Pricing Approach Research Articles

Efficient and Robust Combinatorial Option Pricing Algorithms on the Trinomial Lattice for Polynomial and Barrier Options

Options can be priced by the lattice model, the results of which converge to the theoretical option value as the lattice’s number of time steps n approaches infinity. The time complexity of a common dynamic programming pricing approach on the lattice is slow (at least O n 2 ), and a large n is required to obtain accurate option values. Although O n -time combinatorial pricing algorithms have been developed for the classical binomial lattice, significantly oscillating convergence behavior makes them impractical. The flexibility of trinomial lattices can be leveraged to reduce the oscillation, but there are as yet no linear-time algorithms on trinomial lattices. We develop O n -time combinatorial pricing algorithms for polynomial options that cannot be analytically priced. The commonly traded plain vanilla and power options are degenerated cases of polynomial options. Barrier options that cannot be stably priced by the binomial lattice can be stably priced by our O n -time algorithm on a trinomial lattice. Numerical experiments demonstrate the efficiency and accuracy of our O n -time trinomial lattice algorithms.

Between Rack Rents and Paternalism: Economic Behaviour and the Lease Market in Westphalia, with a Particular Focus on the 19th Century

Abstract Departing from research on Westphalian leases between 1600 and 1900 the paper discusses the lease market of and price determination on three Westphalian estates. While economic history approaches suppose that leases can be seen as market relations and are therefore useful indicators to measure agricultural productivity, a more anthropological perspective emphasises the social relations between lessor (here: noble estate owner) and leaseholders. The choice of an adequate perspective has significant implications for research on agricultural productivity based on rents and leases. Our results indicate that the contractual arrangement of leasehold (well defined duration, announcement, auctions) was used to achieve the highest possible leases. However, at least until the 1830s, demand for leasehold land was rather low and leaseholders could benefit from a lack of competition. Price determination of leases resulted in rents below the Ricardian rent. Therefore, we argue that important assumptions of the established price approach, which uses leases as proxies for productivity, are not met and the analysis of agricultural productivity requires additional evidence on the leaseholder’s income and profit.

JUE Insight: Measuring the Stringency of Land Use Regulation Using a Shadow Price Approach

I propose a shadow price approach to measuring the stringency of land use regulation. A regulation is considered more restrictive if the land developer is willing to pay a larger amount for a marginal relaxation of the regulation. I show that existing theory-based measures of land use stringency are either equivalents or variations of this shadow price measure. Using data from China, I demonstrate that it is possible to compare the stringency of two kinds of land use regulation, a key advantage of this shadow price approach.

Barrier Option Pricing with Trading and Non-Trading Hours

We introduce a semi-analytical pricing approach, which takes non-trading hours into account.

Global sustainability of carbon shadow pricing: The distance between observed and optimal abatement costs

As an important environmental assessment indicator, carbon abatement costs may vary across countries in terms of their development stages. This abatement cost can be measured by a shadow pricing approach using parametric or nonparametric methods. The carbon shadow price (CSP) might be usually understood as the opportunity cost of how much we should give up gross domestic products (GDP) to reduce one unit of carbon emissions. Most research focuses on evaluating the observed value of the carbon shadow price at an individual level, while the optimal value at an aggregate level is often ignored. The novelty of this paper is to propose a nonparametric approach for estimating the optimal carbon shadow price by maximizing the possibility of GDP expansion and carbon reduction simultaneously. We investigate the distance between observed and optimal carbon shadow prices among 152 countries all over the world during the sample period 1991–2019. The result shows that an improving carbon shadow price is observed globally. However, a significant disparity in the distance between observed and optimal carbon shadow prices is detected among countries. Furthermore, we examine the impacts of prevalent climate protocols on global environmental performance. Finally, policy implications for improving sustainable development and achieving optimal carbon shadow prices are provided.

The financials of cybercrime: a health insurance pricing approach

The financials of cybercrime: a health insurance pricing approach

“I will pay you more, as long as you are transparent!”: An investigation of the pick-your-price participative pricing mechanism

Participative pricing strategies are increasingly common nowadays. In addition to have an impact on consumers’ purchase decision, these pricing strategies can impact consumers’ brand evaluations. This paper is an investigation of the Pick-Your-Price (PYP) strategy, which is the most novel participative pricing approach. Adopting a mixed-method approach, we shed light on consumers’ perceptions of the PYP and evaluate the impact of a possible configuration of the PYP (i.e. featuring a default price optionarchitecture) on brand evaluations and purchase choice. We find that when the default is placed on the highest price, consumers ascribe negative causal attribution to the company which, in turn, will result in more negative brand attitudes. Interestingly, introducing price transparency improves causal attributions and brand attitudes as well as the estimated revenues for the company. Moreover, we also test the impact of defaults in the context of communicating different Corporate Social Responsibility practices (internal vs. external) finding that the communication of external CSR stimulates more positive brand attitudes and, in turn, higher willingness to purchase in PYP settings. This study represents one of the first explorations of the PYP strategy, providing interesting managerial implications for marketers willing to experiment with it.

A deep reinforcement learning-based approach for pricing in the competing auction-based cloud market

A deep reinforcement learning-based approach for pricing in the competing auction-based cloud market

Unit-Linked Tontine: Utility-Based Design, Pricing and Performance

Due to the low demand for conventional annuities, alternative retirement products are sought. Quite recently, tontines have been frequently brought up as a promising option in this respect. Inspired by unit-linked life insurance and retirement products, we introduce unit-linked tontines in this article, where the tontine payoffs are directly linked to the development of the underlying financial market. More specifically, we consider two different tontine payoff structures differing in the (non-)inclusion of guaranteed payments. We first price the unit-linked tontines by using the risk-neutral pricing approach. Consequently, we study the attractiveness of these products for a utility-maximizing policyholder and compare them with non-unit-linked tontines. Our numerical analysis sheds light on the design challenges and gives explanations why similar products might not be widely adopted already.

Utility-driven renewable energy sharing systems for community microgrid

Community microgrid has become a popular platform for managing renewable energy in power distribution networks. However, due to the intermittency of renewable energy, the unbalanced energy supply and demand in community microgrid has become an urgent problem. To solve the problem, this paper develops a grid and renewable energy sharing system, which consists of pricing and allocation stages. Specifically, we first propose a game theoretic pricing approach to explore an optimal energy price. With the obtained optimal price, the utility of community microgrid users is maximized. We then transform the energy allocation problem into a convex optimization by derivative analysis, and design an energy allocation method based on a water filling algorithm to solve the convex optimization problem. Experimental results show that as compare to benchmarking methods, our proposed electric energy sharing system can reduce the buyers’ energy cost by up to 46.2% and increase the sellers’ energy profit by up to 62.5%.

A multidimensional Hilbert transform approach for barrier option pricing and survival probability calculation

A multidimensional Hilbert transform approach for barrier option pricing and survival probability calculation

ESTIMATION OF SOCIETY ECONOMIC COSTS DUE TO FLOODS

<p>The purpose of this study is to estimate the value of economic losses due to river floods and interpret the perceptions of the affected community on flood risk. This study uses a descriptive analysis method to look at people's perceptions as a result of flooding and uses the actual market price approach, cost of time, and Human Capital Approach to measure how much the estimated value of losses due to flooding is in the Sembakung District area. Community economic losses are calculated based on direct losses and indirect losses. The results of this study show that the direct losses experienced by the community after the floods in January 2021 amounted to Rp.48,685,514.97. The indirect losses experienced by the community after the floods in January 2021 amounted to IDR 131,600,000. The total estimated loss experienced by households is IDR 180,254,514.97. The community knows that the house they live in is a flood-prone area, as a result of the flood some of the community's houses have become dirty with silt and garbage carried by the flood.</p>

Demand side management for solving environment constrained economic dispatch of a microgrid system using hybrid MGWOSCACSA algorithm

Microgrids are a type of restricted power distribution systems in which electricity is generated, transmitted, and distributed within a small geographic region. They are used to ensure that renewable energy sources are used to their full potential. Microgrids provide further benefits, such as lowering transmission losses and the expenses associated with them. This research compares and contrasts the aims of economic dispatch, emission dispatch, fractional programing based combined economic emission dispatch, and environmental restricted economic dispatch (ECED). A low-voltage microgrid system is investigated for three different scenarios. As a study optimization tool, an innovative, resilient, and strong hybrid swarm-intelligence optimization algorithm is utilised, which is based on combining the properties of the traditional grey-wolf optimiser, sine-cosine algorithm, and crow search algorithm. The employment of a time-of-use energy market pricing approach instead of a fixed pricing plan resulted in a 15% decrease in generation costs throughout the course of the research. When ECED was assessed with a 15%–20% demand side management based restructured load demand model for the microgrid system, the generation costs were reduced even further.

Option Pricing Models: From Black-Scholes-Merton to Present

Its intuitiveness and the simplicity of its calculations make the seminal Black-Scholes-Merton option pricing model the most commonly known and used among all asset pricing models ever developed. Almost half a century after it was introduced, a massive literature has been devoted, and is still being generated, to empirical testing of the original model, to developing new models addressing its original assumptions and biases, and to extending the framework of option pricing. This article presents a review of fundamental option pricing models from Black-Scholes-Merton to the present day, covering alternative option pricing approaches, including those for options on different underlying assets as well as those with different asset price and volatility dynamics. This article also reviews contemporary topics in options, including applications to novel risks such as climate-related risks and volatility risk, as well as implementation of novel methodologies from data science and machine learning.

Demand response considering user behaviour differences for load serving entity: A multi‐agent deep reinforcement learning approach

Demand response can reshape the load profiles by adjusting the energy price to improve overall performance of power grids. For the load serving entities (LSEs), the optimal demand response shall satisfy operation constraints and gain maximised profits. However, the individual end-consumer will surely value electricity differently, and price adjustments without considering the differences of user behaviours may induce unbalanced dispatch and degrade the overall profits. Given affordable price ranges under which the non-critical loads can properly response to price signals, the demand response can be operated in a manner where non-critical loads at different areas coordinately adjusted to achieve the global dispatch objectives. For this purposes, this paper proposes a cooperative demand response approach for LSE pricing based on a multi-agent deep reinforcement learning approach. The learning approach constructs the distributed pricing agents that cooperatively determine the price signals to be responded by different load clusters. To update the parameters of pricing agents, the deep deterministic policy gradient is derived considering the constraints and different behaviours of load clusters. The effectiveness of the proposed cooperative demand response method is verified based on numerical simulations.

Seed quality and the true price of native seed for mine site restoration

Native seed underpins the success of most terrestrial restoration efforts globally; however, the fragility of the native seed supply chain presents a key challenge to achieving global restoration goals. With the current heightened global focus on ecological restoration, seed supply chains are under unprecedented pressure worldwide. New and practical solutions are required to help the native seed industry move toward more sustainable and reliable supply, and in turn, facilitate more cost‐effective, successful, seed‐based restoration. Here we focus on species used in biodiverse mine site restoration in two regions of Western Australia as a test case for evaluating two key elements of the seed supply chain: seed quality and price. The study assessed seed quality in 185 species, then combined these results with seed price to determine the actual cost of pure live seeds (PLS) used in restoration. Average seed quality, expressed as a weight percentage of PLS, is 55%. The average price for a native seed batch across 129 species is $1,093 Australian dollars (AUD)/kg, and when adjusted for viability and purity is $2,600 (AUD)/ kg of PLS. We suggest replacing the traditional approach of pricing seed per unit weight ($/kg) with a new method that would reflect seed quality and unit number; price per thousand pure live seeds ($ TPLS). We posit that this new way of pricing native seeds would increase transparency and information flow in the marketing of native seeds, which will, in turn, enable seed users to more reliably plan for, and evaluate the cost‐effectiveness of seed‐based restoration projects.

The impact of economic policy uncertainty and monetary policy on R&D investment: An option pricing approach

This paper adopts a real options approach to investigate the effects of economic policy uncertainty (EPU) and monetary policy on R&D investment. Using a panel of U.S. firms over the period 2000–2019, we show that higher (lower) EPU and contractionary (expansionary) monetary policy exert a positive (negative) and significant influence on R&D investment. Our findings shed light on the counter-intuitive behavior of R&D investments, which may help policymakers to anticipate such collateral effects.

Network-Flow-Based Formulations for Convex Hull Pricing With Maximum Start-Ups

Reducing uplift payments has been a challenging problem for most wholesale markets in the U.S. The main difficulty comes from the discrete decision-making and non-linear objective function in the unit commitment (UC) problem. Recently, the convex hull pricing approach has shown promises to reduce the uplift payments, in which efficient algorithms are available when i) the convex hull description for each unit and ii) the convex envelope for the objective function are available. Following this framework, in this paper, we provide network-flow-based compact extended formulations for each UC considering ramping constraints, different initial statuses, and maximum start-up restrictions. Meanwhile, by using a piece-wise linear approximation of a quadratic cost curve in the objective function, we show that the corresponding piece-wise linear convex envelope converges to the quadratic envelope as the number of pieces increases in the convex hull pricing problem. The final computational experiments on a revised IEEE 118-bus system indicates the effectiveness of the proposed approach. The trade-off between the cost-saving and computational time improvement is also reported as a reference for further usage.

Predicting the Apartment Transaction Price by Using Machine Learning Methods and the Fixed Effects Model

Predicting the prices of housing is a critical issue for all real estate market participants, including financial institutions and governments, as well as homeowners and potential buyers. To evaluate the performance of a housing price prediction analysis model, this study compares machine learning methods and the fixed effects model based on the panel data of actual transactions between January 2006 and July 2020 in Seoul. For this purpose, we are also considering the characteristics of the apartment complex, based on the hedonic pricing approach, as well as macroeconomic variables and policy variables. We find that the Random Forest (RF) model shows higher predictive accuracy than the Multivariate Adaptive Regression Splines (MARS) model in machine learning. Our study shows the potential of the wide use of the machine learning method by utilizing micro-data in predicting and forecasting housing prices more accurately.

Stakeholders, Institutional Challenges and the Valuation of Wetland Ecosystem Services in South Sudan: The Case of Machar Marshes and Sudd Wetlands.

A number of wetland ecosystem-services valuation studies around the world have been carried out, however, most of these studies have focused on wetlands in developed countries and few have been conducted in Africa, particularly in South Sudan. Thus, this study is conducted to value ecosystem services and identify the role and interests of stakeholders of the Machar Marshes and the Sudd wetlands for sustainable wetland management in the Nile basin. Market price and benefit transfer approaches have been applied to value the wetlands biodiversity and ecosystem services, by adjusting for income and price differences. In addition to environmental valuation methods, we conducted stakeholder analysis. Accordingly, Machar Marshes wetland provides an estimated per annum economic value of $200 million, of which the provisioning services contributed about $61 million, regulating services $132 million, and biodiversity services $7.35 million considering the 2015 price as a base year. Similarly, the Sudd wetland provides an estimated per annum economic value of 2.3 billion, of which regulating is about $1.2 billion, biodiversity $857 million, provisioning $209 million, and transportation service $293,400. The findings show that the ecosystem services from the wetlands have benefits beyond the local communities. Thus, to maintain and ensure sustainable wetlands ecosystem services, stakeholders should play a significant role to implement alternative wetland development options through managing the existing institutional challenges. Ecosystem-services assessment and wetland development options suffer from weak institutional capacity due to prolonged conflicts and instability and physical inaccessibility to critical natural resources in the wetlands.