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Monte Carlo Simulation in Financial Risk Management
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an event. Ideally one will be presented with several alternatives to perform certain task, and by the use of Monte Carlo simulation informed decision can be made choosing the correct or the most convenient course of actions. The mechanism of Monte Carl simulation is a system model will be generated multiple time based on probability models that best suite the key factors. Each time the input value will be changed, on random basis. The model is mostly appropriate on trying to reach a rounded opinion on decision problems like a company profit that is derived from set of factors as associated costs, advertisement and market share. The idea of this simulation tool is to treat basic variables with probabilistic nature instead of the deterministic approach that may misrepresent the real life. Those variables will be assigned a random number that will differ on each run. On performing many runs, probability distributions can fitted on the outcome to study and analysis it. The simulation process helps the decision maker to enumerate various instances where the involved factors may be interrelate. This paper will look into the literature and how scholars made use of simulation tools, in general and Monte Carlo simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management and how it is considered as an important tool for visualizing and recognizing the real nature and integrative scope of supply chain management; the authors defined supply chain risk management (SCRM) is an evolving discipline in which holistic solutions across the various sectors typically do not harmonize. Since organizational risk management encompasses many areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to reduce the gap between these fields by creating an integrated SCRM methodology utilizing financial instruments and operative processes. Authors investigated an aluminum supply chain, a manufacturer and a retailer. Aluminum cost volatility and speculation about the product market were related to the supply chain. In order to manage the risk along the supply chain, a stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the performance of an adaptive risk management system with the sequences of a design, under which decisions on financial risk management are taken after decisions on operational risk management are finalized. Through modeling on a simulated basis and using simulations and statistical analyses, they evaluate the output of both models in order to minimize the total expected supply chain opportunity cost. They look at supply chain performance based on three considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in aluminum prices, the commodity of study. In most instances, they find that their findings on a unified system beats a series model. While the findings often show that its reliability is increased by the conventional supply chain, a criterion is possible that does not require the need of higher levels of risk. Of various business situations evaluated, management lessons are given from past experts opinions on how to build a better holistic approach.
In the fifth chapter of his book “Supply chain risk management”, Olson (2014) described particularly how nature is uncertain; hence, whatever model they are trying to build based on deterministic approach may be unrealistic. So every model that they build incorporates some level of risk due uncertainty. In addition, the more they are aware of the associated risk in their models and accounting for it, the more accurate their work will be. Risk in supply chain models can arise from unpredicted weather conditions like a natural disaster that can hinder certain logistical operation. Political unrest, economic situations, and major industrial accidents are other factors that can affect well-established supply chain models. Mostly, to account for these risks, they have to use probability to change the deterministic nature of their models. This mandates the use of Monte Carlo simulation in supply chain professionals’ work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief description of what inventory can be; which was defined as, the resources that are stored that is exceeding current usage to be used in the future. Inventory is important because demand and supply are not always matching. The probability of a stock out event decreases with a higher inventory level. Stocking out is not favorable because its cost is not just represented in lost sales. It can lead to customers’ dissatisfaction and for them to start looking after alternative products, a matter that can put sales mission under unfavorable risk. On the other hand, building inventory means more cost and tied up capital, this is a risk on its own, because now the chances of bankruptcy, given that sales isn’t meeting their targets, is higher due to the large amount of money invested in keeping higher inventory levels. The problem is like a balancing problem that should be maintained to a certain level with the introduced variability. Inventory costs can be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory and special treatment expenses that should be accounted for, like refrigeration. Ordering cost represented in the administrative work and expenses associated with requesting an order from the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost sales, which will typically be identified on qualitative basis by sales experts. Finally, purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte Carlo simulation can be used to quantify the risk involved here with the four different parameters. The trade off in the model will be to reach the optimal level minimizing the expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk management mechanism involving the cooperation of supply chain stakeholders and the coordination of these members’ operational units. The system incorporates functional and financial risk management actions to reduce the expected total expense of a beer, the commodity of study, supply chain. The authors’ results show that the integrated model’s value efficiency is better than the sequential model as decisions are made separately by functional units, and it is more resilient as well, if exposed to evolving business environments. The results further illustrate the business environment where the unified design led to better results. A less volatile supply chain, for instance, can have some merit over a conservative supply chain while working with low demand volatility or low price volatility. The unified model is more convincing for conservative supply chains as the reduction in overall opportunity cost relative to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the opportunity cost for high price volatility events. The form of risk management approach used is relatable to how conservative the supply chain model and the volatility in request. When faced with increased demand volatility, the analyzed supply chain handled risks more using operative functions methods and less using financial tradeoffs. But since the supply chain is more aggressive or dynamic in nature, it uses organizational and financial instruments less to manage risk. The model has been improved by taking into account stochastic simulation lead times for null outputs and variations in the exchange rates in foreign currencies when a foreign supplier buys aluminum as per the stock market spot rates. In different organizational and financial context of risk management, the integrated supply chain risk management model can be further expanded. The design can include a large range of commodity items and several suppliers. The decisions incorporated can be formed as a dynamic process. Applying this approach, it may be more appropriate to use future or forward contracts rather than options.
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