How much is corporate cash-pooling worth? Modelling and simulation

The paper analyzes a special corporate banking product, the so called cash-pool, which gained remarkable popularity in the recent years as firms try to centralize and manage their liquidity more efficiently. The novelty of this paper is the formalization of a valuation model which can serve as a basis for a Monte Carlo simulation to assess the most important benefits of the firms arising from the pooling of their cash holdings. The literature emphasizes several benefits of cash-pooling such as interest rate savings, economy of scale and reduced cash-flow volatility. The presented model focuses on the interest rate savings complemented with a new aspect: the reduced counterparty risk toward the bank. The main conclusion of the analysis is that the value of a cash-pool is higher in case of firms with large, diverse and volatile cash-flows having less access to the capital markets especially if the partner bank is risky and offers a high interest spread. It is also shown that cash-pooling is not the privilege of large multinational firms any more as the initial direct costs can be easily regained within a year even in the case of SMEs.

Weighted Sum of Correlated Lognormals: Convolution Integral Solution

Probability density function (pdf) for sum of n correlated lognormal variables is deducted as a special convolution integral. Pdf for weighted sums (where weights can be any real numbers) is also presented. The result for four dimensions was checked by Monte Carlo simulation.

Does Lost Time Cost You Money and Create High Risk?

The aim of the case study is to express the delayed repair time impact on the revenues and profit in numbers with the example of the outage of power plant units. Main steps of risk assessment: • creating project plan suitable for risk assessment • identification of the risk factors for each project activities • scenario-analysis based evaluation of risk factors • selection of the critical risk factors based on the results of quantitative risk analysis • formulating risk response actions for the critical risks • running Monte-Carlo simulation [1] using the results of scenario-analysis • building up a macro which creates the connection among the results of the risk assessment, the production plan and the business plan.

Egységes kockázatkezelési módszertan kialakítása a villamosenergia-ipari átviteli rendszerirányító társaságnál (Development of an integrated risk management methodology for an electricity transmission system operator (TSO) company)

A felelős vállalatirányítás egyik stratégiai jelentőségű tényezője a vállalati szintű kockázatkezelés, mely napjaink egyik legnagyobb kihívást jelentő területe a vállalatvezetés számára. A hatékony vállalati kockázatkezelés nem valósulhat meg kizárólag az általános, nemzetközi és hazai szakirodalomban megfogalmazott kockázatkezelési alapelvek követése mentén, a kockázatkezelési rendszer kialakítása során figyelembe kell venni mind az iparági, mind az adott vállalatra jellemző sajátosságokat. Mindez különösen fontos egy olyan speciális tevékenységet folytató vállalatnál, mint a villamosenergia-ipari átviteli rendszerirányító társaság (transmission system operator, TSO). A cikkben a magyar villamosenergia-ipari átviteli rendszerirányító társasággal együttműködésben készített kutatás keretében előálló olyan komplex elméleti és gyakorlati keretrendszert mutatnak be a szerzők, mely alapján az átviteli rendszerirányító társaság számára kialakítottak egy új, területenként egységes kockázatkezelési módszertant (fókuszban a kockázatok azonosításának és számszerűsítésének módszertani lépéseivel), mely alkalmas a vállalati szintű kockázati kitettség meghatározására. _______ This study handles one of today’s most challenging areas of enterprise management: the development and introduction of an integrated and efficient risk management system. For companies operating in specific network industries with a dominant market share and a key role in the national economy, such as electricity TSO’s, risk management is of stressed importance. The study introduces an innovative, mathematically and statistically grounded as well as economically reasoned management approach for the identification, individual effect calculation and summation of risk factors. Every building block is customized for the organizational structure and operating environment of the TSO. While the identification phase guarantees all-inclusivity, the calculation phase incorporates expert techniques and Monte Carlo simulation and the summation phase presents an expected combined distribution and value effect of risks on the company’s profit lines based on the previously undiscovered correlations between individual risk factors.