The document presents a model for estimating exposure at default (EAD) for contingent credit lines (CCLs) at the portfolio level. It models each CCL as a portfolio of put options, with the exercise of each put following a Poisson process. The model convolutes the usage distributions of individual obligors, sub-segments, and segments to estimate the portfolio-level EAD distribution. The authors test the model using data from Moody's and find near-Gaussian results. They discuss future work to refine the model and make it more practical for banks to estimate regulatory capital requirements.
Improving Returns from the Markowitz Model using GA- AnEmpirical Validation o...idescitation
Portfolio optimization is the task of allocating the investors capital among
different assets in such a way that the returns are maximized while at the same time, the
risk is minimized. The traditional model followed for portfolio optimization is the
Markowitz model [1], [2],[3]. Markowitz model, considering the ideal case of linear
constraints, can be solved using quadratic programming, however, in real-life scenario, the
presence of nonlinear constraints such as limits on the number of assets in the portfolio, the
constraints on budgetary allocation to each asset class, transaction costs and limits to the
maximum weightage that can be assigned to each asset in the portfolio etc., this problem
becomes increasingly computationally difficult to solve, ie NP-hard. Hence, soft computing
based approaches seem best suited for solving such a problem. An attempt has been made in
this study to use soft computing technique (specifically, Genetic Algorithms), to overcome
this issue. In this study, Genetic Algorithm (GA) has been used to optimize the parameters
of the Markowitz model such that overall portfolio returns are maximized with the standard
deviation of the returns being minimized at the same time. The proposed system is validated
by testing its ability to generate optimal stock portfolios with high returns and low standard
deviations with the assets drawn from the stocks traded on the Bombay Stock Exchange
(BSE). Results show that the proposed system is able to generate much better portfolios
when compared to the traditional Markowitz model.
IFRS 9 Implementation : Using the Z-score approach as a KRI to identify adverse credit deterioration for Stage Transition from 1 to stages 2/3 in IFRS 9 Modeling
These Lecture series are relating the use R language software, its interface and functions required to evaluate financial risk models. Furthermore, R software applications relating financial market data, measuring risk, modern portfolio theory, risk modeling relating returns generalized hyperbolic and lambda distributions, Value at Risk (VaR) modelling, extreme value methods and models, the class of ARCH models, GARCH risk models and portfolio optimization approaches.
Credit Default Swap (CDS) Rate Construction by Machine Learning TechniquesZhongmin Luo
1. Financial institutions need to construct proxy CDS rates for counterparties lacking liquid CDS quotes, which are required for CVA pricing, CVA risk charge calculation, etc;
2. Existing CDS Proxy Methods do not meet regulatory requirements and are vulnerable to arbitrage;
3. After investigating 8 most popular Machine Learning algorithms, we show that Machine Learning techniques can be used to construct reliable CDS proxies that meet regulatory regulations while free from the above problem
4. Feature variable selection can be critical for performance of CDS-proxy construction methods
5. Effects of feature variable correlations on classification performances have to be investigated in the case of financial data
International Journal of Computational Engineering Research (IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
These Lecture series are relating the use R language software, its interface and functions required to evaluate financial risk models. Furthermore, R software applications relating financial market data, measuring risk, modern portfolio theory, risk modeling relating returns generalized hyperbolic and lambda distributions, Value at Risk (VaR) modelling, extreme value methods and models, the class of ARCH models, GARCH risk models and portfolio optimization approaches.
Improving Returns from the Markowitz Model using GA- AnEmpirical Validation o...idescitation
Portfolio optimization is the task of allocating the investors capital among
different assets in such a way that the returns are maximized while at the same time, the
risk is minimized. The traditional model followed for portfolio optimization is the
Markowitz model [1], [2],[3]. Markowitz model, considering the ideal case of linear
constraints, can be solved using quadratic programming, however, in real-life scenario, the
presence of nonlinear constraints such as limits on the number of assets in the portfolio, the
constraints on budgetary allocation to each asset class, transaction costs and limits to the
maximum weightage that can be assigned to each asset in the portfolio etc., this problem
becomes increasingly computationally difficult to solve, ie NP-hard. Hence, soft computing
based approaches seem best suited for solving such a problem. An attempt has been made in
this study to use soft computing technique (specifically, Genetic Algorithms), to overcome
this issue. In this study, Genetic Algorithm (GA) has been used to optimize the parameters
of the Markowitz model such that overall portfolio returns are maximized with the standard
deviation of the returns being minimized at the same time. The proposed system is validated
by testing its ability to generate optimal stock portfolios with high returns and low standard
deviations with the assets drawn from the stocks traded on the Bombay Stock Exchange
(BSE). Results show that the proposed system is able to generate much better portfolios
when compared to the traditional Markowitz model.
IFRS 9 Implementation : Using the Z-score approach as a KRI to identify adverse credit deterioration for Stage Transition from 1 to stages 2/3 in IFRS 9 Modeling
These Lecture series are relating the use R language software, its interface and functions required to evaluate financial risk models. Furthermore, R software applications relating financial market data, measuring risk, modern portfolio theory, risk modeling relating returns generalized hyperbolic and lambda distributions, Value at Risk (VaR) modelling, extreme value methods and models, the class of ARCH models, GARCH risk models and portfolio optimization approaches.
Credit Default Swap (CDS) Rate Construction by Machine Learning TechniquesZhongmin Luo
1. Financial institutions need to construct proxy CDS rates for counterparties lacking liquid CDS quotes, which are required for CVA pricing, CVA risk charge calculation, etc;
2. Existing CDS Proxy Methods do not meet regulatory requirements and are vulnerable to arbitrage;
3. After investigating 8 most popular Machine Learning algorithms, we show that Machine Learning techniques can be used to construct reliable CDS proxies that meet regulatory regulations while free from the above problem
4. Feature variable selection can be critical for performance of CDS-proxy construction methods
5. Effects of feature variable correlations on classification performances have to be investigated in the case of financial data
International Journal of Computational Engineering Research (IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
These Lecture series are relating the use R language software, its interface and functions required to evaluate financial risk models. Furthermore, R software applications relating financial market data, measuring risk, modern portfolio theory, risk modeling relating returns generalized hyperbolic and lambda distributions, Value at Risk (VaR) modelling, extreme value methods and models, the class of ARCH models, GARCH risk models and portfolio optimization approaches.
Should all a- rated banks have the same default risk as lehman?Zhongmin Luo
1. Financial institutions need to construct proxy CDS rates for counterparties lacking liquid CDS quotes, which are required for CVA pricing, CVA risk charge calculation, etc;
2. Existing CDS Proxy Methods do not meet regulatory requirements and are vulnerable to arbitrage;
3. After investigating 8 most popular Machine Learning algorithms, we show that Machine Learning techniques can be used to construct reliable CDS proxies that meet regulatory regulations while free from the above problem
4. Feature variable selection can be critical for performance of CDS-proxy construction methods
5. Effects of feature variable correlations on classification performances have to be investigated in the case of financial data
Auctions for perishable goods such as internet ad inventory need to make real-time allocation
and pricing decisions as the supply of the good arrives in an online manner, without knowing the
entire supply in advance. These allocation and pricing decisions get complicated when buyers
Quantifi Whitepaper: The Evolution Of Counterparty Credit Riskamoini
Written by David Kelly (Head of Credit and Counterparty Risk Product Development, Quantifi) and Jon Gregory (former Head of Counterparty Risk at Barclays Capital)
Due to the limited size of the insurance market, insurance companies usually purchase insurance
from a few reinsurance companies with large differences. At this time, using the Vasicek model to describe the
counterparty credit risk will be inaccurate; besides, the insurance company’s understanding of the counterparty
default threshold distribution is incomplete, which makes it difficult to effectively determine the counterparty
default probability.
This study provides a practical way to anticipate systematic LGD risk. It introduces an LGD function that requires no parameters other than PD, expected LGD, and correlation. This function survives testing against more-elaborate models of corporate credit loss that allow either greater or less LGD risk. Unless a significant improvement were discovered, the LGD function presented here can be used to anticipate systematic LGD risk within a credit loss model or to quantify downturn LGD.
odd-Frank and Basel III Post-Financial Crisis Developments and New Expectations in Regulatory Capital. Following the recent global financial crisis of 2009, financial regulators have responded with arrays of proposals to revise existing risk frameworks for financial institutions with the objective to further strengthen and improve upon bank models. In this meeting, Dr. Michael Jacobs will discuss new developments and expectations in regulatory capital with particular reference to the definition of the capital base, counterparty credit risk, procyclicality of capital, liquidity risk management, and sound compensation practices. He will also explain the implications of the Frank-Dodd rule for financial institutions and will conclude by presenting the implementation schedule for Basel III.
This presentation will survey and discuss various quantitative considerations in liquidity risk for a financial institution. This includes the concept of liquidity-at-risk (LaR) as a determinant of buffers, as well as how one defines and quantifies such buffers. We will also examine issues such as limit-related input for liquidity policy and transfer pricing as an alternative concept. Two stylized models of liquidity risk are presented and analyzed.
Should all a- rated banks have the same default risk as lehman?Zhongmin Luo
1. Financial institutions need to construct proxy CDS rates for counterparties lacking liquid CDS quotes, which are required for CVA pricing, CVA risk charge calculation, etc;
2. Existing CDS Proxy Methods do not meet regulatory requirements and are vulnerable to arbitrage;
3. After investigating 8 most popular Machine Learning algorithms, we show that Machine Learning techniques can be used to construct reliable CDS proxies that meet regulatory regulations while free from the above problem
4. Feature variable selection can be critical for performance of CDS-proxy construction methods
5. Effects of feature variable correlations on classification performances have to be investigated in the case of financial data
Auctions for perishable goods such as internet ad inventory need to make real-time allocation
and pricing decisions as the supply of the good arrives in an online manner, without knowing the
entire supply in advance. These allocation and pricing decisions get complicated when buyers
Quantifi Whitepaper: The Evolution Of Counterparty Credit Riskamoini
Written by David Kelly (Head of Credit and Counterparty Risk Product Development, Quantifi) and Jon Gregory (former Head of Counterparty Risk at Barclays Capital)
Due to the limited size of the insurance market, insurance companies usually purchase insurance
from a few reinsurance companies with large differences. At this time, using the Vasicek model to describe the
counterparty credit risk will be inaccurate; besides, the insurance company’s understanding of the counterparty
default threshold distribution is incomplete, which makes it difficult to effectively determine the counterparty
default probability.
This study provides a practical way to anticipate systematic LGD risk. It introduces an LGD function that requires no parameters other than PD, expected LGD, and correlation. This function survives testing against more-elaborate models of corporate credit loss that allow either greater or less LGD risk. Unless a significant improvement were discovered, the LGD function presented here can be used to anticipate systematic LGD risk within a credit loss model or to quantify downturn LGD.
odd-Frank and Basel III Post-Financial Crisis Developments and New Expectations in Regulatory Capital. Following the recent global financial crisis of 2009, financial regulators have responded with arrays of proposals to revise existing risk frameworks for financial institutions with the objective to further strengthen and improve upon bank models. In this meeting, Dr. Michael Jacobs will discuss new developments and expectations in regulatory capital with particular reference to the definition of the capital base, counterparty credit risk, procyclicality of capital, liquidity risk management, and sound compensation practices. He will also explain the implications of the Frank-Dodd rule for financial institutions and will conclude by presenting the implementation schedule for Basel III.
This presentation will survey and discuss various quantitative considerations in liquidity risk for a financial institution. This includes the concept of liquidity-at-risk (LaR) as a determinant of buffers, as well as how one defines and quantifies such buffers. We will also examine issues such as limit-related input for liquidity policy and transfer pricing as an alternative concept. Two stylized models of liquidity risk are presented and analyzed.
EAD Parameter : A stochastic way to model the Credit Conversion FactorGenest Benoit
This white paper aims at estimating credit risk by modelling the Credit Conversion Factor (CCF) parameter related to the Exposure-at-Default (EAD). It has been decided to perform the estimation thanks to stochastic processes instead of usual statistical methodologies (such as classification tree or GLM).
Our paper will focus on two types of model: the Ornstein Uhlenbeck (OU) model – part of ARMA model types – and the Geometric Brownian Movement (GBM) model. First, we will describe, then implement and calibrate each model to ensure relevance and robustness of our results. Then, we will focus on GBM model to model CCF.
Review Parameters Model Building & Interpretation and Model Tunin.docxcarlstromcurtis
Review Parameters: Model Building & Interpretation and Model Tuning
1. Model Building
a. Assessments and Rationale of Various Models Employed to Predict Loan Defaults
The z-score formula model was employed by Altman (1968) while envisaging bankruptcy. The model was utilized to forecast the likelihood that an organization may fall into bankruptcy in a period of two years. In addition, the Z-score model was instrumental in predicting corporate defaults. The model makes use of various organizational income and balance sheet data to weigh the financial soundness of a firm. The Z-score involves a Linear combination of five general financial ratios which are assessed through coefficients. The author employed the statistical technique of discriminant examination of data set sourced from publically listed manufacturers. A research study by Alexander (2012) made use of symmetric binary alternative models, otherwise referred to as conditional probability models. The study sought to establish the asymmetric binary options models subject to the extreme value theory in better explicating bankruptcy.
In their research study on the likelihood of default models examining Russian banks, Anatoly et al. (2014) made use of binary alternative models in predicting the likelihood of default. The study established that preface specialist clustering or mechanical clustering enhances the prediction capacity of the models. Rajan et al. (2010) accentuated the statistical default models as well as inducements. They postulated that purely numerical models disregard the concept that an alteration in the inducements of agents who produce the data may alter the very nature of data. The study attempted to appraise statistical models that unpretentiously pool resources on historical figures devoid of modeling the behavior of driving forces that generates these data. Goodhart (2011) sought to assess the likelihood of small businesses to default on loans. Making use of data on business loan assortment, the study established the particular lender, loan, and borrower characteristics as well as modifications in the economic environments that lead to a rise in the probability of default. The results of the study form the basis for the scoring model. Focusing on modeling default possibility, Singhee & Rutenbar (2010) found the risk as the uncertainty revolving around an enterprise’s capacity to service its obligations and debts.
Using the logistic model to forecast the probability of bank loan defaults, Adam et al. (2012) employed a data set with demographic information on borrowers. The authors attempted to establish the risk factors linked to borrowers are attributable to default. The identified risk factors included marital status, gender, occupation, age, and loan duration. Cababrese (2012) employed three accepted data mining algorithms, naïve Bayesian classifiers, artificial neural network decision trees coupled with a logical regression model to formulate a prediction m ...
Case Notes on MW Petroleum Corporation (A)Why Should We Care A.docxwendolynhalbert
Case Notes on MW Petroleum Corporation (A)
Why Should We Care About Real Options?
Ignoring real options in a project often leads to an underestimation of the true project value. Because real options are not explicitly linked to cash flows, they may seem difficult to identify. Here are some typical examples of real options.
· The option to expand an existing investment project.
· Research and development (R&D) is an example of a growth option.
· The option to delay an investment project.
· The option to abandon a project that has already been undertaken.
From the above examples, we find that real options reflect the flexibility inherent in any capital investment process, which is often ignored by the DCF analysis because flexibility is hard to quantify in terms of cash flows. Fortunately, the breakthrough in option pricing theory provides us with the tools to find the value of these real options.
Types of Reserves
MW Petroleum’s estimated reserves can be classified into four major categories:
· proved developed reserves
· proved undeveloped reserves
· probable reserves
· possible reserves
Exhibits 3 through 6 tell us the production and cash flow projections for each of the four types of reserves.
Risk-adjusted Discount Rate (RADR)
For valuation purposes, we need an estimate of MW's WACC to discount cash flows. Unfortunately, the case does not provide many details. This presents a very realistic problem that is often faced when attempting to do analysis in the real world. For example, because MW is a subsidiary of Amoco, its (market) equity value is not available. We do not have a clear idea about the debt and equity mix of MW either. However, we do have the following information:
The average asset (unlevered) beta for Oil companies = 0.64 (footnote b of Exhibit 2).
Given this information, we can use the CAPM to calculate the cost of equity for MW.
· Cost of equity = risk-free rate + beta * market risk premium
For the risk-free rate, we can use the 1990 year-end 30-year US government bond yield given in the MW case in Exhibit 10. We choose the 30-year bond because the time horizon of the cash flows given in the case is 15 years, which is longer than 10 years. Remember, projects in this industry are long-term and, therefore, call for a longer-term Treasury yield to proxy for the risk-free rate.
To determine the market risk premium, we can rely on a report that is maintained by the Stern School of Business at New York University. This report maintains historic annual returns on stock, T-bonds, and T-bills from 1928 – Current. The report also maintains the historic market-risk premium, starting in 1960. To be consistent with our risk-free rate, we want to use the historical market-risk premium for 1990 in the following report:
· http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/histretSP.html
Next we assume that Miller Equilibrium holds. This implies:
· The WACC, which is the risk-adjusted discount rate for discoun ...
In this paper, we construct a Credit Default Swap pricing model for default recovery rates under
distributional uncertainty based on a structured pricing model and distributional uncertainty theory. The model
is algorithmically transformed into a solvable semi-definite programming problem using the Lagrangian dual
method, and the solution of the model is given using the projection interior point method. Finally, an empirical
analysis is conducted, and the results show that the model constructed in this paper is reasonable and efficient
Creating an Explainable Machine Learning AlgorithmBill Fite
How to create an explainable scorecard model using machine learning to optimize its performance with results and insights from applying it to a stock picking problem.
RADRmarket risk premiumLicheng This premium is taken from the.docxmakdul
RADRmarket risk premium
Licheng: This premium is taken from the NYU report .Input Variablesrisk-free rate
Licheng: From Exhibit 10.Calculation Variables asset beta
Licheng: Footnote b of Exhibit 2.Cost of equity
Licheng: Assuming that Miller's equilibrium holds, this is the same as WACC.
Value with Real OptionsTotal Real Option Values from proved undeveloped, probable, and possible reserves ($ Millions)Input VariablesTime to Maturity (in years)567Calculation Variables volatility0.30.40.50.60.7Aggregated MW Cash Flow Projections Year123456789101112131415Cash flowTerminal valueTotal Cash FlowValue from other opportunities (see case page 5)DCF value without optionsTotal value w/o optionsTotal value with optionsTime to Maturity (in years)567volatility0.30.40.50.60.7% difference in value between the firm with and without optionsTime to Maturity (in years)567volatility0.30.40.50.60.7
Proved UndevelopedProved Undeveloped Reserves: Production and Cash Flow Projections($ millions)year123456789101112131415Cash from operationsCapital expendituresExtraordinary Cap Exp. in first 2 yearsRoutine Cap Exp.Net Cash flow Terminal valueTotal net cash flowCalculation of Real Option ValueStike Price (X), discounted at risk-free rate Input VariablesUnderlying Asset Value (S), at RADRRisk Free RateCalculation Variables T (in years)Sigmad1d2N(d1)N(d2)Call Option valueSensitivity Analysis of Option Value on volatility and Time to MaturityTime to Maturity (in years)0.0567volatility30%40%50%60%70%
PossiblePossible Reserves: Production and Cash Flow Projections($ millions)year123456789101112131415Cash from operationsCapital expendituresExtraordinary Cap Exp. in first 5 yearsRoutine Cap Exp.Net Cash flow Terminal valueTotal net cash flowCalculation of Real Option ValueStike Price (X), discounted at risk-free rate Input VariablesUnderlying Asset Value (S), at RADRRisk Free RateCalculation Variables T (in years)Sigmad1d2N(d1)N(d2)Call Option valueSensitivity Analysis of Option Value on volatility and Time to MaturityTime to Maturity (in years)0.0567volatility30%40%50%60%70%
ProbableProbable Reserves: Production and Cash Flow Projections($ millions)year123456789101112131415Cash from operationsCapital expendituresExtraordinary Cap Exp. in first 4 yearsRoutine Cap Exp.Net Cash flow Terminal valueTotal net cash flowCalculation of Real Option ValueStike Price (X), discounted at risk-free rate Input VariablesUnderlying Asset Value (S), at RADRRisk Free RateCalculation Variables T (in years)Sigmad1d2N(d1)N(d2)Call Option valueSensitivity Analysis of Option Value on volatility and Time to MaturityTime to Maturity (in years)0.0567volatility30%40%50%60%70%
Supply Chain Planning and Control module
Write a short essay that discusses the following statement, using examples and references to support your answer:
“Supply chain management is an important function of today competitive business operations”
Assignment 1 (1200 words, +/- 10%) Harvard referencing.
Case Notes on MW Petr ...
International journal of engineering and mathematical modelling vol1 no1_2015_2IJEMM
Default risk has always been a matter of importance for financial managers and scholars. In this paper we apply an intensity-based approach for default estimation with a software simulation of the Cox-Ingersoll-Ross model. We analyze the possibilities and effects of a non-linear dependence between economic and financial state variables and the default density, as specified by the theoretical model. Then we perform a test for verifying how simulation techniques can improve the analysis of such complex relations when closed-form solutions are either not available or hard to come by.
In this study we survey practices and supervisory expectations for stress testing (ST), in a credit risk framework for banking book exposures. We introduce and motivate ST; and discuss the function, supervisory requirements and expectations, credit risk parameters, interpretation results
with respect to ST. This includes a typology of ST (uniform testing, risk factor sensitivities, scenario analysis; and historical, statistical and hypothetical scenarios) and procedures for con-ducting ST. We conclude with two simple and practical stress testing examples, one a ratings migration based approach, and the other a top-down ARIMA modeling approach.
Empirical Analysis of Bank Capital and New Regulatory Requirements for Risks ...Michael Jacobs, Jr.
We examine the impact of new supervisory standards for bank trading portfolios, additional capital requirements for liquidity risk and credit risk (the Incremental Risk Charge), introduced under Basel 2.5. We estimate risk measures under alternative assumptions on portfolio dynamics (constant level of risk vs. constant positions), rating systems (through-the-cycle vs. point-in-time), for different sectors (asset classes and industry groups), alternative credit risk frameworks (al-ternative dependency structures or factor models) and an extension to a Bayesian framework. We find a potentially material increase in capital requirements, above and beyond that concluded in the far-ranging impact studies conducted by the international supervisors utilizing the participation of a large sample of banks. Results indicate that capital charges are in general higher for either point-in-time ratings or constant portfolio dynamics, with this effect accentuated for financial or sovereign as compared to industrial sectors; and that regulatory is larger than economic capital for the latter, but not for the former sectors. A comparison of the single to a multi-factor credit models shows that capital estimates larger in the latter, and for the financial / sovereign by orders of magnitude vs. industrial or the Basel II model, and that there is less sensitivity of results across sectors and rating systems as compared with the single factor model. Furthermore, in a Bayesian experiment we find that the new requirements may introduce added uncertainty into risk measures as compared to existing approaches.
Modern credit risk modeling (e.g., Merton, 1974) increasingly relies on advanced mathematical, statistical and numerical echniques to measure and manage risk in redit portfolios
This gives rise to model risk (OCC 2011-16) and the possibility of nderstating nherent dangers stemming from very rare yet plausible occurrencs perhaps not in our eference data-sets International supervisors have recognized the importance of stress testing credit risk in the Basel framework (BCBS, 2009)
It can and has been argued that the art and science of stress testing has lagged in the domain of credit, vs. other types of risk (e.g., market), and our objective is to help fill this vacuum
We aim to present classifications & established techniques that will help practitioners formulate robust credit risk stress tests
It is not difficult to find situations of marked change in variables and with unpredictable event risk implies estimation problems. E.g.,
Credit spreads in 2008 rise to levels that could never have been forecast based upon previous history. The subprime crisis of 2007/8: credit spreads & volatility rise to unseen levels & shift in debtor behavior (delinquency patterns)
E.g., estimating the volatility from data in a calm (turbulent) period implies under (over) estimation of future realized volatility
how to sell pi coins in all Africa Countries.DOT TECH
Yes. You can sell your pi network for other cryptocurrencies like Bitcoin, usdt , Ethereum and other currencies And this is done easily with the help from a pi merchant.
What is a pi merchant ?
Since pi is not launched yet in any exchange. The only way you can sell right now is through merchants.
A verified Pi merchant is someone who buys pi network coins from miners and resell them to investors looking forward to hold massive quantities of pi coins before mainnet launch in 2026.
I will leave the telegram contact of my personal pi merchant to trade with.
@Pi_vendor_247
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
Currently pi network is not tradable on binance or any other exchange because we are still in the enclosed mainnet.
Right now the only way to sell pi coins is by trading with a verified merchant.
What is a pi merchant?
A pi merchant is someone verified by pi network team and allowed to barter pi coins for goods and services.
Since pi network is not doing any pre-sale The only way exchanges like binance/huobi or crypto whales can get pi is by buying from miners. And a merchant stands in between the exchanges and the miners.
I will leave the telegram contact of my personal pi merchant. I and my friends has traded more than 6000pi coins successfully
Tele-gram
@Pi_vendor_247
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
Turin Startup Ecosystem 2024 - Ricerca sulle Startup e il Sistema dell'Innov...Quotidiano Piemontese
Turin Startup Ecosystem 2024
Una ricerca de il Club degli Investitori, in collaborazione con ToTeM Torino Tech Map e con il supporto della ESCP Business School e di Growth Capital
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
how to sell pi coins at high rate quickly.DOT TECH
Where can I sell my pi coins at a high rate.
Pi is not launched yet on any exchange. But one can easily sell his or her pi coins to investors who want to hold pi till mainnet launch.
This means crypto whales want to hold pi. And you can get a good rate for selling pi to them. I will leave the telegram contact of my personal pi vendor below.
A vendor is someone who buys from a miner and resell it to a holder or crypto whale.
Here is the telegram contact of my vendor:
@Pi_vendor_247
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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
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
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.
Empowering the Unbanked: The Vital Role of NBFCs in Promoting Financial Inclu...Vighnesh Shashtri
In India, financial inclusion remains a critical challenge, with a significant portion of the population still unbanked. Non-Banking Financial Companies (NBFCs) have emerged as key players in bridging this gap by providing financial services to those often overlooked by traditional banking institutions. This article delves into how NBFCs are fostering financial inclusion and empowering the unbanked.
how can I sell my pi coins for cash in a pi APPDOT TECH
You can't sell your pi coins in the pi network app. because it is not listed yet on any exchange.
The only way you can sell is by trading your pi coins with an investor (a person looking forward to hold massive amounts of pi coins before mainnet launch) .
You don't need to meet the investor directly all the trades are done with a pi vendor/merchant (a person that buys the pi coins from miners and resell it to investors)
I Will leave The telegram contact of my personal pi vendor, if you are finding a legitimate one.
@Pi_vendor_247
#pi network
#pi coins
#money
1. May 30, 2010 An Exposure at Default Model for Contingent Credit Lines Pinaki Bag Union National Bank, United Arab Emirates Michael Jacobs, Jr. Credit Risk Analysis Division U.S. Office of the Comptroller of the Currency The views expressed herein are those of the authors and do not necessarily represent the views of either Union National Bank, UAE or of the U.S. Office of the Comptroller of the Currency.
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3. Outline 1 Introduction - Motivation 2 Review of the Literature 3 The Model 4 Numerical Experiment 5 Conclusions
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7. What We Did? Portfolio Segments Segment Level Usage Unused Obligor Limits Each CCL as Portfolio of Put Options Basic CreditRisk+ Algorithm Fast Fourier Transform Moody's DRS Database (Current Sample Portfolio) Moody's MURD Database & Compustat (Reference Data for CCF Estimates)
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13. Model Overview Segment Level Usage Obligor Level Unused Limits Each obligor’s CCL is modeled as portfolio of large number of put options to determine usage Similar put size obligors are clubbed under each sub-segment Each sub-segment having similar expected usage are combined to determine segment level usage FFT used to convolute each segment to the overall portfolio usage distribution Individual obligors Sub-segment Segment Portfolio