Period Of Prices Research Articles

Black-Scholes Model and Binomial Model Tests on Nvidia Stock Option Contracts

In order to invest, investors can purchase a number of shares. The characteristics of stocks are high risk/high return and low risk/low return. Stock price volatility is a measurement of how quickly and in a short period of time stock prices can rise or fall (fluctuate). With the advent of derivative instruments, specifically options, risk can be reduced in several ways. A contract known as an option grants its owner the right, but not the duty, to sell or acquire a specific quantity of the underlying asset at a specified price at a specific point in the future. Methods for calculating option prices in derivative instruments is the Binomial option model and the Black Scholes option model. The aim of this research to determine the accuracy of the Black Scholes compared with the Binomial in predicting call option shares of Nvidia Coorporation (NVDA) on the due date of 1 month, 2 months, and 3 months, using data price daily and call option which at 17 sample from January 2022 to May 2023. To determine the accuracy use price absolute error comparisson between the Black Scholes and the Binomial was conducted. In result the Black Scholes model is more accurate than the Binomial.

PRICING OF CALL OPTIONS USING THE QUASI MONTE CARLO METHOD

A call option is a type of option that grants the option holder the right to buy an asset at a specified price within a specified period of time. Determining the option price period of time within a certain period of time is the most important part of determining an investment strategy. Various methods can be employed to determine the prices of options, such as Quasi-Monte Carlo and Monte Carlo simulations. The purpose of this research is to determine the price of European-type call options using the Quasi-Monte Carlo method. The data used is daily stock closing price data on the Apple Inc. for the period October 1, 2021, to September 30, 2022. Apple Inc. stock options in this study were chosen because it is the largest technology company in the world in 2022. The steps taken in this study are to determine the parameters obtained from historical data such as the initial risk-free interest rate (r), stock price (, volatility , maturity time (T), and strike price (K). Next is to generate Halton’s quasi-randomized sequence and simulate the stock price by substituting the parameters by substituting the parameters. Then proceed to calculate the call option payoff and estimate the call option price by averaging the call option payoff values. The results showed that the call option price of the company Apple Inc. using the Quasi-Monte Carlo with Halton’s quasi-randomized sequence on the 1000000th simulation with a standard error of 0,045 is $90,163. The call option price obtained can be used as a reference for investors in purchasing options to minimize losses from call option investments in that period.

OP10 The Ripple Effects of Healthcare Reforms in Germany: A Reduced Free Pricing Period

OP10 The Ripple Effects of Healthcare Reforms in Germany: A Reduced Free Pricing Period

Evidence of households' demand flexibility in response to variable hourly electricity prices – Results from a comprehensive field experiment in Norway

Demand-side flexibility which reacts to hourly price signals is expected to play an essential role in balancing intermittent electricity generation in the future carbon-neutral power system. This study investigates the price responsiveness of households and various influencing factors through a pricing experiment which was implemented as a reward scheme and involved 3746 Norwegian households. These households, characterised by a highly electrified energy usage such as electric heating and charging electric cars, were subjected to variable hourly price signals with a one-day advance notification over the course of three winter months. The study reveals that households reduced their electricity demand by, on average, 2.92% in hours with high prices, and the reduction did not diminish significantly after repeated interventions. Moreover, an increased response could be observed for price signals with a short peak price period and when prices exceeded a threshold of 15 NOK/kWh. These results suggest that despite the limited potential of manual demand response from households, it can still be relied upon and utilised to enhance power system operations and planning. Additionally, to fully exploit its potential, it is recommended to enhance price responsiveness through tailored price information and by incentivising investments in automatic response and energy storage.

Evaluation of model predictive control (MPC) of solar thermal heating system with thermal energy storage for buildings with highly variable occupancy levels

The presence or absence of occupants in a building has a direct effect on its energy use, as it influences the operation of various building energy systems. Buildings with high occupancy variability, such as universities, where fluctuations occur throughout the day and across the year, can pose challenges in developing control strategies that aim to balance comfort and energy efficiency. This situation becomes even more complex when such buildings are integrated with renewable energy technologies, due to the inherently intermittent nature of these energy source. To promote widespread integration of renewable energy sources in such buildings, the adoption of advanced control strategies such as model predictive control (MPC) is imperative. However, the variable nature of occupancy patterns must be considered in its design. In response to this, the present study evaluates a price responsive MPC strategy for a solar thermal heating system integrated with thermal energy storage (TES) for buildings with high occupancy variability. The coupled system supplies the building heating through a low temperature underfloor heating system. A case study University building in Nottingham, UK was employed for evaluating the feasibility of the proposed heating system controlled by MPC strategy. The MPC controller aims to optimize the solar heating system’s operation by dynamically adjusting to forecasted weather, occupancy, and solar availability, balancing indoor comfort with energy efficiency. By effectively integrating with thermal energy storage, it maximizes solar energy utilization, reducing reliance on non-renewable sources and ultimately lowering energy costs. The developed model has undergone verification and validation process, utilizing both numerical simulations and experimental data. The result shows that the solar hot water system provided 63% heating energy in total for the case study classroom and saved more than half of the electricity cost compared with that of the original building heating system. The electricity cost saving has been confirmed resulting from the energy shifting from high price periods to medium to low price periods through both active and passive heating energy storages.

Eksekusi Jaminan Gadai Emas pada Bank Syariah Indonesia

Pawn transactions in Islamic jurisprudence are called al rahn. Rahn has many definitions, one of which in Arabic rahn has a permanent and continuous meaning. As for the definition of rahn in sharia terms, the scholars explained with the phrase, "Making property as collateral for debt to be repaid with the guarantee when unable to pay it off." The system of debt and credit transactions with pawns is allowed in Islam because there are arguments from the Qur'an, Sunnah, and ijtihad on which to base. So you don't need to hesitate anymore in sharia pawn transactions. The verse of the Qur'an that can be used as the legal basis for a pawn agreement is Q.S. Al Baqarah verses 282 and 283. Indonesia is one of the countries with the largest Muslim population, this makes Indonesia a potential market for sharia-based financial services. Where this sharia service meets the elements of Islamic law, including Rukun Rahn. faced by society. One of the banks that has service products in the form of pawns is the Indonesian Islamic Bank, hereinafter referred to as BSI. Where BSI runs a pawn service product in accordance with sharia and Islamic law that applies in Indonesia. This pawn is a bank lending money to customers/debtors with collateral, within a certain time and a certain amount of interest, and the debtor has an obligation to pay the loan in accordance with the amount of the price and maturity period that has been determined by the Bank and agreed upon by the debtor. Goods that can be guaranteed objects at BSI include Gold, Precious Metals, House Certificates or Land Certificates, etc.

The Impact of Information Uncertainty on Stock Performance During the 2008-2009 Financial Crisis

We investigate the impact of information uncertainty on stock performance during the 2008-2009 financial crisis. We document that firms with more information uncertainty suffered a greater stock price drop during the period of market-wide price declines. In contrast, the negative effect of information uncertainty on stock returns was absent during the market-reversal or pseudo-drop period. In addition, during the reversal period, firms with forecast dispersion in the top tercile (or quartile) had more positive returns than those with lower forecast dispersion. We contribute to the literature by testing the relationship between information uncertainty and firm returns in the special setting of the most recent financial crisis and providing initial evidence that firm-specific information uncertainty amplified stock price fluctuations during the financial crisis.

The Influence of Earning Per Share, Turnover of Total Asset, Return on Equity and Debt Asset Ratio on LQ45 Index Share Price Period 2017 – 2021 listed on the Indonesia Stock Exchange

Due to the inflation factor, for that we need to invest in an investment asset, the current value of money will decrease. The investment instrument studied in this study were shares, using financial ratios to establish whether financial ratios are correlated with stock prices so that we can search for companies that can provide benefits in the form of capital gains, the object of this research uses ratios, among others: earnings per share, total asset turnover, return on equity, and debt/asset ratio. This research, using the help of the Eviews version 10 program as an application to process the data used, was carried out on companies listed on the lq45 index. In this study, the purposive sampling method was used as the sampling method. The number of samples used were 23 companies with research data carried out from 2017 to 2021, so 115 observation data were obtained. From the results of this study it is known that: (1) Earnings per share has a significant positive effect on share prices, this is evidenced with a significance of 0.0000 and a t value of 11.35574, so H1 is accepted; (2) The Turnover of Total Assets has a significant positive effect on stock prices, this is evidenced by the significance of 0.0156 and the value of the arithmetic t (2.456378) for which H2 is accepted; (3) Return on Equity has a significant positive effect on share prices, this is evidenced by a significance of 0.0010 and a t-count value of 3.368250, so H3 is accepted; (4) The debt to asset ratio has a significant negative effect on share prices, this is evidenced by a significant value of 0.1872 and a t-count value of (1.327267) so H4 is rejected; (5) Obtained Simultaneous Test Results Arithmetic results F of 33.50263 with probability 0.000000 can be concluded if simultaneously all variables have a significant influence on stock prices. The results of the regression analysis show the adjusted value (r^2) of 0.532807, which means that variations in Earnings per Share, Total Asset Turnover, Return on Equity, and Debt to Assets Ratio can explain the 53.28 % of changes in stock price. While the remaining 46.72% is explained by other variables not examined in this study.

The impact of the war in Ukraine on energy prices: Consequences for firms’ financial performance

We analyze the impact of the shock to energy prices induced by the war in Ukraine on the financial performance of the major European firms listed in the Eurostoxx 600 index. We find that equity returns (CDS spreads) decreased (increased) more substantially for firms characterized by high energy intensity and carbon emission intensity. We then present a method, based on a VAR model, to produce forecasts of firms’ CDS spreads conditional on a 1 or 3-month stress period of high electricity prices and we document a non-negligible increase in the number of firms with a CDS-implied non-investment rating.

Data Center HVAC Control Harnessing Flexibility Potential via Real-Time Pricing Cost Optimization Using Reinforcement Learning

With increasing electricity prices, cost savings through load shifting are becoming increasingly important for energy end users. While dynamic pricing encourages customers to shift demand to low price periods, the non-stationary and highly volatile nature of electricity prices poses a significant challenge to energy management systems. In this paper, we investigate the flexibility potential of data centres by optimising heating, ventilation, and air conditioning systems with a general model-free reinforcement learning approach. Since the soft actor-critic algorithm with feed-forward networks did not work satisfactorily in this scenario, we propose instead a parameterisation with a recurrent neural network architecture to successfully handle spot-market price data. The past is encoded into a hidden state, which provides a way to learn the temporal dependencies in the observations and highly volatile rewards. The proposed method is then evaluated in experiments on a simulated data centre. Considering real temperature and price signals over multiple years, the results show a cost reduction compared to a proportional, integral and derivative controller while maintaining the temperature of the data centre within the desired operating ranges. In this context, this work demonstrates an innovative and applicable reinforcement learning approach that incorporates complex economic objectives into agent decision-making. The proposed control method can be integrated into various Internet of things-based smart building solutions for energy management.

Hierarchical transactive power exchange method on expressways for EV energy supplement

Electric vehicle (EV) users’ driving requirement is restricted by the long charging period and high cost. In this paper, a hierarchical transactive power exchange method on expressways is proposed to eliminate range anxiety faced by EV users and further reduce their cost. When EVs are driven on expressways, battery swapping is considered the suitable power exchange mode due to high efficiency and adjustability. EVs are scheduled to supplement energy in battery swapping stations (BSSs) according to the remaining energy and battery swapping cost. Then, the power exchange among batteries and the power grid in BSSs is optimized for reducing the operation cost. In the optimization process, battery-to-battery and battery-to-grid modes are considered for reducing the power cost in the high electric price period. Some EVs release battery energy in designated BSSs and supplement energy in other BSSs. It reduces fast charging power of the battery and operation costs in designated BSSs. Several case studies are presented to validate the effectiveness and economy of the proposed method.

Energy Internet-Based Load Shifting in Smart Microgrids: An Experimental Study

This study investigated a grid-connected smart microgrid (MG) system integrating solar photovoltaic (PV) panels and a battery energy storage system (BESS) as distributed energy resources (DERs) to locally serve residential loads. The load-shifting demand-side management (DSM) technique was employed to effectively manage the load appliances. The proposed load-shifting algorithm relies on minimum price incentives to allow customers to allocate their load appliances economically during minimum price periods. The algorithm considers the waiting times and minimum tariff periods for appliances, calculates precise operating durations for each appliance, and prioritizes powering the appliances from the MG first, followed by the main grid. The system comprises two non-shiftable and three shiftable loads. When the MG power is insufficient to activate all shiftable loads, the system transfers the remaining unsupplied shiftable appliances to periods with low-priced energy. The Energy Internet concept is adopted to manage energy and monitor usage when a customer is unable to check the accuracy of their energy meter by supervising the system’s features on-site. The proposed comprehensive system enables load management, continuous monitoring, customer awareness, and energy cost saving. Six cases were studied, both numerically and experimentally, with varying MG power generation and load pre-scheduling periods, with and without DSM application. In all adopted cases, the implemented system save energy costs by at least 50%.

Optimal Solution of Peer-to-Peer and Peer-to-Grid Trading Strategy Sharing between Prosumers with Grid-Connected Photovoltaic/Wind Turbine/Battery Storage Systems

This paper presents an energy management peer-to-peer (P2P) and peer-to-grid (P2G) trading strategy for power sharing between prosumers with grid-connected photovoltaic/wind turbine/battery storage systems. The optimal results are obtained using the “Fmincon” solver due to its ability to solve large-scale optimization problems. Two prosumers sectors are considered in this study that is the residential and commercial. The results show that for the residential consumer side, the load is satisfied by the energy from the grid, its own renewable energy system (RES), and the energy from the commercial consumer side RES during the first off-peak period (00:00 AM to 05:00 AM). During the peak pricing period (17:00 PM to 22:00 PM), the load is satisfied by the residential consumer’s battery and the commercial consumer’s battery, and no energy is bought from the grid due to the high price. Furthermore, 37.69% and 39.40% cost savings for residential and commercial, respectively, and a reduction of the bill by $42.26/day and $42.97154/day, respectively, are recorded. Significant grid energy savings for the developed P2P/P2G trading for prosumers and maximum daily cost savings are achieved compared to existing cases such as P2G only and P2P only. Finally, the residential consumer can achieve a greenhouse gases (GHG) emissions reduction of 141.88 kgCO2-eq and 176.99 kgCO2-eq net savings, while the commercial consumer can achieve 122.34 kgCO2-eq GHG emissions mitigation and 196.53 kgCO2-eq net savings.

Pricing and replenishment strategy for a perishable product under various payment schemes and cap-and-trade regulation

This study investigates a sustainable three-echelon supply chain structure (supplier–retailer–customer) from the retailer’s perspective for a perishable product where: (1) the retailer pays the purchase cost to the supplier in the form of an advance-cash-credit (ACC) payment while granting their customers a partial credit, (2) the supplier offers a price discount to the retailer to facilitate sales, (3) the deterioration rate of items increases over time due to an expiration date, (4) the customer is allowed a partial delay in orders with a fixed market tolerance period, and (5) the supply chain management (SCM) structure takes consideration of government cap-and-trade regulation. This paper intends to establish the retailer’s optimum cycle time, selling price, and inventory period simultaneously to maximize his/her total profit. An efficient algorithm has been constructed to find optimal solutions. Two examples have been presented to validate the proposed model. Furthermore, a comparative analysis of the four different payment methods (upstream ACC, advance, cash, credit, and downstream partial credit payments) has been carried out and sensitivity analyses have been performed to gain managerial insights. Our numerical results reveal that with the increase in the discount rate, the selling price reduces significantly, however, the retailer’s profit increases tremendously. The reason for this phenomenon is that a higher discount rate can lead to a higher profit for the retailer, hence, the retailer may set a lower price to increase sales. Many retailers desire a high discount rate, which is associated with the upstream advance and downstream partial credit payment; however, the computational results show that this payment method causes significant damage to the environment. Hence, two payment methods (upstream ACC, credit, and downstream partial credit payments) are recommended. The reason is that these two payment types bring more profits to the retailer and result in less damage to the environment.

Assessing the Economic Impact of Introducing Localised PV Solar Energy Generation and Energy Storage for Fleet Electrification

Greenhouse gas (GHG) emissions from the transport sector and their effect on air quality are now a major concern, and the electrification of road freight transport is seen as one potential solution. However, this presents a challenge with the increased electricity demand on a depot’s grid connection, and increased costs if this has to be upgraded. This study seeks to evaluate the impact on costs of introducing solar (PV) panels and a Battery Energy Storage System (BESS) when a company electrifies its fleet, based on two different priorities. On one hand, avoiding the use of the grid at peak price periods at the expense of upgrading the power connection. On the other hand, avoiding any power connection upgrade at the expense of incurring excess capacity charges. These two priorities aim to represent real-life challenges that logistics and commercial companies are facing when it comes to fleet electrification. The choice of prioritising one over the other may be driven by operational requirements and/or technical constraints. For each approach, a different energy management algorithm is developed using MATLAB and Simulink. The results obtained suggest that there is more flexibility in cost reduction when the upgrade of the power connection is not an obstacle. If the upgrade of the power network is not an option, the installation of PV panels and a BESS must be implemented together with other strategies (i.e., smart charging) to make it an economic option.

Freeze desalination via thermoacoustic cooling: System analysis and cost overview

Clean, drinkable water is essential for human health and well-being. However, access to clean water is becoming a global problem due to climate change and over exploitation of natural water resources. Desalination is widely accepted as a solution to meet water demands in areas with access to seawater. However, remote areas such as small islands far from the mainland pose a special challenge as they lack the infrastructure to meet electricity and water demands. This paper investigates the deployment of freeze desalination (FD) coupled with thermoacoustic engine and refrigerator (TAE/R) to provide freshwater to small settlements in remote locations like small islands. It presents an economic feasibility assessment of deployment of waste energy to satisfy the freshwater requirements, as an alternative to using conventional techniques such as reverse osmosis (RO) and thermal distillation (TD) that are energy intensive. Different scenarios are presented comparing the economic feasibility of FD vs. RO. The analysis is done based on the net present value (NPV), internal rate of return (IRR), selling price, and payback period using cashflow diagrams. Compared with the RO, FD powered by TAE/R turns out to be the most favorable option to produce freshwater from seawater for a small community on an isolated island due to high NPV and IRR, and low selling price and payback period.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI VOLUME PENJUALAN MOTOR HONDA PADA CV NASIONAL MOTOR RUTENG

Motorcycles are a human need that really helps provide convenience in transportation. The need for motorcycles encourages people's purchasing power of motorbikes as a means of transportation. One of the alternatives used in an effort to win the competition is by selling credit concerning prices, products, credit services and credit terms. Purpose: to determine the magnitude of the influence of price, multiplied by product, credit service and credit period on sales of Honda motorcycles at CV. National Motor Ruteng. The sampling technique used the Quota sampling method, namely 100 samples who bought Honda motorbikes at CV. National Motor Ruteng. The research variables are price, product, credit service and credit term. Collecting research data using interviews, questionnaires, documentation and observation studies. Data analysis was performed using multiple regression analysis method. The results obtained (1) in terms of price, product, credit service, and credit period have a significant influence on the sales volume of Honda motorcycles at CV Nasional Motor Ruteng. (2) Simultaneously price, credit service, and credit period have a significant effect on the sales volume of Honda motorcycles at CV.Nasional Motor Ruteng (p = 0.000) where P <0.05.

Who is better for single and double coupon promotion? Comparison from dual‐channel and two‐period

AbstractThis paper examines the e‐commerce platform decides whether to distribute electronic coupons during the promotional period: Single coupon (SC) and double coupon (DC) are available. We analyze the difference in promotional strengths and profit levels between the two coupon scenarios. With the retailer in the SC scenario promoting more when the platform commission rate is increased. In the DC scenario, the larger the gains of the manufacturer and the platform during the promotional period, and the profit trends of the dual‐channel in the original price period remain the same. The platform can only enjoy significant benefits when the platform fee is higher.

Electric Vehicle Charging Scheduling Optimization Method Based on Improved Ant Colony

The influx of electric vehicles significantly impacts the power grid. In the process of power grid operation, the operation of charging and discharging of electric vehicles can be used to dispatch the load of power grid operation. The optimization method of charging and dispatching electric vehicles based on improved ant colony is studied. Pheromones generated during the charging of the electric vehicle are obtained, and the charging limit range of the battery is delimited. The dynamic window constructs the charging scheduling model improved ant colony algorithm, and the optimal charging path is selected to complete the scheduling method design. The experimental results show that the proposed method can adjust the charging and discharge in the period of valley price and peak price, play the role of peak clipping, and realize the load regulation in different periods, which has application value.

Demand response with active phase change material based thermal energy storage in buildings

A large potential to shift the electricity consumption to adapt to the stochastic renewable electricity generation is identified through the utilisation of a combination of Heat Pumps (HP) and local Thermal Energy Storage (TES) devices in building heating systems. In this paper, a building heating system coupled with an active Phase Change Material (PCM) TES device and a HP is simulated to characterise its potential for Demand Response (DR) applications. A control-oriented numerical model for the PCM TES is developed and previously validated numerical models of the building, HP and hot water radiators are integrated to simulate the dynamics of the coupled building heating system. A Genetic Algorithm (GA) based control strategy is designed to optimise the building heating energy consumption and operational cost with respect to time-varying electricity price signals. The developed control strategy is successfully implemented to utilise the TES capability of the PCM and the thermal inertia of the building to intelligently shift the electrical load of the HP to low price periods, while satisfying the specified indoor comfort requirements. In comparison to a reference case utilising a sensible TES, cost savings and consumption reductions of more than 40% and 30%, respectively, are attained with the active PCM TES. Simulation results indicate that utilising an active PCM TES over a sensible TES offers significant advantages for DR applications in building heating systems in terms of load shift flexibility, energy costs and consumption.