Pricing Catastrophe Options with Credit Risk in a Regime-Switching Model

In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.

From Finnish Assortment Pricing to Market Economy Using Prices for Sawn Wood and Chips in Reference Bucking

Dominant Finnish assortment pricing gives prices for sawlog and pulp wood volumes. Buyers buck stems to sawlogs using secret price matrices. Agreed dimensions allow wide range of sawlog volumes. Forest owners cannot objectively compare biddings: timber trade is a lottery game. Bucking is analyzed in terms of sawlog, pulp wood, log cylinder, sawn wood, value-weighted sawn wood, and chips. Sawn wood and its value are computed from top diameter of the sawlog. Profit maximization requires buyers to buck logs producing smaller than maximal value, causing dead weight loss. Nominal assortment prices have unpredictable relation to effective stumpage price. Assortment pricing does not meet requirements of market economy. If sawmills linked to pulp mills buck smaller sawlog percentages than independent sawmills, as generally believed, they use higher price for chips in their own harvests than they pay for independent sawmills, indicating imperfect competition for chips. Sawn wood potential pricing is suggested which gives prices for sawn wood and chips coming both from sawlogs and pulp wood in reference bucking which maximizes sawn wood for given minimum and maximum log length and minimum top diameter. Simple algorithm generates feasible bucking schedules from which optimum can be selected using any objective. Pricing produces unit price for all commercial wood utilizing ratio of theoretical sawn wood and commercial volume in stand. Unit price can be compared to stem pricing and could be compared to assortment pricing if assortment pricing would produce predictable sawlog percentages. Sawn wood potential pricing is concrete, transparent, easy to compute, considers stem size and tapering, reduces trading cost and is less risky to buyers than stem pricing. It meets requirements of market economy. Readers can repeat computations using open-source software Jlp22.

弹性退休制度下谁更愿意延迟退休?——基于Option Value模型的微观模拟

人口老龄化背景下延迟退休年龄、建立弹性退休制度是大势所趋。养老金激励是弹性退休制度的重要内容。建立期权价值模型和养老金给付及奖惩因子模型,基于中国家庭收入调查项目(CHIP2018)的数据,对不同特征人群的养老金峰值、期权价值、内部报酬率进行模拟。研究发现:养老金总财富随退休年龄“先增后减”,男性的峰值年龄早于女性;引入养老金“奖惩”机制有助于提高最优退休年龄,激励劳动者延迟退休;考虑闲暇偏好的异质性,男性参保者更倾向于早退休,而女性参保者特别是女性较高收入群体更愿意延迟退休;厌恶风险的参保者更有可能选择早退休。建议尽早建立弹性退休年龄政策体系,增加劳动者的选择权和制度灵活性;引入精算调节因子构建养老金奖惩机制,完善养老保险待遇计发办法。

基于IUV_5G Option3X架构网络规划与部署设计

本文基于IUV_5G Option3X架构,详细阐述了5G网络的规划与部署设计过程。首先介绍了5G NSA组网架构的背景和优势,然后深入探讨了IUV_5G全网部署与优化虚拟仿真平台的功能和应用。重点描述了核心网、无线网和承载网的部署过程,包括设备部署、数据配置和线路连接等具体步骤。通过业务验证确认了网络部署的成功,并对5G技术的未来发展进行了展望。

基于互信息优化的Option-Critic算法

时序抽象作为分层强化学习的重要研究内容,允许分层强化学习智能体在不同的时间尺度上学习策略,可以有效解决深度强化学习难以处理的稀疏奖励问题。如何端到端地学习到优秀的时序抽象策略一直是分层强化学习研究面临的挑战。Option-Critic(OC)框架在Option框架的基础上,通过策略梯度理论,可以有效解决此问题。然而,在策略学习过程中,OC框架会出现Option内部策略动作分布变得十分相似的退化问题。该退化问题影响了OC框架的实验性能,导致Option的可解释性变差。为了解决上述问题,引入互信息知识作为内部奖励,并提出基于互信息优化的Option-Critic算法(Option-Critic Algorithm with Mutual Information Optimization,MIOOC)。MIOOC算法结合了近端策略Option-Critic(Proximal Policy Option-Critic,PPOC)算法,可以保证下层策略的多样性。为了验证算法的有效性,把MIOOC算法和几种常见的强化学习方法在连续实验环境中进行对比实验。实验结果表明,MIOOC算法可以加快模型学习速度,实验性能更优,Option内部策略更有区分度。

三色膏联合PRICE疗法治疗急性踝关节Ⅰ和Ⅱ度扭伤的临床效果

目的探讨三色膏联合PRICE疗法治疗急性踝关节Ⅰ、Ⅱ度扭伤的临床效果。方法选取2022年2月至2023年5月上海市嘉定区中医医院骨伤科收治的52例Ⅰ、Ⅱ度急性踝关节扭伤患者作为研究对象,其中急诊患者35例,门诊患者17例,采用随机数字表法将其分为对照组和观察组,每组均失访1例,两组各25例纳入统计。对照组采用PRICE疗法治疗,观察组采用三色膏联合PRICE疗法,两组患者均治疗1周,治疗结束后,比较两组患者治疗前和治疗后第1、3周的视觉模拟评分法(VAS)评分及Mazur功能评分。结果52例患者中50例得到随访,随访时间不短于4周。治疗1、3周后,两组患者的VAS评分低于本组治疗前,Mazur功能评分高于本组治疗前,观察组的VAS评分低于对照组,Mazur功能评分高于对照组,差异有统计学意义(P<0.05)。结论三色膏联合PRICE疗法治疗急性踝关节Ⅰ、Ⅱ度扭伤可以获得更好的疗效,具有重要的临床价值和推广意义。

Pricing European Options Based on a Logarithmic Truncated t-Distribution

The t-distribution has a “fat tail” feature, which is more suitable than the normal probability density function to describe the distribution characteristics of return on assets. The difficulty of using t-distribution to price European options is that a fat tail can lead to a deviation in one integral required for option pricing. We use a distribution called logarithmic truncated t-distribution to price European options. A risk neutral valuation method was used to obtain a European option pricing model with logarithmic truncated t-distribution.

Intelligent option portfolio model with perspective of shadow price and risk‑free profit

Since Markowitz proposed modern portfolio theory,portfolio optimization has been being a classic topic in financial engineering.Although it is generally accepted that options help to improve the market,there is still an improvement for the portrayal of their unique properties in portfolio problems.In this paper,an intelligent option portfolio model is developed that allows selling options contracts to earn option fees and considers the high leverage of options in the market.Deep learning methods are used to predict the forward price of the underlying asset,making the model smarter.It can find an optimal option portfolio that maximizes the final wealth among the call and put options with multiple strike prices.We use the duality theory to analyze the marginal contribution of initial assets,risk tolerance limit,and portfolio leverage limit for the final wealth.The leverage limit of the option portfolio has a significant impact on the return.To satisfy the investors with different risk preferences,we also give the conditions for the option portfolio to gain a risk-free return and replace the Conditional Value-at-Risk.Numerical experiments demonstrate that the intelligent option portfolio model obtains a satisfactory out-of-sample return,which is significantly positively correlated with the volatility of the underlying asset and negatively correlated with the forecast error of the forward price.The risk-free option model is effective in achieving the goal of no drawdown and gaining satisfactory returns.Investors can adjust the balance point between returns and risks according to their risk preference.

Understanding the Relationship Between Shrinking Cities and Land Prices:Spatial Pattern,Effectiveness,and Policy Implications

Urban shrinkage has emerged as a widespread phenomenon globally and has a significant impact on land,particularly in terms of land use and price.This study focuses on 2851 county-level cities in China in 2005–2018(excluding Hong Kong,Macao,Taiwan,and‘no data’areas in Qinhai-Tibet Plateau)as the fundamental units of analysis.By employing nighttime light(NTL)data to identify shrinking cities,the propensity score matching(PSM)model was used to quantitatively examine the impact of shrinking cities on land prices,and evaluate the magnitude of this influence.The findings demonstrate the following:1)there were 613 shrinking cities in China,with moderate shrinkage being the most prevalent and severe shrinkage being the least.2)Regional disparities are evident in the spatial distribution of shrinking cities,especially in areas with diverse terrain.3)The spatial pattern of land price exhibits a significant correlated to the economic and administrative levels.4)Shrinking cities significantly negatively impact on the overall land price(ATT=–0.1241,P<0.05).However,the extent of the effect varies significantly among different spatial regions.This study contributes novel insights into the investigation of land prices and shrinking cities,ultimately serving as a foundation for government efforts to promote the sustainable development of urban areas.

A novel stochastic modeling framework for coal production and logistics through options pricing analysis

We propose a novel stochastic modeling framework for coal production and logistics using option pricing theory.The problem of valuing the inherent real optionality a coal producer has when mining and processing thermal coal is modelled as pricing spread options of three assets under the stochastic volatility model.We derive a three-dimensional Fast Fourier Transform(“FFT”)lower bound approximation to value the inherent real optionality and for robustness check,we compare the semi-analytical pricing accuracy with the Monte Carlo simulation.Model parameters are estimated from the historical monthly data,and stochastic volatility parameters are obtained by matching the Kurtosis of the low-ash diff data to the Kurtosis of the stochastic volatility process which is assumed to follow Cox–Ingersoll–Ross(“CIR”)model.

Brent vs.West Texas Intermediate in the US petro derivatives price formation

In this paper,we apply the spatial panel model to explore the relationship between the dynamic of two types of crude oil prices(WTI and Brent crude oil)and their refined products over time.Considering the turbulent months of 2011,when Cushing Oklahoma had reached capacity and the crude oil export ban removal in 2015 as breakpoints,we apply this method both in the full sample and the three resultant regimes.First,results suggest our results show that both WTI and Brent display very similar behaviour with the refined products.Second,when attending to each regime,results derived from the first and third regimes are quite similar to the full sample results.Therefore,during the second regime,Brent crude oil became the benchmark in the petrol market,and it influenced the distillate products.Furthermore,our model can let us determine the price-setters and price-followers in the price formation mechanism through refined products.These results possess important considerations to policymakers and the market participants and the price formation.

Modeling the Spatio-Temporal Dynamics of Local Context for a Contextualized Diffusion of Agroecological Intensification Options in Niger

Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view to sustainable development. The territorial scale of municipalities is not sufficient for this necessary contextualization;the scale of the “village terroir” seems to be a better option. This is the hypothesis we put forward in the framework of the Global Collaboration for Resilient Food Systems program (CRFS), i.e. local context is spatially defined by village terroir. The study is based on data collected through participatory mapping and surveys in “village terroirs” in three regions of Niger (Maradi, Dosso and Tillabéri). Then the links between farm managers and their cultivated land, as well as the spatio-temporal dynamics of local context are analyzed. This study provides evidence of the existence and functional usefulness of the village terroir for farmers, their land management and their activities. It demonstrates the usefulness of contextualizing agricultural options at this scale. Their analysis elucidates the links between “terroirs village” and the specific functioning of the agrosocio-ecosystems acting on each of them, thus laying the systemic and geographical foundations for a model of the spatio- temporal dynamics of “village terroirs”. This initial work has opened up new perspectives in modeling and sustainable development.

The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

Economic Resilience in Bangladesh: Analyzing Household Well-Being amidst Price Hikes through ANOVA and Paired Sample t-Test Insights

This study delves into the multifaceted impact of price hikes on the standard of living in Bangladesh, with a specific focus on distinct socioeconomic segments. Amidst Bangladesh’s economic growth, the challenges of rising inflation and increased living costs have become pressing concerns. Employing a mixed-methods approach combines quantitative data from a structured survey with qualitative insights from in-depth interviews and focused group discussions to analyze the repercussions of price hikes. Stratified random sampling ensures representation across affluent, middle-class, and economically disadvantaged groups. Utilizing data [1] from 2020 to November 2023 on the yearly change in retail prices of essential commodities, analysis reveals significant demographic shifts, occupational changes, and altered asset ownership patterns among households. The vulnerable population, including daily wage laborers and low-income individuals, is disproportionately affected by adjustments in consumption, income generation, and living arrangements. Statistical analyses, including One-Way ANOVA and Paired Sample t-tests, illuminate significant mean differences in strategies employed during price hikes. Despite challenges, the prioritization of education remains evident, emphasizing its resilience in the face of economic hardships. The result shows that price hikes, especially in essential items, lead to substantial adjustments in living costs, with items like onions, garlic, and ginger experiencing significant increases of 275%, 108%, and 483%, respectively.

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

Production Chain Length and PPI-CPI Divergence:Analysis Based on the Global Input-output Price Model

Productivity and international energy price shocks are reflected in PPI and CPI via industrial chains.China’s in-depth participation into the global value chains has increasingly lengthened its industrial production chains.The question is how the changing length of production chains will affect CPI and PPI,as well as CPI-PPI correlation?By constructing a global input-output price model,this paper offers a theoretical discussion on the impact of production chain length on the CPI-PPI divergence.Our findings suggest that the price shock of international bulk commodities has a greater impact on China’s PPI than that on CPI.The effects on both China’s PPI and CPI estimated by using the single-country input-output model are higher than the results estimated with the global input-output model.However,the difference between CPI and PPI variations estimated with the global input-output model is greater than the result estimated with the single-country input-output model,which supports the view that the lengthening of production chains,especially international production chains,leads to a divergence between CPI and PPI.Empirical results based on cross-national panel data also suggest that the lengthening of production chains has reduced the CPI-PPI correlation for countries,i.e.the lengthening of production chains has increased the PPI-CPI divergence.That is to say,policymakers should target not just CPI in maintaining price stability,but instead focus on the stability of both PPI and CPI.Efforts can be made to proactively adjust the price index system,and formulate the industrial chain price index.

Artificial Intelligence-Based Automated Actuarial Pricing and Underwriting Model for the General Insurance Sector

The Automated Actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and underwriting. This model utilizes data analytics based on Artificial Intelligence to merge microfinance and car insurance services. Introducing and applying a no-claims bonus rate system, comprising base rates, variable rates, and final rates, to three key policyholder categories significantly reduces the occurrence and impact of claims while encouraging increased premium payments. We have enhanced frequency-severity models with eight machine learning algorithms and adjusted the Automated Actuarial Pricing and Underwriting Model for inflation, resulting in outstanding performance. Among the machine learning models utilized, the Random Forest (RANGER) achieved the highest Total Aggregate Comprehensive Automated Actuarial Loss Reserve Risk Pricing Balance (ACAALRRPB), establishing itself as the preferred model for developing Automated Actuarial Underwriting models tailored to specific policyholder categories.

Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.

Spatiotemporal Evolution and Linkage Analysis of Mutton Price in Xinjiang

The mutton industry plays a pivotal role in the animal husbandry industry of Xinjiang Uygur Autonomous Region.To better monitor the volatility movement and risk warning of mutton price,the fluctuation characteristics of mutton prices and the future trend of prices are analyzed systematically.On the one hand,the Hodrick Prescott(HP)filter method is used to analyze the long-term trends and cyclical characteristics of mutton prices in Xinjiang and explored the spatial evolution characteristics of mutton price fluctuations in various regions of Xinjiang.On the other hand,the Threshold Auto-Regressive(TAR)model is used to analyze the linkage relationship between mutton prices and the prices of other livestock products.The empirical results show that 1)the overall volatility of mutton price in Xinjiang is high,showing a trend of rising first,then falling and then rising from the temporal perspective.2)At the regional level,the price in the south is higher than that of the north,showing a decreasing trend from south to north on the whole from the spatial viewpoint.3)From the linkage relationship perspective,mutton and beef are complementary in the short term,but they are substitutes each other in the long term.This paper explores the characteristics of mutton price fluctuations in Xinjiang from the single time series of mutton prices and the linkage with other livestock products,which provides a reliable basis for the monitoring and risk warning of mutton price fluctuation.

Dynamic Pricing Strategies Using Artificial Intelligence Algorithm

Pricing strategies can have a huge impact on a company’s success. This paper focuses on the advantages and disadvantages of using artificial intelligence in dynamic pricing strategies. A good understanding of the possible benefits and challenges will help companies to understand the impact of their chosen pricing strategies. AI-driven Dynamic pricing has great opportunities to increase a firm’s profits. Firms can benefit from personalized pricing based on personal behavior and characteristics, as well as cost reduction by increasing efficiency and reducing the need to use manual work and automation. However, AI-driven dynamic rewarding can have a negative impact on customers’ perception of trust, fairness and transparency. Since price discrimination is used, ethical issues such as privacy and equity may arise. Understanding the businesses and customers that determine pricing strategy is so important that one cannot exist without the other. It will provide a comprehensive overview of the main advantages and disadvantages of AI-assisted dynamic pricing strategy. The main objective of this research is to uncover the most notable advantages and disadvantages of implementing AI-enabled dynamic pricing strategies. Future research can extend the understanding of algorithmic pricing through case studies. In this way, new, practical implications can be developed in the future. It is important to investigate how issues related to customers’ trust and feelings of unfairness can be mitigated, for example by price framing.