long run. Therefore, the fluctuation of indicators altered and exposed a low volatility scale for the Bangladeshi economy in the long run. [ABSTRACT FROM AUTHOR]

This paper introduces a simple yet effective modification to bootstrap aggregation (bagging) and boosting techniques, aimed at addressing substantial errors arising from parameter estimation, particularly prevalent in macroeconomic and financial forecasting. We propose "egalitarian" bagging and boosting algorithms, where forecasts are derived through an equally weighted combination scheme following variable selection procedures, rather than relying on estimated model parameters. Our empirical work focuses on volatility forecasting, where our approach is applied to a hierarchical model that aggregates a diverse array of volatility components over different time intervals. Significant improvements in predictive accuracy are observed when conventional bagging and boosting approaches are replaced by their "egalitarian" counterparts, across a range of assets and forecast horizons. Notably, these improvements are most pronounced during periods of financial market turmoil, particularly for medium‐ to long‐term predictions. In contrast to boosting, which often yields a sparse model specification, bagging effectively leverages a diverse range of volatility cascades to capture rich information without succumbing to increasing estimation errors. The proposed "egalitarian" algorithm plays a crucial role in facilitating this process, contributing to the superior performance of bagging over other competing approaches. [ABSTRACT FROM AUTHOR]

This study examines the nexus between external shocks and micro-transmission aspects of volatility persistence and market efficiency for the Malawian Foreign exchange market. Garch-type models are employed on monthly data stretching back to June 2011 through to October 2021. Results of the study show the presence of two structural breaks for the volatility series and one structural break for the return series that fall within the rolling window for external shock associated with the Russia-Ukrainian war. The negative nature of the shock leads to a significant additive impact on volatility persistence, asymmetry, and threshold at different levels of significance in the three models of Garch, E-Garch and T-Garch respectively. Relaxing the models confirms the presence of domestic influence. However, the external influence remains significant especially for the negative shock. As such, the leverage and volatility feedback effects of external shocks are corroborated. Augmenting the approach with a FRUH analysis reveals that the foreign exchange market does not follow a random walk as it departs from the 1:1 relationship between the spot and forward rate thus being weak-form inefficient. [ABSTRACT FROM AUTHOR]

An increasing number of exchanges, mainly in the U.S., have adopted a commission structure called maker–taker fees in which traders placing limit orders (makers) are paid a rebate (negative trading commission) and traders placing market orders (takers) are charged a trading fee. The reason is that by paying rebates to makers, exchanges can expect to receive a large number of maker's orders and gain market share. Makers include arbitrageurs who make large transactions. Maker–taker fees constitute one of the most important commission structures for exchanges, because they are expected to attract arbitrageurs who are looking for rebate profits, on top of their trading profits. There have been many studies about arbitrage trading, but none we could find focused on the impact of arbitrage trading between markets with maker–taker fees where arbitrage traders place limit orders and markets without maker–taker fees where they place market orders. In this study, we investigated volatility and market liquidity by changing the amount of rebate under our proposed artificial markets, where there are or are not maker–taker fees. Then we checked the performance of arbitrage trading when the rebate increased. The results were that volatility in the market with maker–taker fees decreased and that in the market without maker–taker fees increased, and that market liquidity and arbitrage performance both increased in the market with maker–taker fees when rebates increased. The above results indicate that exchanges that operate markets adopting maker–taker fees can provide investors with more attractive markets than those that do not adopt them. However, if more arbitrageurs participate in the market with maker–taker fees to take advantage of these rebates, the cost burden on exchanges may increase unnecessarily. [ABSTRACT FROM AUTHOR]

This study aims to investigate the interaction between market volatility, economic growth, and cognitive biases over the period from April 2006 to March 2024. Market volatility and economic growth are critical indicators that influence economic stability and investment behavior. Financial market volatility, defined by abrupt and erratic changes in asset values, can have a big impact on the expansion and stability of the economy. According to conventional economic theory, there should be an inverse relationship between market volatility and economic growth since high volatility can discourage investment and erode trust. Market participants' cognitive biases are a major aspect that complicates this connection. Due to our innate susceptibility to cognitive biases, including herd mentality, overconfidence, and loss aversion, humans can make poor decisions and increase market volatility. These prejudices frequently cause investors to behave erratically and irrationally, departing from reasonable expectations and causing inefficiencies in the market. Cognitive biases have the capacity to sustain feedback loops, which heighten market turbulence and may hinder economic expansion. Similarly, cognitive biases have the potential to cause investors to misread economic indicators or ignore important details, which would increase volatility. This study uses the generalized autoregressive conditional heteroskedasticity (GARCH) model on GDP growth data from the US, the UK, and India, alongside S&P 500, FTSE 100, and NIFTY 50 data sourced from Bloomberg, to examine evidence of these biases. The results show evidence of the predictive nature of market fluctuations on economic performance across the markets and highlight the substantial effects of cognitive biases on market volatility, disregarding economic fundamentals and growth, emphasizing the necessity of considering psychological factors in financial market analyses and developing strategies to mitigate their adverse effects. [ABSTRACT FROM AUTHOR]

This study investigated the effects of cryptocurrency on investment portfolios through currency brokers at Yellow Card Company in Tanzania, focusing on price fluctuations, volatility metrics, and market swings. A cross-sectional research design was chosen to capture a snapshot of broker and investor experiences at a specific moment, allowing for a comprehensive analysis of current practices and attitudes. This design is particularly effective in understanding relationships between variables in a relatively short timeframe, making it ideal for exploring the dynamic cryptocurrency market. Utilizing a quantitative approach, data were collected from 43 brokers and investors through structured questionnaires, enabling statistical analysis and robust conclusions. Analysis conducted with IBM SPSS Version 26 revealed that while price fluctuations positively impacted broker returns, this effect was statistically insignificant. In contrast, high volatility metrics significantly negatively affected returns, while market swings exhibited a positive but insignificant effect. These findings underscore the substantial risks posed by cryptocurrency volatility, adversely impacting broker returns despite potential diversification benefits. The study concludes that effective risk management strategies are essential for mitigating these negative impacts. Recommendations for brokers and investors include the implementation of advanced risk management techniques and the establishment of regulatory frameworks to mitigate risks associated with cryptocurrency investments in emerging economies. This research enriches practical insights and emphasizes the significance of volatility in the management of investment portfolios in the context of cryptocurrency. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to the relationship between liquidity fluctuation trends and stocks' expected returns. Design/methodology/approach: The authors use the multi-fractal detrended fluctuation analysis to study the liquidity non-linear multi-fractal characteristics of stock market and predict the future volatility trend of market liquidity with the help of potential entropy dimensional model and also consider the non-linear volatility trend of liquidity, construct a fractal momentum strategy under different sorting periods and holding periods, to test the liquidity momentum effects of Chinese stock market and, finally, compare with the traditional price momentum strategy. Findings: The result suggests that: Chinese stock market liquidity has obvious non-linear multi-fractal characteristics; the trend entropy dimensional model can accurately predict the future volatility trend of market liquidity; significant liquidity trend momentum effects persist in the Chinese stock market, while price momentum effects exist only in the short term; a significant liquidity premium exists in the Chinese equity market; the fractal momentum strategy constructed in this paper achieves higher returns and less risk than the price momentum strategy. Originality/value: Based on the above analysis, this paper will further construct a momentum strategy incorporating liquidity trends, i.e. fractal momentum strategy (LMP strategy), with the realistic background that Chinese stock market liquidity has non-linear multi-fractal characteristics – the main contribution of this paper is to overcome the shortcomings of existing momentum strategies, which seldom combine the trend of future liquidity fluctuations with momentum strategies, and to incorporate the real characteristics of the market. [ABSTRACT FROM AUTHOR]

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This paper uses a state-dependent local projection model to empirically test the dynamic risk performance of cryptocurrency assets under geopolitical risk events, and to examine whether they have safe-haven properties in the face of major global external shocks. We demonstrate that the volatility of the cryptocurrency market exhibits a non-linear relationship with geopolitical risk. They are uncorrelated in normal times, but the risk of cryptocurrency market rises significantly under extreme geopolitical risk events. The cumulative impulse response pattern of volatility in the cryptocurrency market is similar to that of volatility in speculative assets such as stocks and bonds, but negatively correlated with that of volatility in safe-haven assets such as gold and the U.S. dollar. Our findings suggest that the volatility of cryptocurrencies should not be underestimated when investors consider hedging strategies under external shocks. [ABSTRACT FROM AUTHOR]

In this paper, we employ the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of state-level stock returns in the United States (US) based on structurally decomposed four monthly oil shocks associated with oil supply, global economic activity, oil consumption and oil inventory. We find that over the daily period of (February) 1994 to (December) 2022 and various forecast horizons, in 46 out of the 50 states, the GARCH-MIDAS model with at least one oil shock can outperform the benchmark, i.e., the GARCH-MIDAS-Realized Volatility (RV), with 24 states depicting the importance of all the four shocks. In general, oil market-specific shocks, whether supply or demand, tend to matter more than a global economic impact driving the oil market in forecasting volatility of regional stock returns across with better forecasting performances related to states with higher CO2 emissions based on underlying energy consumption data. Our findings have important implications for investors and policymakers, with the observations for the former group depicted by an analysis of economic significance, i.e., utility gains. [ABSTRACT FROM AUTHOR]

Political instability has increased drastically in Pakistan during the last few decades. This may intensify the fear of investors and eventually affect investment decisions. Therefore, the stock market's reaction to political stability must be explored and appropriate policy measures should be prescribed. This paper examines the effect of political stability on stock market returns and volatility using time series data from Pakistan for the period from January 02, 2004, to May 31, 2018. The study uses the Generalized Autoregressive Conditional Heteroskedastic (GARCH) and Exponential Generalized Autoregressive Conditional Heteroskedastic (EGARCH) models to achieve the main objectives. The findings of the GARCH and EGARCH models confirm that political stability has a positive and significant influence on both stock market returns and volatility in Pakistan. In addition, the EGARCH findings indicate that negative shock (political instability) creates more volatility in the stock market than positive shock (political stability). The results of this study reveal that financial market investors are affected more by political instability. In conclusion, this study suggests that political stability is an essential factor affecting financial market volatility, and it leads to stable financial markets. It also emphasizes that political instability needs to be resolved to achieve stable financial markets. [ABSTRACT FROM AUTHOR]

We study firms' return and volatility connectedness in the stock and corporate bond markets. Our approach to capturing firm‐specific return and volatility time series in the corporate bond market is based on a repeat‐sales index at the firm level. Measuring the pairwise connectedness of firms, we show that the two markets share similar dynamics in the connectedness of their firms. Firms tend to cluster within their own sectors and ties between firms in the corporate bond market are proportionally weaker. Financial firms play a critical role in the propagation of shocks, but this role differs markedly in the two markets. [ABSTRACT FROM AUTHOR]

Using the Self-exciting Threshold Autoregressive Model (SETAR_M) and linear models such as the vector error correction model (VECM), and univariate models, this article specifies forecasting models for exchange rate volatilities in Ghana and compares their forecasts accuracy using Diebold–Mariano and Pesaran-Timmermann tests statistics. The relevance of this research is to equip business owners and businesses on managing forex losses and to reduce their impact on profits, productivity and employment in high volatile and unstable currency environments. The research concludes that the non-linear SETAR model is superior to the linear models in predicting short-term volatilities in exchange rates, while the fundamentally based linear model is superior for predicting long-term volatility in exchange rates. Therefore, short-term business commitments or transactions such as raw material purchases, cash expenses or incomes in foreign currencies should be planned or managed using SETAR or a non-linear model, whereas long-term contractual obligations like futures and forward contracts should be planned with a fundamentally based multivariate linear model. [ABSTRACT FROM AUTHOR]

This study examines corporate pension funds' search for yield through investments in private equity (PE). Using pension asset allocation data from 10‐K filings, we find that corporate pension funds significantly increase the PE share within their risky assets following underperformance relative to their expected return. This risk‐taking behavior is more pronounced for sponsoring firms that were more financially constrained with poorly funded plans in the previous year. Furthermore, we discover that pension asset allocation to PE does not significantly increase pension returns but does lower pension return volatility and tracking error relative to the expected return. [ABSTRACT FROM AUTHOR]

This paper studies the pricing of the risk associated with the location of the assets. The local real estate market risk is measured by 'local beta', which combines the systematic risk of local property markets and the property allocation strategy of real estate firms. The empirical results confirm a higher equity return for a firm with higher exposure to the most volatile property markets, particularly for REITs which are more geographically concentrated. For REITs with highly diversified assets, local real estate risks are not reflected in REIT returns. For those REITs with most concentrated assets, a one standard deviation increase in the local beta will lead to a 4.7% increase in the annual return. Investors can use REITs' local real estate risk as an information tool to construct a long-short investment portfolio of real estate firms and can achieve a significant non-market performance of 4.9% per annum. [ABSTRACT FROM AUTHOR]

In this paper, we empirically examine the relationship between the novel macroeconomic attention indices (MAI) and commodity market volatility. In-sample analysis indicates that MAI contribute significantly to the volatility fluctuations in commodity markets. In addition, we employ dimension reduction techniques, shrinkage methods, and combination models in an out-of-sample exercise to assess the predictive ability of MAI, alongside a variety of economic predictors including uncertainty measures, and global as well as US economic indicators. Our empirical results demonstrate the superior predictive ability of the elastic net and LASSO models incorporating MAI together with macroeconomic and uncertainty indicators. This empirical finding is reinforced through a series of robustness checks. However, dimension reduction methods exhibit superior performance in longer forecast horizons. Finally, MAI are more informative for commodity volatility forecasting during economic expansions and non-crisis periods. Our study offers new insights on commodity volatility forecasting. [ABSTRACT FROM AUTHOR]

Most existing studies on volatility forecasting have focused on interday characteristics and ignored intraday characteristics of high‐frequency data, especially the asymmetric impact of positive and negative jumps on volatility. In this paper, 5‐min high‐frequency data are used to construct realized volatility which is decomposed into continuous components and jump components with positive and negative directions. Then, this information is combined with the long short‐term memory model for the realized volatility prediction. The empirical analysis demonstrates that negative jumps resulting from negative news have a more significant impact on market volatility than positive jumps. Additionally, the long short‐term memory model, which incorporates positive and negative jump volatility, outperforms traditional econometric and machine learning models in predicting out‐of‐sample volatility. Furthermore, applying the prediction results to value at risk yields a better measurement effect than the generalized autoregressive conditional heteroskedasticity model. [ABSTRACT FROM AUTHOR]

Risk parity portfolio optimization, using expected shortfall as the risk measure, is investigated when asset returns are fat‐tailed and heteroscedastic with regime switching dynamic correlations. The conditional return distribution is modeled by an elliptical multi‐variate generalized hyperbolic distribution, allowing for fast parameter estimation via an expectation‐maximization algorithm, and a semi‐closed form of the risk contributions. A new method for efficient computation of non‐Gaussian risk parity weights sidesteps the need for numerical simulations or Cornish–Fisher‐type approximations. Accounting for fat‐tailed returns, the risk parity allocation is less sensitive to volatility shocks, thereby generating lower portfolio turnover, in particular during market turmoils such as the global financial crisis or the COVID shock. While risk parity portfolios are rather robust to the misuse of the Gaussian distribution, a sophisticated time series model can improve risk‐adjusted returns, strongly reduces drawdowns during periods of market stress and enables to use a holistic risk model for portfolio and risk management. [ABSTRACT FROM AUTHOR]

In this paper, we explore the applications of fractional stochastic volatility (FSV) models within the realm of market microstructure theory and optimal execution strategies. FSV models extend traditional stochastic volatility frameworks by incorporating fractional differentiation, allowing for more flexible and realistic representations of asset price dynamics over time. Our investigation begins with an introduction to FSV models, highlighting their ability to capture long-memory effects and volatility clustering observed in financial markets. These models provide a robust framework for understanding market microstructure dynamics, including order flow behavior, price impact functions, and liquidity provision mechanisms. Furthermore, we discuss recent advancements and empirical findings using FSV models, emphasizing their role in uncovering intraday volatility patterns and their implications for trading strategies under varying market conditions. By incorporating these nuanced volatility dynamics, FSV models contribute to the development of optimal execution algorithms that enhance transaction cost efficiency and market stability. The FSV model, when the Hurst exponent H is set to 0.5, effectively reduces to a standard stochastic volatility model. THis nested relationship can be formally demonstrated by considering the FSV model's general form and showing that for H = 0.5, the fractional Brownian motion BH(t) becomes a standard Brownian motion Wt, thereby aligning the FSV model with traditional models. Overall, our analysis underscores the significance of FSV models in advancing both theoretical insights and practical applications in modern finance, offering new avenues for research in high-frequency trading strategies and market efficiency. [ABSTRACT FROM AUTHOR]

This study explores the impact of Climate Policy Uncertainty (CPU) on real estate market volatility, utilizing the CPU index to assess how climate policy affects various real estate segments. It highlights the significant impact of CPU on sectors with high energy demand and emissions, such as industrial and residential. A multi-horizon analysis reveals the long-term sensitivity of CPU's influence, with significant sensitivity noted in coastal regions prone to climate risks. The findings provide crucial insights for investors and policymakers, emphasizing the importance of integrating CPU considerations into strategic decision-making for real estate investment. [ABSTRACT FROM AUTHOR]

Data-driven smart investment decisions are important for financial development, which has not received much attention from academia. As a result, this paper resorts to the evolutionary game theory, and proposes a novel multi-agent financial investment decision method. Specifically, an evolutionary game theory-based decision-making approach is formulated as the main model for the research purpose. By considering the strategic choices and adaptability among various entities, a comprehensive analysis of the behavior and decision-making process of entities in the financial market is achieved. This paper combines stock exchanges and financial data providers (Bloomberg and Thomson Reuters) to conduct case studies on this method, verifying its effectiveness and feasibility in practical applications. By comparing traditional financial investment decision-making methods, it can be seen that the proposal has significant advantages in improving investment efficiency, reducing risks, and responding to market volatility. This paper delves into the multi-agent financial investment decision-making method based on the evolutionary game, providing new ideas and methods for academic research and practical applications in the financial field. [ABSTRACT FROM AUTHOR]

The realized recurrent conditional heteroscedasticity (RealRECH) model improves volatility prediction by integrating long short-term memory (LSTM), a recurrent neural network unit, into the realized generalized autoregressive conditional heteroskedasticity (RealGARCH) model. However, at present, there is no literature on the ability of the RealRECH model to fit and predict volatility in the Chinese market. In this paper, a study is conducted to test the in-sample explainability and out-of-sample prediction ability of the RealRECH model for the SSE50, CSI300, CSI500 and CSI1000 indices in the Chinese market and to determine whether it performs better than the RealGARCH model. The results of the in-sample analysis show that the RealRECH model not only provides better in-sample interpretability for all four indices but also captures the complex dynamics of time series volatility that the RealGARCH model cannot capture, such as long-term dependence and nonlinearity. The results of out-of-sample volatility prediction show that the RealRECH model better predicts the volatility of the CSI500 and CSI1000 indices but yields worse predictions for the SSE50 and CSI300 indices. Thus, the RealRECH model can be used for CSI500 and CSI1000 prediction. [ABSTRACT FROM AUTHOR]

We propose scale mixtures of Birnbaum-Saunders distributions as a new class of positive skewed and leptokurtic distributions and use it to model volatility in stock markets. To estimate the model parameters, we develop an Expectation-Conditional-Maximization algorithm. The numerical performance of the proposed methodology is evaluated by means of Monte Carlo simulations. Application of the new model in volatility modeling is illustrated with some real-life data. [ABSTRACT FROM AUTHOR]

An important but rarely-addressed option pricing question is how to choose appropriate strikes for implied volatility inputs when pricing more exotic multi-asset derivatives. By means of Malliavin calculus, we construct an asymptotically optimal log-linear strike convention for exchange options under stochastic volatility models. This novel approach allows us to minimize the difference between the corresponding Margrabe computed price and the true option price. We show that this optimal convention does not depend on the specific stochastic volatility model chosen and, furthermore, that parameter estimation can be dramatically simplified by using market observables as inputs. Numerical examples are given that provide strong support for the new methodology. [ABSTRACT FROM AUTHOR]

This study compares the inter- and intra-metropolitan patterns of housing return volatility in four market phases (boom, bust, recovery, and pandemic) in the United States between 2000 and 2022. The study finds a weak correlation between the physical characteristics of neighborhoods and housing market volatility. Notably, higher-density neighborhoods show greater market volatility throughout the boom, bust, and recovery phases but the trend is reversed during the pandemic period. However, the sociodemographic composition of neighborhoods exhibits a stronger correlation with market volatility. Specifically, housing markets in Black neighborhoods demonstrate persistently higher volatility, irrespective of market fluctuations. Similarly, low-income neighborhoods are consistently associated with greater volatility before the pandemic. The study does not find a consistent association between housing supply and market volatility across the four market phases. It shows that during the 2008 housing downturn, housing markets were less volatile in metropolitan areas with stricter land use regulations, less developable land, fewer home constructions, and better natural amenities. [ABSTRACT FROM AUTHOR]