This presentation provides a highlight of the key issues in the management of Market Risk. It touches briefly some of the elements of the Basel 2 Accord with respect to Market Risk
Given the recent financial crisis and the extended impact on global credit market and liquidity, it is imperative that financial institutions strengthen their market risk management capabilities to effectively meet compelling business objectives and challenges which include portfolio pricing and portfolio exposure management
This presentation provides a highlight of the key issues in the management of Market Risk. It touches briefly some of the elements of the Basel 2 Accord with respect to Market Risk
Given the recent financial crisis and the extended impact on global credit market and liquidity, it is imperative that financial institutions strengthen their market risk management capabilities to effectively meet compelling business objectives and challenges which include portfolio pricing and portfolio exposure management
Interest rate risk management for banks under Basel II, presentation by Christine Brown, Department of Finance , The University of Melbourne, Shanghai, December 8-12, 2008
Fixed Income securities- Analysis and Valuation. Very useful for CFA and FRM level 1 preparation candidates. For a more detailed understanding, you can watch the webinar video on this topic. The link for the webinar video on this topic is https://www.youtube.com/watch?v=r9j6Bu3aUNI
RISK & RETURN UNDER SECURITY ANALYSIS AND PORTFOLIO MANAGEMENT IS DESCRIBED, ALL THE DETAILED EXPLANATION OF TOPIC IS GIVEN UNDER THIS DOCUMENT.
CAN ALSO REFERRED FOR FINANCIAL MANAGEMENT, INSURANCE.
This material takes a pragmatic look at how the risks in the Treasury operations of a Bank can best be managed. It identifies the risks in the treasury function of a bank and highlights the need for an ERM approach for optimality.
In this article how risk management in banks is an important concept, what type of risks banks faces and how they curb it through risk management model is described
Interest rate risk management for banks under Basel II, presentation by Christine Brown, Department of Finance , The University of Melbourne, Shanghai, December 8-12, 2008
Fixed Income securities- Analysis and Valuation. Very useful for CFA and FRM level 1 preparation candidates. For a more detailed understanding, you can watch the webinar video on this topic. The link for the webinar video on this topic is https://www.youtube.com/watch?v=r9j6Bu3aUNI
RISK & RETURN UNDER SECURITY ANALYSIS AND PORTFOLIO MANAGEMENT IS DESCRIBED, ALL THE DETAILED EXPLANATION OF TOPIC IS GIVEN UNDER THIS DOCUMENT.
CAN ALSO REFERRED FOR FINANCIAL MANAGEMENT, INSURANCE.
This material takes a pragmatic look at how the risks in the Treasury operations of a Bank can best be managed. It identifies the risks in the treasury function of a bank and highlights the need for an ERM approach for optimality.
In this article how risk management in banks is an important concept, what type of risks banks faces and how they curb it through risk management model is described
MODULE 4:
Market Risk (includes asset liability management)
Yield Curve Risk Factor-Domestic and global contexts-handling multiple risk factor-principal component analysis- value at Risk (VAR) – implementation of a VAR system- Additional Risk in fixed income markets-Stress testing- Bank testing.
Building An Anti-Fragile Investment Portfoliogjohnsen
How to build an investment portfolio which withstands the uncertainties and stresses of life. An anti-fragile portfolio is the opposite of a portfolio which breaks under stress.
In this presentation you will be introduced to the “Various Risk Factors in Banking”, which will help you understand the components and types of risk and it’s peril on the banking sector and risk terminologies used in the banking sector.
To know more about Welingkar School’s Distance Learning Program and courses offered, visit:
http://www.welingkaronline.org/distance-learning/online-mba.html
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. MARKET RISK
• Old wisdom dictates that one should avoid putting all eggs inOld wisdom dictates that one should avoid putting all eggs in
the same basket.the same basket.
• Diversification does not reduce risk below a minimum level.Diversification does not reduce risk below a minimum level.
• The minimum level of risk which is attributable to factors whichThe minimum level of risk which is attributable to factors which
are external to portfolio is MARKET or SYSTEMATIC RISK.are external to portfolio is MARKET or SYSTEMATIC RISK.
• The component of risk which can be reduced by diversificationThe component of risk which can be reduced by diversification
is NON SYSTEMATIC RISK.is NON SYSTEMATIC RISK.
3. What is Market Risk ?
• The possibility of loss to a Bank caused by
changes in market variables.
• Risk to the Bank’s Earnings & Capital due to
changes in the market level of Interest rates
or prices of Securities, Foreign Exchange,
Commodities & Equities as well as volatilities
in the prices
5. WHAT IS LIQUIDITY?
• Ability to fund increases in assets & meet
payment obligations on due date efficiently
& economically.
6. WHAT IS LIQUIDITY RISK?
• “ Potential inability to meet the Bank’s
liabilities on the due date.”
• Arises due to :
• 1. Inability to generate cash to cope with
decline in deposits or increase in assets.
• 2. Mismatches in maturity pattern of
assets & liabilities.
7. Liquidity Risk – Why to Manage?
1. Demonstrates safety of Bank & provides confidence.
2. Necessary to nurture relationship.
3. Avoid unprofitable sale of assets.
4. Lowers the default risk premium.
5 Reduces the need to resort to borrowings from the
Central Bank.
9. Liquidity Risk – how to manage?
• Have an effective Liquidity management policy.
• Should speak out: Funding Strategies, Liquidity
planning under alternative scenario, Prudential
limits, Liquidity reporting & reviewing.
• Tracking of cash flow mismatches. Track the
impact of prepayment of loans & pre mature
closure of deposits.
10. Cap on Inter bank borrowings.
Purchased funds vis-à-vis core assets.
Core deposits vis-à-vis Core assets.
Duration of liabilities & Investment portfolio
Cumulative mismatches across all time
bands.
Commitment ratio – tracking commitments
given
11. Monitoring high value deposits.
Seasonal pattern of deposits/loans.
Potential liquidity needs for meeting
new loan demands, un-availed credit
limits, potential deposit losses,
investment obligations, statutory
obligations etc.
Contingency funding plans.
12. INTEREST RATE RISK
• Deregulation of interest rates has exposed the
Banks to adverse impact of Interest rate risk.
• Risk that value of Assets & Liabilities as also Net
Interest Income get affected due to movements in
interest rates.
• Mismatch in cash flows or re-pricing dates of
assets & liabilities expose Bank’s NII or NIM to
variations
13. INTEREST RATE RISKS- TYPES
1. Mismatch or Gap Risk.
2. Embedded Option Risk.
3. Reinvestment Risk.
4. Price Risk.
14. Rigidities on Interest Rate Risk
• Most liabilities are on Fixed rate basis while
assets are on the floating rate basis.
• There is no definite interest rate re-pricing date
for floating rate.
• Banks have to strengthen the MIS & Computer
processing capabilities for accurate measurement
of IRR
16. FOREIGN EXCHANGE RISK
• Risk that Bank may suffer loss as result of
adverse exchange rate movements during
the period in which it has open position.
• Risk arises whenever business has
income/expenditure, asset/liability in a
currency other than balance sheet
currency.
17. TYPES OF FOREX RISK
1. Open or Mismatch positions.
2. Price Risk.
4. Credit Risk.
5. Country Risk.
6. Operating Risk.
7. Legal Risk.
18. FOREX RISK MANAGEMENT
1. Set appropriate limit for open
position/gaps.
2. Clear cut & well defined division of
responsibility between front, middle & back
office.
3. Use of hedging tools like forwards, futures
& options.
19. EQUITY PRICE RISK
• Changes in Equity prices can result losses
to the bank holding Equity portfolio.
• Banks are not allowed to sell the securities
with out holding the same.
• Banks are free to acquire
Shares/Debs/Units of equity oriented
mutual funds subject to ceiling of 5% of the
total domestic credit
20. Equity Price Risk - Management
1. Build up adequate exposure to equity market
2. Formulate transparent policy & procedure for
investment in shares.
3. Formation of Investment committee.
4. Review of Investment portfolio – on going basis.
5. Fixing prudential exposure limits.
21. COMMODITY PRICE RISK
Banks have very little exposure to
commodities in their trading book.
Price rise/movement in commodities is more
complex & volatile.
Banks in developed countries use
derivatives to hedge commodity price risk.
Banks in India have to acquire the skills to
manage as & when they get exposed to
commodity price risk.