• Stress Testing Under Adverse Economic Scenarios - Bank Stress Testing Using MATLAB, Part 1

    882 views 4 days ago
    Watch a step-by-step example that illustrates how to use MATLAB® to perform stress testing based on economic scenarios.

    Download the Code Used in this video: http://bit.ly/2KtojSS
    Learn more about MATLAB for Finance and Risk Management: http://bit.ly/2KrVtSX

    This video uses a simplified loan portfolio dataset to make it easier to understand the workflow.

    The example shows how to:
    • Import data into the MATLAB workspace
    • Join two tables together using the outerjoin function
    • Fit data using a generalized linear model
    • Predict the probability of default (PD) based on the fitted model and adverse economic scenarios
    • Calculate the expected loss using predicted PD

    Get a free product Trial: https://goo.gl/ZHFb5u
    Learn more about MATLAB: https://goo.gl/8QV7ZZ
    See What's new in MATLAB and Simulink: https://goo.gl/pgGtod

    © 2018 The MathWorks, Inc. MATLAB and Simulink are registered
    trademarks of The MathWorks, Inc.
    See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders. Show less
    Read more
  • Introduction to Deep Learning Play all

    Watch this series of MATLAB Tech Talks to explore key deep learning concepts. Learn to identify when to use deep learning, discover what approaches are suitable for your application, and explore some of the challenges you might encounter.
    This item has been hidden
  • Understanding Control Systems Play all

    Learn the basic concepts behind controls systems. Walk through everyday examples that outline fundamental ideas, and explore open-loop and feedback control systems.

    These videos explore open-loop systems that are found in everyday appliances like toasters or showers. The series illustrates how you can tune these systems using trial-and-error to achieve a desired output. You’ll also learn about situations where open-loop control may fail due to unexpected environmental changes (disturbances) or variations in the system.

    Next, you’ll explore the working principles behind feedback control, and discover how it deals with the shortcomings of open-loop control. Basic components of a feedback control system (such as “plants,” “actuators,” and “sensors”) are discussed, along with how these components interact with each other to form a closed-loop control system. You’ll discover how disturbances acting on the plant can affect system output in an undesired way, and how feedback control can compensate for such disturbances. The video series also discusses how noise can enter the system through measurement, which affects the measured output.

    Finally, you’ll learn to use MATLAB and Simulink to model and simulate some of the open-loop and feedback control systems introduced in this series.
    This item has been hidden
  • Understanding Kalman Filters Play all

    Discover real-world situations in which you can use Kalman filters. Kalman filters are often used to optimally estimate the internal states of a system in the presence of uncertain and indirect measurements. Learn the working principles behind Kalman filters by watching some introductory examples.
    You will explore situations where Kalman filters are commonly used. When the states of a system can only be measured indirectly, then Kalman filter can be used to optimally estimate the states of the system. And when measurements from different sensors are available but subject to noise, Kalman filter is used to combine sensory data from various sources (known as sensor fusion) to find the best estimate of the parameter of interest.
    You will also learn about state observers by walking through some examples and simple math. This will help you understand what a Kalman filter is and how it works. At a high level, Kalman filters are a type of optimal state estimator. The videos include a discussion of nonlinear state estimators, such as extended and unscented Kalman filters.
    Finally, an example demonstrates how the states of a linear system can be estimated using Kalman filters, MATLAB®, and Simulink®.
    This item has been hidden
  • Understanding PID Control Play all

    This series provides an introduction to proportional-integral-derivative (PID) control.

    PID is just one form of feedback controller, and it can be fairly easy to understand and implement. It is the simplest type of controller that uses the past, present, and future error, and it’s these primary features that you need to satisfy most control problems. That is why PID is the most prevalent form of feedback control for a wide range of real applications.

    Often, when learning something new in control theory, it’s easy to get bogged down in the detailed mathematics of the problem. So in this series, we’re going to skip most of the math and instead focus on building a solid foundation.

    Throughout this series, you’ll learn what a PID controller is, how to modify it to make it more robust, and you’ll get an overview of tuning methods. Along the way, you’ll understand how PID controllers are used to handle practical applications like actuator saturation and the anti-windup algorithms that protect against it, sensor noise and the derivative filter that is required, and multi-loop control.
    This item has been hidden
  • Understanding Bode Plots Play all

    MATLAB Tech Talks on Bode plot.
    This item has been hidden
  • Using Bode Plots Play all

    This item has been hidden
  • Understanding Wavelets Play all

    Watch the videos in this series to learn the basics behind applications such as wavelet-based denoising and compression. You will learn fundamental concepts in wavelet analysis such as what wavelets are and how to scale and shift them. You will get an overview of the continuous and discrete wavelet transforms, and you will also see example applications of how to use these transforms in MATLAB®.
    This item has been hidden
  • Understanding Discrete-Event Simulation - MATLAB Tech Talks Play all

    Watch the videos in this MATLAB® Tech Talk series to learn the fundamentals behind discrete-event simulation. Discrete-event simulation is a simple, versatile way of describing a process. You can use it to build complex models that explore fundamental questions such as latency, utilization, and bottlenecks. The video series also outlines how to use stochastic processes to approximate details of a system that you can’t model. You’ll learn about how you can use discrete-event simulation for operations research, and you’ll explore how to use discrete-event simulation to evaluate the performance of digital communication systems.
    This item has been hidden
  • Understanding State Machines Play all

    Watch the videos in this series to learn the fundamental concepts of state machines. A state machine is a model that describes the behavior of a system in each state. It defines how the system should transition between these states when certain conditions are true. State machines are used to model logic in many dynamic systems such as automobiles, aircraft, robots, and mobile phones.
    This item has been hidden
  • Coder Summits Play all

    Coder Summit talks are from an annual technical interchange involving MathWorks field engineers and development staff. These talks are your direct lens into the debut of key MATLAB® and Simulink® capabilities for embedded code generation, software design, code verification, and certification.

    Learn more about embedded system solutions: https://goo.gl/kCqw3F

    Learn more about Embedded Coder: https://goo.gl/4uLCr2

    Try the Production Code Generation Evaluation Kit: https://goo.gl/pC8zgk

    Generating code from MATLAB and Simulink using MATLAB Coder™, Simulink Coder™, and Embedded Coder® is established as an approach for production software development that saves time and improves quality over hand coding. Yet, engineers using coder products may seek more advanced features to support their next generation designs and hardware. As such, MathWorks development and engineering staff are continuously working on improvements and often first share these at Coder Summits.

    Topics include:
    • Advanced code optimizations that leverage parallel processing hardware such as SIMD and multicore
    • New semantics for software design including service-oriented architecture (SOA) and C++ code generation
    • Optimizing designs and code based on the available resources and word sizes on your processor
    • Automated code verification and profiling such as processor-in-the-loop (PIL) testing.
    • Support for industry standards such AUTOSAR, DO-178, and ISO 26262

    Now, select Coder Summit talks are available as a video series for all to watch. We hope you enjoy learning directly from the experts that created the features and examples you need for your production code generation success.
    This item has been hidden
to add this to Watch Later

Add to

Loading playlists...