Name: Asset Allocation Modeling with Black Swans Using Monte Carlo Simulation
Start: 2020-11-19T16:00:00Z
End: 2020-11-19T16:00:58.000Z
Location: BrightTALK
Rating: 3.8

Palisade Company is the world’s leading provider of risk and decision analysis software solutions for science and industry. 

Our array of software products and custom services enhance the management experience by combining the latest in cutting-edge technology with over 35 years of analytics experience. Palisade’s unified software platform helps clients increase margins, improve performance, expand market share, and maximize operational efficiencies. 

We have a very simple mission: to minimize risk while maximizing potential.

Introduction to Optimization with Evolver and RISKOptimizer: Comparing Portfolio Allocation with Deterministic versus Stochastic Assumptions

Optimization is a very important tool in business decisions, but there's a huge difference in outcomes if the business environment deviates from the assumptions used to optimize the strategy. In this webinar we will show the difference between deterministic and stochastic optimization, and how we can develop much more robust strategies by accounting for uncertainties in the models.

Stochastic optimization helps integrate both risk analysis and optimization into a single process, generating a decision strategy that is both optimized and robust in face of an uncertain future. We will demonstrate how to perform Portfolio Allocation under a context of uncertainty using stochastic optimization, and compare the resulting decision strategy with the traditional deterministic optimization.

Introduction to Optimization with Evolver and RISKOptimizer

Introducción al Análisis De Riesgos con Simulación Monte Carlo Usando @RISK: Flujo de Caja en Riesgo

Este seminario presenta una introducción del uso de simulación de Monte Carlo para realizar análisis de riesgo cuantitativo.

Presentaremos un ejemplo práctico de Flujo de caja en riesgo, incluyendo CAPEX, OPEX, producción/ventas y riesgos de precios, para mostrar cómo podemos cuantificar los riesgos para mejorar sus pronósticos, la planificación y la gestión de un proyecto con el uso de métodos probabilísticos.

Introducción al Análisis De Riesgos con Simulación Monte Carlo Usando @RISK

Decision trees are structured decision models containing sequences of decisions and events. They allow us to graphically map the plan throughout the lifetime of a project, along with uncertain events in the future, managerial flexibilities of the project, and alternative strategies available.

This webinar will demonstrate how to use decision tree models improve the decision process of Maintenance Planning, in order to minimize costs and limit risk exposure. All within Excel in an intuitive way, but with all the necessary mathematical rigor.

1 - Basic construction of decision tree models
2 - Optimal strategy and interpretation of results
3 - Interpretation of the model and calculations
4 - Sensitivity analysis and tools for complex models
5 - Advanced ways to model risks and uncertainties

Introduction to Decision Analysis Using PrecisionTree: Maintenance Plan Costs

Decision trees are structured decision models containing sequences of decisions and events. They allow us to graphically map the plan throughout the lifetime of a project, along with uncertain events in the future, managerial flexibilities of the project, and alternative strategies available.

In most projects, there are several ways to gather more information before making an irreversible decision. Information gathering can be a complex process such as market research (in retail and manufacturing) or seismic tests (in oil and gas), or a very simple thing such as waiting for a competitor to launch a product first and measure the market acceptance.

This webinar will demonstrate how to use decision tree models measure the value of information (VOI), both perfect and imperfect, and the consequences it brings to the decision process. All within Excel and in an intuitive way, but with all the necessary mathematical rigor.
1 - Basic construction of decision tree models
2 - Modeling alternatives and information revealed
3 - Interpretation of the model and calculations
4 - Sensitivity analysis and tools for complex models
5 - Advanced ways to model risks and uncertainties

Introduction to Using PrecisionTree: Measuring the Value of Information

Introdução à Otimização com Evolver e RISKOptimizer: Comparando a Alocação de Portfólio com Premissas Determinísticas Versus Estocásticas

A otimização é uma ferramenta muito importante nas decisões de negócios, mas há uma enorme diferença nos resultados se o ambiente de negócios se desviar das premissas usadas para otimizar a estratégia.

Neste webinar vamos mostrar a diferença entre otimização determinística e estocástica, e como podemos desenvolver estratégias muito mais robustas levando em conta as incertezas nos modelos.
A otimização estocástica ajuda a integrar tanto a análise de risco quanto a otimização em um único processo, gerando uma estratégia de decisão otimizada e robusta diante de um futuro incerto.

Demonstraremos como realizar a Alocação de Portfólio sob um contexto de incerteza usando otimização estocástica e compararemos a estratégia de decisão resultante com a otimização determinística tradicional.

Introdução à Otimização com RISKOptimizer: Alocação de Portfólio

Los árboles de decisión son modelos de decisión estructurados que contienen secuencias de decisiones y eventos. Ellos permiten mapear gráficamente el plan a lo largo de toda la vida útil de un proyecto, junto con los eventos inciertos en el futuro, las flexibilidades de gestión del proyecto y estrategias alternativas disponibles.

En la mayoría de los proyectos, hay varias formas de obtener más información antes de tomar una decisión irreversible. La obtención de información puede ser un proceso complejo, como un market research (en comercio o en fabricación) o pruebas sísmicas (en petróleo y gas), o algo muy simple, como esperar a que un competidor lance un producto primero y entonces medir la aceptación del mercado.

Este webinar demostrará cómo usar modelos de árboles de decisión para medir el valor de la información (VOI), tanto perfecta como imperfecta, y sus consecuencias para el proceso de decisión. Todo dentro de Excel y de forma intuitiva, pero con todo el rigor matemático necesario.

1 - Construcción básica de modelos de árboles de decisión
2 - Modelado de alternativas e información
3 - Interpretación del modelo y cálculos
4 - Análisis de sensibilidad y herramientas para modelos complejos
5 - Formas avanzadas de modelar riesgos e incertidumbres

Introducción al Análisis de Decisiones con PrecisionTree:Valor de la Información

Whether of raw material, intermediate or final product, human or financial resource, or even resources such as hotel rooms and seats on a plane, inventory is a fact of life for the service and manufacturing industries. Most introductory courses in operations research or management science present analytical models (e.g., EOQ) to assist with the related inventory decisions of how much and how often to order in a manner that minimizes total inventory-related costs. If the assumptions behind these models hold they can prove very useful. When the assumptions don’t hold however, simulation is appropriate. Recent experience with supply chain disruptions and customer demand during turbulent economic times should have woken all who work with inventory to the difficulties presented by uncertain demand and order lead time, making most analytical inventory system models, where both demand and lead time are assumed to be constant, inappropriate.

Following a brief discussion of several basic inventory system analytical models and their associated assumptions, this webinar will present @RISK models for simulating inventory systems that handle lost sales and back orders while treating demand and lead time as uncertainties. Several additional embellishments will then be discussed to account for situations where further analytical model assumptions don’t hold.

Modeling Inventory Systems with @RISK: Lost Sales and Back Orders

This webinar presents an introduction on using Monte Carlo Simulation to perform Quantitative Risk Analysis. We'll present a practical example of Cash Flow at Risk, including CAPEX, OPEX, production/sales and price risks, to show how you can quantify risks to improve your forecasts, planning, and overall management of a project with the use of probabilistic analysis.

Intro to Risk Analysis with Monte Carlo Simulation Using @RISK: Cash Flow

En este Webinar mostraremos cómo utilizar series de tiempo para realizar proyecciones tanto de variables macroeconómicas como de variables internas de una empresa tales como ventas, utilidad y precios.  Explicaremos por qué un pronóstico no puede ser un valor fijo, bajo qué condiciones un proceso AR(1) se convierte en una caminata aleatoria, y funciones útiles de RISK tales como RISKPercentile y RISKTarget.

Ventajas de Series de Tiempo como Herramienta de Proyección y Análisis en @RISK

Introducción a la Optimización con Evolver y RISKOptimizer: Comparación de la Asignación de Cartera con Supuestos Deterministas Versus Estocásticos

La optimización es una herramienta muy importante en las decisiones de negocio, pero hay una gran diferencia en los resultados si el entorno del negocio se desvía de los supuestos utilizados para optimizar la estrategia.

En este webinar, mostraremos la diferencia entre la optimización determinista y la estocástica, y cómo podemos desarrollar estrategias mucho más sólidas teniendo en cuenta las incertidumbres en los modelos.
La optimización estocástica ayuda a integrar tanto el análisis de riesgos como la optimización en un solo proceso, generando una estrategia de decisión optimizada y robusta ante un futuro incierto.

Demostraremos cómo realizar la asignación de cartera en un contexto de incertidumbre mediante la optimización estocástica y compararemos la estrategia de decisión resultante con la optimización determinista tradicional.

Introducción a la Optimización con RISKOptimizer: la Asignación de Cartera

On this last webinar of the O&G series we will wrap up everything we presented so far: based on the risk analysis of each project, we will group them in a portfolio and optimize it according to the company's objectives and constraints.

We will discuss which risks are specific to each project (such as its production curve), which risks are systemic to the portfolio (such as crude oil prices), the correct way to model each risk, and the effects of correlations.

Oil & Gas Webinar Series 5: Project Portfolio Analysis and Optimization

On this forth webinar of the O&G series will present how to represent and forecast crude oil prices using time series models. We will use historical data to fit/calibrate a model for spot prices, and then use this model to forecast future prices for the valuation of an E&P project.

Oil & Gas Webinar Series 4: Modeling Commodity Price Risk with Time Series

This is the third webinar of the O&G series, and here will present how to model the uncertainty around drilling times, including a risk matrix and risk drivers such as NPT (non-productive time) and WOW (wait on water). We will also see an example of how to measure the impact of these risks based on historical data.

Oil & Gas Webinar Series 3: Drilling Times Forecasting with @RISK

Oil & Gas Webinar Series 2: Production Curve Risk: Useful Approaches to Modeling Production Curves

This second webinar of the O&G series will show a few easy and straightforward ways to include production curve risk into your project's analysis, including individual risk drivers and correlation between risks. We will also discuss some pitfalls of modeling curves and how to avoid them.

And even though the focus of this presentation will be Oil&Gas, the concepts and ideas presented apply to any kind of curve forecast (sales, demand, growth, etc).

Oil & Gas Webinar Series 2: Production Curve Risk

Bombeo de Agua en Minería: Aprovechando las Diferencias de Precios en Mercado Spot con @RISK.

El mercado spot de electricidad en Chile, ya desde el 2021 ha presentado una característica que ha resultado notoria, diferencias de precios importante en ciertas horas del día, lo anterior originado fundamentalmente por una gran capacidad de generación renovable del tipo fotovoltaica instalada en el sistema eléctrico. Es así como en horas de sol y en la zona del norte de Chile donde operan las principales compañías mineras, el precio del energía o costo marginal del sistema, presenta características del tipo mostrada en la gráfica adjunta la cual corresponde a S/E Crucero nivel 220 kV; más aún en ciertas horas se han presentado precios nulos en determinados días. Destacamos esta subestación tanto por la zona en donde se ubica como al hecho que es la que presenta mayor número de horas con precios nulos, vertimiento, dentro de las principales subestaciones del país.

La existencia de las características en los precios del mercado spot, presenta cualidades que pueden ser aprovechadas por clientes y/o empresas generadoras para una reducción de los costos asociados en el suministro de ciertos procesos mineros que presentan holguras y capacidad de almacenamiento (pulmones). Es así como procesos asociados al bombeo de agua en minería presenta características que permiten aprovechar el perfil de precios mostrado. En el webinar desarrollaremos el análisis de un bombeo de agua típico en Minería, considerando aspectos estocásticos con ayuda de @RISK, como el bombeo de agua y la eficiencia asociada, demanda de agua, fiabilidad del suministro eléctrico como la evaporación del insumo en las piscinas de almacenamiento, al cambiar de un bombeo tradicional a uno que considere una mayor impulsión en horas de sol bajando en terceras horas, de forma de bajar los costos asociados e impulsar el bombeo hacia uno Verde, aprovechando mayor presencia de generación de energía renovable.

Bombeo de Agua en Minería: Aprovechando las Diferencias de Precios en Mercado...

How much is this E&P project really worth for my company?

On this first webinar of the Oil & Gas Webinar Series we will present how to measure the value of an E&P project under a scenario uncertainty using @RISK and the DecisionTools Suite.

We will discuss the typical risks associated with an E&P project, how to model and measure them, and the impact they bring to the valuation.

Oil & Gas Webinar Series 1: Project Valuation and Risks

Risks, uncertainties and probabilistic decision making in an increasingly volatile engineering & construction – A case study assessing optimum delivery strategy for procuring a major water pipeline project

Post COVID-19, the Engineering & Construction sector is changing fundamentally, leading to significant risk exposure faced by both public and private organizations. Beyond the recent economic uncertainties, more frequent catastrophic events are expected in future, as well as disruptions including climate change, digital revolution, limited capability and capacity, supply chain constraints, social license to operate, stakeholder expectations and political risks. 

One of the best ways to embrace uncertainty in our approach is to learn to think probabilistically, like a professional gambler. Developing a probabilistic mindset allows us to be better prepared for uncertainties, complexities, and different scenarios for making better informed decisions. 

With a global investment of approximately US$60 trillion, including a potential pipeline of $800b projects in Australia over the three decades to 2050 net zero emission targets, across power, mining and metals, hydrogen, carbon capture and oil & gas projects, Pedram believes there are good opportunities for the industry to improve the way it selects, develops, and delivers major projects by better utilizing the risk engineering techniques such as probabilistic decision tree analysis. 

By sharing a recent case study of using a probabilistic decision tree analysis (by using Palisade Decision Tools software) for comparing and selecting the most optimum delivery strategy for procuring a major water pipeline project, Pedram will explain the fundamentals of decision-making process. According to Pedram, organizations that arm themselves with probabilistic thinking and capabilities will become more resilient and put themselves in a much better position to safeguard for uncertainties ahead.

Assessing Optimum Delivery Strategy for Procuring a Major Water Pipeline Project

How to account for correlation in price models for commodities?

On this fifth webinar of the Forecasts with Uncertainty Webinar Series we will present how to estimate and forecast prices of groups of correlated commodities - for example, petroleum and its refined products or soy bean, oil and meal. 

Correlation adds an extra layer of complexity when creating realistic forecast models, especially for commodities. We will use historical data to fit, calibrate and simulate correlated time series models for commodities, and discuss some pitfalls and practical insights on modeling.

Forecasts with Uncertainty Webinar Series 5: Forecasting Correlated Commodities

The traditional approach to asset allocation has been to use efficient frontier models that seek to find the optimal portfolio mix that has the lowest possible level of risk (standard deviation) for its level of return (mean). In these models all asset class returns are assumed to follow the normal distribution. The financial crisis of 2008 convinced us that it was time to look closer at the use of the normal distribution for modeling the probability of investment returns, in particular for equities. While the normal distribution assigns about 1 chance in 1000 for the stock market to experience a 3-standard-deviation decline, such as the market experienced in 2008, the reality is that a move of this magnitude occurs more frequently than that. Looking back at historical data, a move up or down of 3 standard deviations or more, over 6 months to a year, happens more like 2 out of every 100 time periods. The problem then is: How do you add 'Fat Tails' (outlier events that occur more frequently in reality than would be predicted by the normal distribution) to the model distribution in an effort to give the ‘theoretical’ stock market a more realistic chance of experiencing one of these unexpected 'Black Swan' events? 
In this webinar we'll demonstrate how @RISK can be used to create financial models that give investors a more realistic view of the risks in their portfolios by incorporating simulated distributions that more closely resemble reality and provide more realistic 'Fat Tails'. Another revelation of the financial debacle in 2008 was the fact that correlations between most asset classes were, at least temporarily, much higher than expected. On the other hand, some asset classes that historically had a low or negative correlation with equities had positive returns that year, a result that makes sense. In this case we'll talk about how we were able to incorporate a second correlation table in to our model to account for stress in financial markets.

Asset Allocation Modeling with Black Swans Using Monte Carlo Simulation

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Asset Allocation Modeling with Black Swans Using Monte Carlo Simulation

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