You are viewing this post: Are inflation fears priced into the market? | liquidity risk
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Are inflation fears priced into the market?
นอกจากการดูบทความนี้แล้ว คุณยังสามารถดูข้อมูลที่เป็นประโยชน์อื่นๆ อีกมากมายที่เราให้ไว้ที่นี่: ดูความรู้เพิ่มเติมที่นี่
Priya Misra, global head of rates strategy at TD Securities, and Stephanie Link, Hightower’s chief investment strategist, join Closing Bell to discuss the markets and whether they’re pricing in risks from inflation and Fed policy. For access to live and exclusive video from CNBC subscribe to CNBC PRO: https://cnb.cx/2NGeIvi
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Machine Learning Framework for Liquidity Risk Management (Cloud Next ’19)
Financial models have always been impacted by the lack of data (or many highly noisy data), by necessary mathematical simplifications such as normality or linearity assumptions and by a limited ability to use a wide set of features to better describe the problem and have greater predictive power. This is true in risk management, trading and portfolio construction, but even more so in liquidity models.
Liquidity is a multidimensional beast that economists, quants and statisticians have tried to understand for several decades. This type of problem has a very high dimensionality and highly nonlinear patterns and very sparse data.
The nature of the problem lends itself to be faced with machine learning techniques.
We have therefore decided over the years to test some of these techniques in the calibration of models for liquidity.
In this research, leveraging GPUs and cloud, we focused on the estimation of market liquidity, in particular of the transaction cost.
In this research, we tested random forests and neural networks for the estimation of tradable volumes showing a significant increase in the outofsample performances. We are now extending the experiment to the entire transaction cost and not to a single component of it by testing deep learning and in particular deep reinforcement learning.
In the application of these more advanced and complex techniques, we are paying particular attention to the ongoing research on the interpretability (XAI), which is a necessary condition and not yet completely resolved for extensive use of Deep Learning in finance.
Liquidity Risk Mangement → http://bit.ly/2WQBYtk
Watch more:
Next ’19 ML \u0026 AI Sessions here → https://bit.ly/Next19MLandAI
Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions
Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform
Speaker(s): Stefano Pasquali
Session ID: MLAI232
product: Cloud General; fullname: Stefano Pasquali; event: Google Cloud Next 2019;
Bank Credit Risk Management
In banking, credit risk refers to the risk arising out of an individual counterparty (a borrower or a lender) failing to meet or being prevented from meeting its obligations. Credit risk management is important for a bank to increase shareholders value. A bank can have risk model to manage credit risk. A bank can manage credit risk through robust credit analysis, reducing concentration at a portfolio, maintaining diversified loan portfolio, internal credit rating, etc.
Bank Risk Management: Liquidity Risk
Using a simple working example, this video describes the impact of liquidity risk on bank net worth.
What are Derivatives ?
An introduction to Derivatives.
นอกจากการดูหัวข้อนี้แล้ว คุณยังสามารถเข้าถึงบทวิจารณ์ดีๆ อื่นๆ อีกมากมายได้ที่นี่: ดูวิธีอื่นๆINVESTMENT
Articles compiled by CASTU. See more articles in category: INVESTMENT
