This document describes CLEANTM, a patented MBS valuation approach. It models prepayments by treating mortgages as callable amortizing bonds, with homeowners refinancing optimally based on interest rates and volatility. Other prepayments are modeled statically. Two yield curves are used - one for mortgage rates and one for MBS yields. Key inputs include laggard distributions, turnover speeds, and homeowner credit spreads calibrated to agency spreads. CLEANTM durations and OAS closely track dealer models and indexes. It is well-suited for trading due to its realistic and transparent behavior based on financial principles rather than statistical fitting.
Interest rate risk management what regulators want in 2015 7.15.2015Craig Taggart MBA
Areas covered in this section
Why Interest Rate Risk (IRR) should not be ignored
• Forward Rate Agreements (FRA’s) Forwards, Futures
• Swaps, Options
Why Bank Regulators continue to have a poor handle on interest rate risk
• Interest Rate Caps, floors, Collars
• LIBOR and UBS & Barclays rigging rates
• How should Financial Institutions determine which IRR vendor models are appropriate?
IRR Measurement methodologies are institutions
Creating Shareholder Value in Midcap BanksJohn Rickmeier
The mission of a bank is to operate a safe and sound financial institution, while creating shareholder value. The value of a bank, defined by the ratio of market value to common equity, most often is directly related to the return on equity (ROE) less the cost of equity capital (COE).
Interest-rate risk substantially affect the values of the assets and liabilities of most corporations and is often a dominant factor affecting the values of pension funds, banks and many other financial intermediaries.
Stochastic modelling of the loss given default (LGD) for non-defaulted assetsGRATeam
In the Basel framework of credit risk estimation, banks seek to develop precise and stable internal models to limit their capital charge. Following the recent changes in terms of regulatory requirements (Basel regulation, definition of the Downturn…), it is prudent to think about innovative methods to estimate the credit risk parameters with the constrains of models’ stability, robustness, and economic cycles sensitivity.
This paper introduces a different recovery forecasting methodology for LGD (loss given default) parameter. The goal is to model the recovery dynamic by assuming that each maturity in default has a specific behavior and that the recovery rate depends on default generation change. The model focuses on the recovery rate time series where the time period is the default generation. Thus, the estimation of upcoming recoveries uses vertical diffusions, where the triangle’s columns are completed one by one through stochastic processes. This model is suggested to replace classical horizontal forecasting with Chain-Ladder methods.
First, a definition of the LGD parameter and the regulatory modelling requirements are provided, as well as a presentation of the data set used and the construction of the recovery triangle. Second, the stochastic forecasting is introduced with details of how to calibrate the model. Third, three classical methods of recovery forecasting based on Chain-Ladder are presented for comparison and to contest and the stochastic methodology. Finally, a regulatory calibration of the LGD for non-defaulted assets is proposed to include Downturn effects and margins of prudence.
BONDS, FEATURES OF BONDS, BOND VALUATION, MEASURING YIELD, ASSESSING RISK, TYPES OF LONG- TERM DEBT INSTRUMENTS, SERIAL BONDS, TYPES OF RISK, SEMI- ANNUAL BONDS, YIELD TO CALL, YIELD TO MATURITY, DEFAULT RISK & FACTORS AFFECTING DEFAULT RISK & BOND RATINGS, etc.
Interest rate risk management what regulators want in 2015 7.15.2015Craig Taggart MBA
Areas covered in this section
Why Interest Rate Risk (IRR) should not be ignored
• Forward Rate Agreements (FRA’s) Forwards, Futures
• Swaps, Options
Why Bank Regulators continue to have a poor handle on interest rate risk
• Interest Rate Caps, floors, Collars
• LIBOR and UBS & Barclays rigging rates
• How should Financial Institutions determine which IRR vendor models are appropriate?
IRR Measurement methodologies are institutions
Creating Shareholder Value in Midcap BanksJohn Rickmeier
The mission of a bank is to operate a safe and sound financial institution, while creating shareholder value. The value of a bank, defined by the ratio of market value to common equity, most often is directly related to the return on equity (ROE) less the cost of equity capital (COE).
Interest-rate risk substantially affect the values of the assets and liabilities of most corporations and is often a dominant factor affecting the values of pension funds, banks and many other financial intermediaries.
Stochastic modelling of the loss given default (LGD) for non-defaulted assetsGRATeam
In the Basel framework of credit risk estimation, banks seek to develop precise and stable internal models to limit their capital charge. Following the recent changes in terms of regulatory requirements (Basel regulation, definition of the Downturn…), it is prudent to think about innovative methods to estimate the credit risk parameters with the constrains of models’ stability, robustness, and economic cycles sensitivity.
This paper introduces a different recovery forecasting methodology for LGD (loss given default) parameter. The goal is to model the recovery dynamic by assuming that each maturity in default has a specific behavior and that the recovery rate depends on default generation change. The model focuses on the recovery rate time series where the time period is the default generation. Thus, the estimation of upcoming recoveries uses vertical diffusions, where the triangle’s columns are completed one by one through stochastic processes. This model is suggested to replace classical horizontal forecasting with Chain-Ladder methods.
First, a definition of the LGD parameter and the regulatory modelling requirements are provided, as well as a presentation of the data set used and the construction of the recovery triangle. Second, the stochastic forecasting is introduced with details of how to calibrate the model. Third, three classical methods of recovery forecasting based on Chain-Ladder are presented for comparison and to contest and the stochastic methodology. Finally, a regulatory calibration of the LGD for non-defaulted assets is proposed to include Downturn effects and margins of prudence.
BONDS, FEATURES OF BONDS, BOND VALUATION, MEASURING YIELD, ASSESSING RISK, TYPES OF LONG- TERM DEBT INSTRUMENTS, SERIAL BONDS, TYPES OF RISK, SEMI- ANNUAL BONDS, YIELD TO CALL, YIELD TO MATURITY, DEFAULT RISK & FACTORS AFFECTING DEFAULT RISK & BOND RATINGS, etc.
Sps whitepaper auvesy datenmanagement in der automatisierungstechnikAUVESY
Das Whitepaper beinhaltet sechs Fachbeiträge zum Thema Datenmanagement in der Automatisierungstechnik und ist in Zusammenarbeit mit dem SPS-Magazin entstanden.
Denn bei Automatisierungs-Applikationen fallen eine Unmenge unterschiedlicher Daten an, deren Versionen verwaltet und jederzeit gesichert werden müssen. Eine Software bzw. ein Datenmanagement-System wie versiondog bietet hierbei eine sichere Lösung für diese Aufgabenstellung.
Mehr Infos zum Versionieren, Dokumentieren und Backup mit versiondog erfahren Sie unter www.versiondog.de
MindManager 9 - A solução ideal para Mapas MentaisSaldit Software
MIndManager 9 é a nova versão do software da MIndjet fornecido pela Saldit Software no Brasil. A Saldit atua com comercialização de softwares nacionais e importados. Fale com a equipe comercial: vendas@saldit.com.br
1º serviço de e-mail grátis do Brasil, hoje são mais de 3,7 MM de contas ativas
O BOL ainda conta com vasto conteúdo de Entretenimento e Notícias
Conheça os projetos que o BOL oferece para divulgar sua marca na internet.
This presentation discusses the criteria an institution should use to evaluate its ALLL, recommendations and best practices to support a change and key areas examiners investigate after a significant change to the ALLL. See how automation can help: http://web.sageworks.com/alll/
In-spite of large volumes of Contingent Credit Lines (CCL) in all commercial banks, the paucity of Exposure at Default (EAD) models, unsuitability of external data and inconsistent internal data with partial draw-downs has been a major challenge for risk managers as well as regulators in for managing CCL portfolios. This current paper is an attempt to build an easy to implement, pragmatic and parsimonious yet accurate model to determine the exposure distribution of a CCL portfolio. Each of the credit line in a portfolio is modeled as a portfolio of large number of option instruments which can be exercised by the borrower, determining the level of usage. Using an algorithm similar to basic the CreditRisk+ and Fourier Transforms we arrive at a portfolio level probability distribution of usage. We perform a simulation experiment using data from Moody\'s Default Risk Service, historical draw-down rates estimated from the history of defaulted CCLs and a current rated portfolio of such.
IFRS 9 defines “Credit Loss” in terms of “Cash Shortfall” or credit loss estimation through projected cash flow discounting. However, there is little explicit information available as to how “Cash Shortfall” should be computed; should it be computed separately or along with “Expected” default path of the borrower, leading to ambiguity around the subject. IFRS 9 has specifically given inputs on PD estimation however, on LGD there is no such specific directives available. The ambiguity around the subject raises a few questions. The blog explores the limits of current knowledge (theoretical and empirical), and offers some preliminary guidance on such questions.
bank core deposits: comparison between new Weibull approach and old U.S. OTS assumptions. Result: increased profitability with better product pricing, improved franchise value metrics, and better liquidity assumptions,
Continuing with our updates on the key aspects of IFRS 9 Implementation, our current post (attached) talks about “Impairment Modelling in Retail ” where, key challenges are highlighted through questions and different solutions are proposed to address the same. The post attempts to address some key implementation challenges such as; Which approach to follow for analysis of retail portfolios?, What timeframe to consider for estimating lifetime of retail products?, How to link forward looking information with PDs? How to carry out Stage Allocation? And, what are the methods for calculation of ECL for Retail Portfolios?
Seminar: Gender Board Diversity through Ownership NetworksGRAPE
Seminar on gender diversity spillovers through ownership networks at FAME|GRAPE. Presenting novel research. Studies in economics and management using econometrics methods.
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the contact information for my personal pi vendor.
Telegram: @Pi_vendor_247
Lecture slide titled Fraud Risk Mitigation, Webinar Lecture Delivered at the Society for West African Internal Audit Practitioners (SWAIAP) on Wednesday, November 8, 2023.
what is the best method to sell pi coins in 2024DOT TECH
The best way to sell your pi coins safely is trading with an exchange..but since pi is not launched in any exchange, and second option is through a VERIFIED pi merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and pioneers and resell them to Investors looking forward to hold massive amounts before mainnet launch in 2026.
I will leave the telegram contact of my personal pi merchant to trade pi coins with.
@Pi_vendor_247
how to sell pi coins on Bitmart crypto exchangeDOT TECH
Yes. Pi network coins can be exchanged but not on bitmart exchange. Because pi network is still in the enclosed mainnet. The only way pioneers are able to trade pi coins is by reselling the pi coins to pi verified merchants.
A verified merchant is someone who buys pi network coins and resell it to exchanges looking forward to hold till mainnet launch.
I will leave the telegram contact of my personal pi merchant to trade with.
@Pi_vendor_247
If you are looking for a pi coin investor. Then look no further because I have the right one he is a pi vendor (he buy and resell to whales in China). I met him on a crypto conference and ever since I and my friends have sold more than 10k pi coins to him And he bought all and still want more. I will drop his telegram handle below just send him a message.
@Pi_vendor_247
when will pi network coin be available on crypto exchange.DOT TECH
There is no set date for when Pi coins will enter the market.
However, the developers are working hard to get them released as soon as possible.
Once they are available, users will be able to exchange other cryptocurrencies for Pi coins on designated exchanges.
But for now the only way to sell your pi coins is through verified pi vendor.
Here is the telegram contact of my personal pi vendor
@Pi_vendor_247
The Evolution of Non-Banking Financial Companies (NBFCs) in India: Challenges...beulahfernandes8
Role in Financial System
NBFCs are critical in bridging the financial inclusion gap.
They provide specialized financial services that cater to segments often neglected by traditional banks.
Economic Impact
NBFCs contribute significantly to India's GDP.
They support sectors like micro, small, and medium enterprises (MSMEs), housing finance, and personal loans.
The secret way to sell pi coins effortlessly.DOT TECH
Well as we all know pi isn't launched yet. But you can still sell your pi coins effortlessly because some whales in China are interested in holding massive pi coins. And they are willing to pay good money for it. If you are interested in selling I will leave a contact for you. Just telegram this number below. I sold about 3000 pi coins to him and he paid me immediately.
Telegram: @Pi_vendor_247
What website can I sell pi coins securely.DOT TECH
Currently there are no website or exchange that allow buying or selling of pi coins..
But you can still easily sell pi coins, by reselling it to exchanges/crypto whales interested in holding thousands of pi coins before the mainnet launch.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and resell to these crypto whales and holders of pi..
This is because pi network is not doing any pre-sale. The only way exchanges can get pi is by buying from miners and pi merchants stands in between the miners and the exchanges.
How can I sell my pi coins?
Selling pi coins is really easy, but first you need to migrate to mainnet wallet before you can do that. I will leave the telegram contact of my personal pi merchant to trade with.
Tele-gram.
@Pi_vendor_247
USDA Loans in California: A Comprehensive Overview.pptxmarketing367770
USDA Loans in California: A Comprehensive Overview
If you're dreaming of owning a home in California's rural or suburban areas, a USDA loan might be the perfect solution. The U.S. Department of Agriculture (USDA) offers these loans to help low-to-moderate-income individuals and families achieve homeownership.
Key Features of USDA Loans:
Zero Down Payment: USDA loans require no down payment, making homeownership more accessible.
Competitive Interest Rates: These loans often come with lower interest rates compared to conventional loans.
Flexible Credit Requirements: USDA loans have more lenient credit score requirements, helping those with less-than-perfect credit.
Guaranteed Loan Program: The USDA guarantees a portion of the loan, reducing risk for lenders and expanding borrowing options.
Eligibility Criteria:
Location: The property must be located in a USDA-designated rural or suburban area. Many areas in California qualify.
Income Limits: Applicants must meet income guidelines, which vary by region and household size.
Primary Residence: The home must be used as the borrower's primary residence.
Application Process:
Find a USDA-Approved Lender: Not all lenders offer USDA loans, so it's essential to choose one approved by the USDA.
Pre-Qualification: Determine your eligibility and the amount you can borrow.
Property Search: Look for properties in eligible rural or suburban areas.
Loan Application: Submit your application, including financial and personal information.
Processing and Approval: The lender and USDA will review your application. If approved, you can proceed to closing.
USDA loans are an excellent option for those looking to buy a home in California's rural and suburban areas. With no down payment and flexible requirements, these loans make homeownership more attainable for many families. Explore your eligibility today and take the first step toward owning your dream home.
2. 2
Heard It Through The Grapevine
"The actual sensitivity of MSRs to implied volatility
is complex and somewhat controversial”
Ben Golub in "Mark-to-Market Methodology, Mortgage
Servicing Rights, and Hedging Effectiveness“
“The model we use doesn’t even get the sign right
for volatility hedging of MSRs”
A/L management advisor
“The price response to skew adjustment seems
exaggerated”
Hedge fund manager
Why do intuition and model disagree
when it comes to volatility?
3. 3
Observations
Modeling prepayments is only a means to an end
The goal is proper valuation and risk measurement
A mortgage is a callable amortizing bond
Prepayment models should be consistent with callable
bond models
Bonds (mortgages) are refunded (refinanced) when
the call option is worth more dead than alive
Therefore bond and mortgage models should respond
similarly to interest rate levels and volatility changes
4. 4
Dynamic Versus Static Variables
In an MBS model
Interest rate driven prepayments
Dynamically hedged
Modeled using a stochastic interest rate process
Other prepayments, such as turnover and defaults
Either not hedged or statically hedged
Modeled statically in CLEAN
5. 5
A financial engineer homeowner uses an option
valuation model
Refinances optimally
Others refinance too early or too late
Early refinancers are called “leapers”
Rarely occurs
Late refinancers are called “laggards”
AKA analysis shows that the 50 bps rule of thumb
is sensible
Most homeowners refinance near-optimally!
Optimum Option Exercise Provides
Benchmark for Suboptimal Behavior
6. 6
MBS Valuation Using CLEAN™
Two separate yield curves are required
One calibrated to mortgage rates
Other calibrated to MBS yields
Modeled using coupled lattice
Mortgage rates used to determine refis
Using notion of call efficiency
MBS rates used for discounting MBS cash
flows
7. 7
Benchmark yield curve and volatility
USD swap curve and appropriate swaption vol
Prepayment parameters
Laggard distribution
Turnover speed vector
Default/buyout speed and recovery percentage vectors
Refinancing cost
Fixed percentage of original principal
Homeowner credit spread
Analogous to corporate credit spread
MBS price/OAS
For EOD pricing, use OAS calibrated to TBA prices
CLEAN™ Model Input Parameters
8. 8
Calibration of CLEAN™:
Straightforward and Intuitive
Rarely adjusted
Laggard distribution
Turnover speed
Default recovery percentage
Refinancing cost
Occasionally adjusted
Homeowner credit spread
Default/buyout speed
9. 9
Calibrating Homeowner Credit Spread
For Agency Pools
Should be consistent with prevailing mortgage rates
Approximately 120 bps for current coupon pools
Implies refi option premium of approximately 40 bps
Higher credit spread for higher coupon collateral
Implies weaker credit, ceteris paribus
Calibrated to dealer consensus duration/convexity, and specified pool
pay-up grids
Additional factors that can be incorporated:
Fannie/Freddie vs. Ginnie/FHA
LTV, FICO
Year of origination
Loan size
Percent of non-owner occupied (low refi rate, high turnover rate)
Average points paid
Credit migration
15. 15
TBA Price Movement vs. Model Implied
Delta Movement
Actual Change in TBA Market Price vs. Sum of Implied Price
Change Due to Risk Factors (1/2/2009 - 7/1/2010)
-3
-2
-1
0
1
2
3
1/1/2009 4/2/2009 7/2/2009 10/1/2009 12/31/2009 4/1/2010 7/1/2010
%ofpar
FNCL 4.5 Δ price
Σ implied price change due to risk factors
16. 16
Why CLEAN™ Is Ideal for
Trading, Hedging, and Risk Management
Realistic transparent behavior
Based on well established financial and economic principles
Instead of mysterious mathematical formulas and parameters
Consistent with valuation models for callable bonds and
cancelable swaps
Calibration is straightforward and intuitive
Concretely defined model parameters
Easier to simulate
Model behavior always realistic
Based on fundamental financial and economic principles
Not on statistical fitting of historical behavior
And ridiculously fast
Criticial for simulation
17. 17
Modeling prepayments
Turnover and defaults modeled using deterministic speeds
Refinancings modeled using stochastic interest rate model
Modeling a mortgage
As a callable amortizing bond
A financial engineer will refinance when the option is worth more
dead than alive
Others will refinance too early (never really happens) or too late
(“laggards”)
Modeling heterogeneous refinancing behavior
Divide mortgage pool into 10 buckets according to laggard
parameter
Use a standard laggard distribution for a new pool
Modeling seasoned pools
Fastest refinancing buckets disappear first
Automatically accounts for ‘burnout’
The CLEAN™ Way
18. 18
References
Andrew Kalotay & Qi Fu (June 2009), A Financial Analysis of
Consumer Mortgage Decisions, Mortgage Bankers
Association.
Andrew Kalotay & Qi Fu (May 2008), Mortgage servicing rights
and interest rate volatility, Mortgage Risk.
Andrew Kalotay, Deane Yang, & Frank Fabozzi (Vol. 1, 2008),
Optimum refinancing: bringing professional discipline to
household finance, Applied Financial Economics Letters.
Andrew Kalotay, Deane Yang, & Frank Fabozzi (Vol. 3, 2007),
Refunding efficiency: a generalized approach, Applied
Financial Economics Letters.
Andrew Kalotay, Deane Yang, & Frank Fabozzi (December 2004),
An option-theoretic prepayment model for mortgages
and mortgage-backed securities, International Journal of
Theoretical and Applied Finance.
Available from http://www.kalotay.com/research