Tiffanie Artigas, CM-BIM’s Post

What is your go-to method for problem-solving? Structured problem-solving methods are a number of steps or phases that guide the problem solver through a "project" to resolve a problem. Most of the different methods have the same purpose: to provide a strategic approach to clearly define the problem, think though the nuances, and create actionable solutions tied to what was uncovered during your deep dive. One common mistake I see people new to these methods make is getting hung up on the individual steps or choosing the "perfect" method. While some of the structured problem-solving methods are better suited to specific types of problems, they all work based on the same underlying principles and the steps are just there to provide some framework to the strategy. The DMAIC framework is a great structured problem-solving method, and admittedly my favorite because it focuses on improving an existing process. Here's a quick breakdown I put together of each of the steps. D - Define: this step includes defining the problem, improvement goals, any requirements/constraints M - Measure: process performance. This can include many different tools to drive data based decision making A - Analyze: this step is all about searching for the root causes of the problem (i.e. variation, defects). More on the tools for this later. I - Improve: Develop countermeasures that address the root causes uncovered in the previous step C - Control: Create a control plan to sustain the changes you've made, and monitor that your efforts are having an effect on the problem or process If you or your team is looking to develop more robust problem-solving approaches like this one, please send me a message. My team has exciting solutions coming in this space Late Summer 2024!

We're just minutes away from changing the world of TPRM. Join us as we unveil the latest and greatest and help you uncover the secrets to effortless third party risk & compliance. Watch live: https://lnkd.in/geb8usVH

Read our latest engineering blog post, “Similarity Learning, the art of identifying neighbors” by Aladdin Financial Engineering leaders Stefano Pasquali, Philip Sommer, Dhagash Mehta, and Dhruv Desai. In this post, my colleagues discuss how they think about similarity and advanced risk and portfolio management processes. Read now: https://ishars.com/3UDoOkh

Beginning with the root attribution of the Planning Model we develop a compliance reporting system that simulates the outcomes on a much larger scale

📈 Merton Process , Variance Gmma Process, Normal Inverse Gaussian Process 📈 Understanding the inherent randomness and uncertainty in financial markets is crucial. This is where stochastic models come into play. Today, I'm excited to delve into three key stochastic models: the Merton process, the Variance Gamma (VG) process, and the Normal Inverse Gaussian (NIG) process. These models are instrumental in various areas of quantitative finance, including option pricing and risk management, as they provide a framework for analyzing the probabilistic behavior of asset prices. Merton Process: The Merton model, also known as the Merton jump-diffusion model, is an extension of the Black-Scholes model that incorporates jumps in asset prices. It is defined by the stochastic differential equation The model assumes that the log-returns of the asset follow a normal distribution with an added jump component. The density function is a mixture of a normal distribution and a Poisson distribution, accounting for both continuous and jump components of asset returns. - Uses: Used in option pricing and risk management to account for market crashes or sudden movements. - Advantages: Captures both continuous and discontinuous movements in asset prices. - Disadvantages: Estimating the jump parameters can be challenging, and the model may not fully capture real-world market dynamics. Variance Gamma (VG) Process: The Variance Gamma process is another model used to capture the leptokurtic (fat-tailed) nature of asset returns. It is a pure jump process with no diffusion component, defined as a Brownian motion with drift subordinated to a gamma process.The density function of the VG process is more complex than the normal distribution and can capture skewness and kurtosis in the asset returns. It is often used in option pricing models to better fit the observed market prices. - Uses: Useful in modeling asymmetric returns and leptokurtic (fat-tailed) distributions observed in financial markets. - Advantages: Can capture skewness and kurtosis in asset returns better than normal distribution-based models. - Disadvantages: More complex to implement and requires estimation of additional parameters. Normal Inverse Gaussian (NIG) Process: The NIG process is a type of Lévy process used to model asset returns with skewness and kurtosis. It is defined as a Brownian motion with drift subordinated to an inverse Gaussian process. The density function of the NIG process can capture both skewness and kurtosis, making it a flexible model for fitting empirical return distributions. - Uses: Applied in financial modeling for assets with asymmetrical returns and heavy tails. - Advantages: Offers flexibility in fitting empirical return distributions with different shapes. - Disadvantages: Computational complexity and the need for careful parameter estimation. Link: #StochasticModels #QuantitativeFinance #OptionPricing #RiskManagement #FinancialMarkets #FinancialModeling

Read our latest engineering blog post, “Similarity Learning, the art of identifying neighbors” by Aladdin Financial Engineering leaders Stefano Pasquali, Philip Sommer, Dhagash Mehta, and Dhruv Desai. In this post, my colleagues discuss how they think about similarity and advanced risk and portfolio management processes. Read now: https://lnkd.in/g_PNnPDr

Read our latest engineering blog post, “Similarity Learning, the art of identifying neighbors” by Aladdin Financial Engineering leaders Stefano Pasquali, Philip Sommer, Dhagash Mehta, and Dhruv Desai. In this post, my colleagues discuss how they think about similarity and advanced risk and portfolio management processes. Read now: https://bit.ly/3RexwlP

Read our latest engineering blog post, “Similarity Learning, the art of identifying neighbors” by Aladdin Financial Engineering leaders Stefano Pasquali, Philip Sommer, Dhagash Mehta, and Dhruv Desai. In this post, my colleagues discuss how they think about similarity and advanced risk and portfolio management processes. Read now: https://bit.ly/48HHXph

Insightful and useful to reinforce and validate skills. Governance track completed, now to set another goal.