Market Scenario Research Articles

Numerical solutions of PDEs for corporate bond pricing: A computational finance approach

Abstract. In this paper, we will see how one can use numerical algorithms to solve PDEs, containing partial derivative terms, for corporate bond pricing. We utilise the Black-Scholes framework which involves adding stochastic interest rates and credit risk into a PDE model. Then, we will take a quick look at some different numerical methods to solve the PDE model, like finite difference methods (FDM), finite element methods (FEM) as well as standard Monte Carlo (MC) simulations. Above all, we provide some simulations to show how well they work for PDEs, and how much time it takes. We would like to highlight that numerical methods lead to more accurate and efficient bond pricing with these methods, and also that we can analyse together how well each method works under different market scenarios. The paper will end with future directions for numerical methods, which should allow including more sophisticated algorithms and machine-learning techniques that improve scalability and can be used in a real-time manner in financial markets.

The CFO financial performance and the financial market scenario: A Review

This article introduces a literature review on the CFO's financial performance in the ever-challenging Brazilian financial market scenario. Key findings pointed out that the CFO of unlisted companies shows a lower inclination to estimate the cost of equity. The study also highlighted the need for technical and organizational solutions to agency problems, such as sound corporate governance practices, computer-aided accounting systems, incentive-based compensation, clear performance goals, and delegation of operational decisions. The study also examined the CFO's perspective and the companies' strategic ecosystem, finding that duality in the roles can negatively impact financial reporting quality.

AI-ENHANCED PORTFOLIO MANAGEMENT: CRAFTING OPTIMAL INVESTMENT STRATEGIES FOR DIVERSE FINANCIAL MARKETS

This study investigates the transformative potential of artificial intelligence (AI) in portfolio management across diverse financial markets. It aims to develop a comprehensive framework that enhances investment strategies in developed, emerging, and frontier markets through AI techniques. The research will employ a mixed-methods approach, combining quantitative and qualitative methodologies. Initially, extensive secondary data will be collected from market reports, financial databases, and previous studies. This data will encompass market-specific characteristics, AI-based portfolio strategies, and financial indicators. AI methods, including neural networks, machine learning models, and natural language processing applications, will then be utilized to analyze portfolio performance across various market scenarios. The anticipated outcome of this study is a versatile AI-driven framework that accommodates the unique aspects of different financial markets. By optimizing asset allocation, risk management, and predictive analytics, this framework will provide portfolio managers with evidence-based recommendations to enhance the stability and performance of their investments. The study aims to demonstrate how AI can mitigate risks associated with market volatility, liquidity issues, and regulatory constraints, leading to a more resilient and data-driven approach to portfolio management on a global scale KEYWORDS: AI integration in finance, Portfolio performance metrics, Compounded Annual Growth Rate (CAGR), Volatility reduction

Optimised scheduling of a cogenerative subnetwork based on a micro gas turbine and thermal storage with the addition of an innovative solar assisted heat pump and Ni-Zn battery

The Innovative Energy Systems (IES) laboratory at the University of Genoa features a plant configuration comprising a micro gas turbine, latent heat thermal energy storage, an innovative heat pump system connected to solar façade panels, and a NiZn battery. This study presents the optimization of four distinct sub-plant configurations, focusing on their economic and environmental performance across different seasons (January, April, July, and October) under two market scenarios (no selling price or selling price equal to buying price). A genetic algorithm-based tool is developed for the optimized energy scheduling of these configurations, taking into account the operational characteristics of programmable, non-programmable energy sources and energy storage devices. The analysis highlighted that when the selling price is equal to zero, the system is optimised to improve sell-consumption. The addition of the battery or the heat pump to the system always lead to reduction of operational costs compared to the baseline case with only the micro gas turbine and thermal energy storage. Notably, the heat pump alone provides greater cost benefits than the battery, although the combined use of both systems yields the highest cost reductions ranging, depending on the month, up to −16.9% in the “no selling” scenario and up to −12.3% when selling and buying prices are equal. Regarding the CO2 emissions, both components lead to an emission reduction in the “no selling scenario” while only the HP guarantees an emission reduction during the “selling equal to buying” scenario, in both cases up to −20.5% less. This analysis highlights the economic and environmental advantages of integrating NiZn battery storage and a solar-assisted heat pump into the energy system, demonstrating cost savings and emission reductions across various market conditions and seasonal demands.

Scenario-Based Analysis of Electrification Effects on Value Creation and Employment Structures for the Automotive Industry in the Federal State of Baden-Wuerttemberg, Germany

The transformation path to electric mobility will have fundamental impacts on the existing value chain and employment structures in the automotive industry. The purpose of this paper is to derive and examine these effects based on two different electric mobility market scenarios for the European (EU27) passenger car as well as truck market 2040 with special focus on the highly export-oriented industrial automotive cluster in Baden-Wuerttemberg. To achieve this, both a moderate and a progressive market scenario were simulated using the scientifically validated DLR VECTOR21 vehicle technology scenario model, based on two different parameter sets derived from actual and possible framework conditions on the European car and truck markets. Based on a detailed analysis of the industrial branch, value creation, and employee structure in Baden-Wuerttemberg and its automotive cluster, the effects resulting from the transformation to electric mobility will be displayed. With detailed Fade-In and Fade-Out analysis, the shifts from internal combustion engine components to electrified components will be derived and illustrated for each employee segment in the automotive cluster in Baden-Wuerttemberg, which leads to completely new and original results at this level of detail. Furthermore, the detailed display of the automotive cluster in this study allows for regionalized statements on employment effects, considering, for the first time, not only the car but also the truck segment. The analysis shows that battery electric vehicles will achieve a share of 34% or 57% for new registrations on the German car market in 2030, depending on the scenario framework conditions. The resulting employment effects for the entire automotive cluster in Baden-Wuerttemberg reach −37,000 (−8%) or −66,000 (−14%) by 2030 with further negative development until 2040 (−155,000, −30%) for the respective scenarios. Employment segments, in particular powertrain-dependent production employees, are at risk, with a potential decline of up to −60%. R&D employees could be also be significantly, affected with a reduction in workforce of about −50%.

Technological progress: Interconnectedness, innovation systems, and technology delivery systems the case of Indian electric vehicles

The electric vehicle (EV) technological supply chain progresses when demand (pull) aligns with supply (push) to perform as a technology delivery system. Actors, institutions, and networks interconnect the technological supply chain with innovation as responses to external pressures to produce and deliver technology products/services. The literature separately examined the progress from a delivery perspective (linking the demand and supply network) and a systems perspective that examines technological change as a socio-technical system. Technological progress requires changes aligning push and pull or one feeding into the other. The ‘motors of innovation’ that shape the innovation system are used in systems dynamics modeling linking technological changes and progress. This study uses ‘motors of innovation’ to combine the two perspectives and uses interconnectedness to examine the alignment of push and pull and the resulting delivery phenomena. The phenomena require studying it in its settings with diverse heuristic and analytic views. Therefore, this study uses case study methodology and selects multiple Indian EV cases comprising all vehicle segments in a government direct market scenario. It makes two contributions to technological progress: one defines the combined delivery and systems perspective and includes interconnectedness. The other is moving knowledge to a broad scope and expertise and striving for interconnectedness with other systems. Further insights are possible with cases that display better technological progress, as findings noted poor interconnectedness with limited technological capability and infrastructure.

Optimal allocation for stock market-excluded retirees: Effects of interest rates, longevity risk, and upfront fees

This paper studies the decision-making dynamics of stock market-excluded retirees, focusing on the optimal allocation of retirement wealth among consumption, risk-free assets, and single premium immediate annuities. The analysis centers on two pivotal factors: insurer interest rates and longevity risk. The study introduces new dimensions by highlighting the modeled financial environment’s optimal investment–consumption-annuitization profiles under different market scenarios. Additionally, insights on the low demand for life annuities are offered, attributing a significant part to the impact of upfront fees. Employing a life-cycle consumption framework, the paper advocates adopting a commission-free model, enabling registered investment advisors to market annuities with periodic assets under management fees to revitalize annuity demand and bolster retirement security.

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

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

Sustainable Decarbonization of Road Transport: Policies, Current Status, and Challenges of Electric Vehicles

Carbon dioxide (CO2) and other greenhouse gases (GHGs) are the primary causes behind the current rise in global temperatures and climate change. CO2, which is present in the atmosphere in significant quantities, is contributing more to the warming effect than other gases that are present at smaller levels. The transportation sector is contributing significantly to the total global CO2 emissions (21.7%), with road transport accounting for 74%. Switching from gasoline-powered vehicles to electric vehicles (EVs) is the most practical way to decarbonize land transportation. We have gathered information on EVs from the USA, China, India, and a few EU members for the current review. The collected data regarding government policies, subsidies, and other rules and laws, the present status of EV technology and performance, market scenarios and future projections of EVs, EV regulations in the regions with the highest penetration rates, consumer perspectives and barriers, and the decarbonization of land transportation were analyzed. The adoption of electric vehicles has advanced significantly in some parts of the world, primarily as a result of government policies that include subsidies, other regulations, and laws that globally lower the cost of acquisition and ownership for owners of electric vehicles (EVs) relative to those of internal combustion engines (ICEs). Favorable government policies have been associated with the success of EV adoption in Norway, China, the United States of America (USA), the European Union (EU), and India. With more advantages than any other country, China is currently the world’s largest EV market. In addition, it has the highest number of EVs in use and the highest manufacturing capacity. Norway leads the world in the percentage of newly registered EVs at over 90%. China is competing with the USA for leadership in manufacturing and technology, especially in the areas of artificial intelligence (AI) and batteries. Favorable perceptions of consumers regarding EVs are vital in increasing the EV adoption rate. In order to achieve the goal of decarbonizing road transportation, we proposed that a number of factors—including government energy policies and regulatory standard development, necessary infrastructure investments and developments, AI incorporation in EVs, innovation in EV and battery technologies, and consumer perspectives in particular—play a crucial role.

Enhanced stock market forecasting using dandelion optimization-driven 3D-CNN-GRU classification

The global interest in market prediction has driven the adoption of advanced technologies beyond traditional statistical models. This paper explores the use of machine learning and deep learning techniques for stock market forecasting. We propose a comprehensive approach that includes efficient feature selection, data preprocessing, and classification methodologies. The wavelet transform method is employed for data cleaning and noise reduction. Feature selection is optimized using the Dandelion Optimization Algorithm (DOA), identifying the most relevant input features. A novel hybrid model, 3D-CNN-GRU, integrating a 3D convolutional neural network with a gated recurrent unit, is developed for stock market data analysis. Hyperparameter tuning is facilitated by the Blood Coagulation Algorithm (BCA), enhancing model performance. Our methodology achieves a remarkable prediction accuracy of 99.14%, demonstrating robustness and efficacy in stock market forecasting applications. While our model shows significant promise, it is limited by the scope of the dataset, which includes only the Nifty 50 index. Broader implications of this work suggest that incorporating additional datasets and exploring different market scenarios could further validate and enhance the model's applicability. Future research could focus on implementing this approach in varied financial contexts to ensure robustness and generalizability.

Receiver or transmitter? Unlocking the role of green technology innovation in sustainable development, energy, and carbon markets

In the context of sustainable development, the energy crisis and the volatile carbon trading markets have made the integration of green technology increasingly essential. This study explores the spillover effects between green technology innovation (GTI), oil price, clean energy (CE), carbon emissions trading prices (CETP), and sustainable development (SD) using quantile connectedness. The static analysis reveals that the connectedness relationship in extreme quantiles is remarkably more related than the middle quantile. The dynamic results indicate that major events significantly strengthen the overall risk correlation, but spillover effects are asymmetric under different quantiles. Further analysis under extreme market conditions demonstrates that SD and CE have produced a continuous and steady risk spillover to GTI, while CETP increased the risk transmission to GTI following the COVID-19 pandemic. This research underscores the significance of coordinating the development of GTI, energy, carbon markets, and SD by investigating risk contagion mechanisms in various market scenarios. In terms of policy implications, the findings indicate that stable energy system and reasonable carbon prices can stimulate the advancement of GTI.

Asymmetric spillovers and resilience in physical and financial assets amid climate policy uncertainties: Evidence from China

Climate policy is essential for fostering sustainable development and addressing climate-related issues, while climate policy uncertainty (CPU) introduces complex challenges in providing a consistent framework for action. In this study, we delve into the spillover dynamics among China's domestic CPU, foreign CPU, physical assets, and financial assets across various market scenarios, through a quantile-based connectedness method and evidence from China. The findings reveal: (1) The spillover dynamics are asymmetric across quantiles and time, and the COVID-19 pandemic influences the total connectedness. (2) The complexity of spillover patterns in physical assets is likely contingent upon market conditions, policy sources, asset types, and industrial-chain positions, while the spillovers of financial assets are quantile-dependent and time-varying, with the agricultural equity market showing relative resilience to the pandemic. (3) There is no significant Granger causality in quantiles between domestic CPU and foreign CPU under the entire sample period, and the rankings regarding the spillovers from CPUs to assets vary across market conditions. Implications for policymakers, investors, and firms are provided.

Enhancing natural carbon sinks: Simulation of land use change under different carbon market scenarios by developing an ANN-ABM model

The carbon market mechanism presents an innovative approach to achieving carbon neutrality, however, its impact on regional carbon sink enhancement remains uncertain. This paper proposes an analysis framework based on an Artificial Neural Network-Agent-Based Model (ANN-ABM) to simulate the potential contribution of carbon market mechanism in enhancing carbon sinks. Taking the case of Chongming, China, the results demonstrate significant economic potential with forest land expansion from 2010 to 2020, equivalent to 30.69 % of eco-compensation. Under scenarios introducing a carbon market, there is an increased probability of converting land to high carbon sink types. Compared to the baseline scenario, carbon prices at 1.34 US$/t and 6.27 US$/t result in additional funds of 8.94 % and 41.46 %, respectively, and increase the carbon sink by 12.52 % and 30.28 %. The findings contribute to an understanding of how the value of carbon sinks can be realized through market mechanism and offer guidance for innovative eco-compensation approaches.

Environmental potentials from wood cascading: A future-oriented consequential yet dynamic approach considering market and time-dependent biogenic carbon effects for selected scenarios under German conditions

As sustainable forestry limits the roundwood supply, wood cascading emerges as a promising concept to meet the growing demand resulting from Germany's transition to a bioeconomy. To assess the environmental impacts of wood cascading resulting from shifting the incineration of recovered wood and the associated substitution of future energy mixes and materials, a consequential life cycle assessment (CLCA) of a wood cascading system providing glued-laminated timber (GLT), particleboard, heat, and electricity is conducted. The assessment of environmental consequences requires a holistic approach, including future-oriented German energy and market scenarios. Furthermore, to analyze the impact of biogenic carbon dynamics, this CLCA was coupled with a dynamic life cycle assessment (DLCA) considering forest growth scenarios and temporal aspects. The results indicate a strong influence of market shifts related to material substitution, followed by energy substitution on the environmental impacts of wood cascading. In fact, the results endorse the implementation of the concept of high-quality wood cascading for substituting non-wood products in Germany, as a transformational path towards a bioeconomy and the achievement of net greenhouse gas neutrality. Compared to the effects of the material and energy substitution scenarios, the forest growth scenarios, which focus on tree species composition influenced by future temperature change and CO2 concentration scenarios, only show a minor influence on global warming impacts. As the findings from applying DLCA contrast with the static approach, it emphasizes the importance of a time-differentiated analysis of biogenic carbon in the evaluation of wood cascading.

Market Scenario of Medicinal Plants: Understanding the Condition during COVID-19

Market Scenario of Medicinal Plants: Understanding the Condition during COVID-19

Monetary non-neutrality in a multisector economy: The role of risk-sharing

We show that the degree of risk-sharing among heterogeneous workers is a key determinant of monetary non-neutrality in a multisector sticky-price model. In this framework, workers are employed across different sectors, earning distinct wages. The limited ability of workers to fully insure against labor income risks results in strategic complementarity in firms' price-setting decisions with respect to aggregate shocks and strategic substitutability with respect to idiosyncratic shocks. These pricing interactions lead to sluggish adjustments of the price level in response to monetary and other aggregate shocks, causing significant fluctuations in the output gap while maintaining large responses of individual prices to idiosyncratic shocks. We illustrate these results across three stylized asset market scenarios: complete markets, non-contingent bond-only markets, and financial autarky.

Anticipatory Technoeconomic Evaluation of Kentucky Bluegrass-Based Perennial Groundcover Implementations in Large-Scale Midwestern US Corn Production Systems

Perennial groundcover (PGC) has promise as a scalable approach to generating natural resource benefits and sustainable biofuel feedstock while preserving the high yields of annual row crop production. Partnering row crops with temporally and spatially complementary low-growing, shallow-rooted perennials, such as Kentucky bluegrass (KBG) (Poa pratensis L.), is one example of an emerging PGC system. PGC’s ecosystem benefits can only be fully realized if commercial-scale adoption occurs, which hinges on its economic feasibility. This paper utilizes an enterprise budget framework to detail and compare the expected cost and revenue of establishing and maintaining PGC in row crop systems with standard continuous corn (SCC) (Zea mays L.) production, including stover harvest, but excluding economic incentives for ecosystem services. Optimistic and pessimistic assumptions were used, along with Monte Carlo simulation, to characterize the uncertainty in results. In the optimistic stover market scenario, Year 1 net returns for PGC averaged USD 84/ac less than for SCC; Year 2+ net returns averaged USD 83/ac more, meaning that cost parity with SCC occurs by the second PGC system year. Without stover revenue, parity is achieved after five years. These results affirm that PGC’s economic viability is critically impacted by a groundcover’s lifespan, the yield parity with SCC, and the availability of a stover market.

Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review

Traditional mean-variance (MV) models, considered effective in stable conditions, often prove inadequate in uncertain market scenarios. Therefore, there is a need for more robust and better portfolio optimization methods to handle the fluctuations and uncertainties in asset returns and covariances. This study aims to perform a Systematic Literature Review (SLR) on robust portfolio mean-variance (RPMV) in stock investment utilizing genetic algorithms (GAs). The SLR covered studies from 1995 to 2024, allowing a thorough analysis of the evolution and effectiveness of robust portfolio optimization methods over time. The method used to conduct the SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The result of the SLR presented a novel strategy to combine robust optimization methods and a GA in order to enhance RPMV. The uncertainty parameters, cardinality constraints, optimization constraints, risk-aversion parameters, robust covariance estimators, relative and absolute robustness, and parameters adopted were unable to develop portfolios capable of maintaining performance despite market uncertainties. This led to the inclusion of GAs to solve the complex optimization problems associated with RPMV efficiently, as well as fine-tuning parameters to improve solution accuracy. In three papers, the empirical validation of the results was conducted using historical data from different global capital markets such as Hang Seng (Hong Kong), Data Analysis Expressions (DAX) 100 (Germany), the Financial Times Stock Exchange (FTSE) 100 (U.K.), S&P 100 (USA), Nikkei 225 (Japan), and the Indonesia Stock Exchange (IDX), and the results showed that the RPMV model optimized with a GA was more stable and provided higher returns compared with traditional MV models. Furthermore, the proposed method effectively mitigated market uncertainties, making it a valuable tool for investors aiming to optimize portfolios under uncertain conditions. The implications of this study relate to handling uncertainty in asset returns, dynamic portfolio parameters, and the effectiveness of GAs in solving portfolio optimization problems under uncertainty, providing near-optimal solutions with relatively lower computational time.

The enteric methane emission conundrum: U.S. beef cattle producer adoption of climate-focused technology

Cattle are ruminant livestock that emit enteric methane (CH4) as part of their natural digestive process. The U.S. beef cattle industry is receiving pressure to reduce greenhouse gas emissions, including methane. The U.S. Roundtable for Sustainable Beef has set a target for the U.S. feedlot sector to reduce emissions by 10 % per pound of beef by 2030. Feed additive 3-Nitrooxypropanol (3-NOP) has been developed to help mitigate methane emissions. While not yet approved for use in U.S. beef production, adoption of 3-NOP in U.S. feedlots upon approval remains unknown as no widespread economic incentive currently exists in the marketplace to spur adoption. The objectives of this study are: (1) to determine potential 3-NOP adoption by U.S. feedlot cattle producers given various marketplace incentives and (2) to explore how differing approaches to reducing emissions from beef production to achieve sector targets impact social welfare. Our study uses data from a U.S. feedlot producer survey to estimate willingness-to-adopt (WTA) measures. Regression results are used to map potential adoption of 3-NOP given various market and policy scenarios. The survey sample is then split into small producers (<2000 head sold in last 12 months) and large producers (2000+ head sold in last 12 months) to determine differences in WTA based on operation size. We find that producers prefer incentives in the form of processor premiums over government subsidies. The incentive level needed to spur adoption increases as the implementation cost of 3-NOP increases and decreases if net profit estimations are included in the messaging. On average, small producers require a higher incentive to adopt 3-NOP than large producers. Improving the emissions reduction efficacy of 3-NOP reduces the level of incentive needed to achieve aggregate emissions targets. The least expensive avenue to achieve emissions reduction targets results in greater outlays to large producers as compared to small producers. The marginal cost to society of feeding 3-NOP to an additional steer or heifer in the feedlot increases with each animal. As such, it may be that improving the efficacy of 3-NOP through increased investment in research and development is less costly than spurring more producers to adopt the additive in their feed rations. Ultimately, producers, processors, beef consumers, voting residents, taxpayers, and policymakers all have influence in shaping how the beef industry tackles the emissions reduction conundrum.

Green hydrogen and wind synergy: Assessing economic benefits and optimal operational strategies

Volatile electricity prices have raised concerns about the economic feasibility of wind projects in Finland. This study assesses the economic viability and optimal operational strategies for integrating wind-powered green hydrogen production systems. Utilizing modeling and optimization, this research evaluates various wind farms in Western Finland over electricity market scenarios from 2019 to 2022, with forecasts extending to 2030. Key economic metrics considered include internal rate of return, future value, net present value (NPV), and the levelized cost of hydrogen (LCOH). Results indicate that integration of hydrogen production with wind farms shows economic benefits over standalone wind projects, potentially reducing LCOH to €2.0/kgH2 by 2030 in regular and low electricity price scenarios, and to as low as €0.6/kgH2 in high-price scenarios. The wind farm with the highest capacity factor achieves 47% reductions in LCOH and 22% increases in NPV, underscoring the importance of strategic site selection and operational flexibility.