Free Preparation Discussions
MENU
PRMIA 8010 Exam Questions
- Topic 1: Classic Credit Products: This section of the exam covers traditional lending instruments like loans and bonds used by banks and financial institutions.
- Topic 2: Classic Credit Life Cycle: This section covers the stages a credit product goes through, from origination to maturity or default.
- Topic 3: Classic Credit Risk Methodology: This section covers conventional approaches to assessing and quantifying the risk of borrower default.
- Topic 4: Credit Derivatives and Securitization: In this section, the topics covered include financial instruments that transfer credit risk and pool debt-based assets into tradable securities.
- Topic 5: Modern Credit Risk Modeling: This section covers advanced statistical and mathematical techniques for measuring and managing credit risk.
- Topic 6: Credit Portfolio Management: This section covers strategies for optimizing the overall risk and return of a collection of credit exposures.
- Topic 7: Basics of Counterparty Risk: This section covers fundamental concepts related to the risk of a counterparty failing to fulfill their contractual obligations.
- Topic 8: Risk Mitigation: This section covers techniques and tools used to reduce or transfer various types of financial risks.
- Topic 9: Credit Valuation Adjustment (CVA): This section covers an adjustment to the fair value of derivatives to account for counterparty credit risk.
- Topic 10: CVA-related Aspects: This section covers additional considerations and implications associated with Credit Valuation Adjustment.
- Topic 11: Managing Counterparty Risk and CVA: This section covers strategies and practices for controlling exposure to counterparty default and optimizing CVA.
Free PRMIA 8010 Exam Actual Questions
Note: Premium Questions for 8010 were last updated On Feb. 19, 2026 (see below)
Which of the following statements are true:
1. Credit risk and counterparty risk are synonymous
2. Counterparty risk is the contingent risk from a counterparty's default in derivative transactions
3. Counterparty risk is the risk of a loan default or the risk from moneys lent directly
4. The exposure at default is difficult to estimate for credit risk as it depends upon market movements
Credit risk is the risk from a borrower defaulting on moneys lent. Counterparty risk, on the other hand, is the risk that a counterparty to a derivative transaction will be unable to pay at the time the transaction is in-the-money.
Credit risk therefore relates more to the banking book, counterparty risk relates more to the trading book. Credit risk and counterparty risk differ in that for counterparty risk, the amount at risk fluctuates for counterparty risk depending upon the value of the underlying derivative. Counterparty risk generally starts at zero, for most swaps and other derivatives are near zero value at inception. Over time, as the prices of the underlying instruments move, one party ends up owing money to the other. A deterioration in the financial situation of the party owing moneys may lead to a loss to the other party, resulting in counterparty risk. Counterparty risk can also arise from stock lending operations and repo trades.
Credit risk on the other hand is the traditional risk of default by a borrower, or a bank's customer who has taken a loan or has an overdraft or other credit facility.
Statement I is therefore incorrect as credit risk and counterparty risks are different.
Statement II is correct as counterparty risk is 'contingent' in the sense it arises only if the transaction with the counterparty ends up being in-the-money, and the counterparty defaults.
Statement III is incorrect. The statement describes credit risk.
Statement IV is incorrect, as the exposure is known for moneys lent. Derivative exposures for the future are difficult to estimate, they can even turn from moneys owed to moneys due as the value of the underlying changes.
Under the actuarial (or CreditRisk+) based modeling of defaults, what is the probability of 4 defaults in a retail portfolio where the number of expected defaults is 2?
The actuarial or CreditRisk+ model considers default as an 'end of game' event modeled by a Poisson distribution. The annual number of defaults is a stochastic variable with a mean of and standard deviation equal to .
The probability of n defaults is given by (^n e^-) /n!, and therefore in this case is equal to (=2^4 * exp(-2))/FACT(4)) = 0.0902.
Note that CreditRisk+ is the same methodology as the actuarial approach, and requires using the Poisson distribution.
Which of the following steps are required for computing the total loss distribution for a bank for operational risk once individual UoM level loss distributions have been computed from the underlhying frequency and severity curves:
1. Simulate number of losses based on the frequency distribution
2. Simulate the dollar value of the losses from the severity distribution
3. Simulate random number from the copula used to model dependence between the UoMs
4. Compute dependent losses from aggregate distribution curves
A recap would be in order here: calculating operational risk capital is a multi-step process.
First, we fit curves to estimate the parameters to our chosen distribution types for frequency (eg, Poisson), and severity (eg, lognormal). Note that these curves are fitted at the UoM level - which is the lowest level of granularity at which modeling is carried out. Since there are many UoMs, there are are many frequency and severity distributions. However what we are interested in is the loss distribution for the entire bank from which the 99.9th percentile loss can be calculated. From the multiple frequency and severity distributions we have calculated, this becomes a two step process:
- Step 1: Calculate the aggregate loss distribution for each UoM. Each loss distribution is based upon and underlying frequency and severity distribution.
- Step 2: Combine the multiple loss distributions after considering the dependence between the different UoMs. The 'dependence' recognizes that the various UoMs are not completely independent, ie the loss distributions are not additive, and that there is a sort of diversification benefit in the sense that not all types of losses can occur at once and the joint probabilities of the different losses make the sum less than the sum of the parts.
Step 1 requires simulating a number, say n, of the number of losses that occur in a given year from a frequency distribution. Then n losses are picked from the severity distribution, and the total loss for the year is a summation of these losses. This becomes one data point. This process of simulating the number of losses and then identifying that number of losses is carried out a large number of times to get the aggregate loss distribution for a UoM.
Step 2 requires taking the different loss distributions from Step 1 and combining them considering the dependence between the events. The correlations between the losses are described by a 'copula', and combined together mathematically to get a single loss distribution for the entire bank. This allows the 99.9th percentile loss to be calculated.
Concentration risk in a credit portfolio arises due to:
Concentration risk in a credit portfolio arises due to a high degree of correlation between the default probabilities of the issuers of securities in the portfolio. For example, the fortunes of the issuers in the same industry may be highly correlated, and an investor exposed to multiple such borrowers may face 'concentration risk'.
A low degree of correlation, or independence of individual defaults in the portfolio actually reduces or even eliminates concentration risk.
The fact that issuers are from the same country may not necessarily give rise to concentration risk - for example, a bank with all US based borrowers in different industries or with different retail exposure types may not face practically any concentration risk. What really matters is the default correlations between the borrowers, for example a lender exposed to cement producers across the globe may face a high degree of concentration risk.
An assumption regarding the absence of ratings momentum is referred to as:
Choice 'c' is the correct answer. The Markov property is the assumption that there is no ratings momentum, and that transition probabilities are dependent only upon where the rating currently is and where it is going to. Where it has come from, or what the past changes in ratings have been, have no effect on the transition probabilities. ('Herstatt risk' refers to settlement risk, and is irrelevant.)
- Select Question Types you want
- Set your Desired Pass Percentage
- Allocate Time (Hours : Minutes)
- Create Multiple Practice tests with Limited Questions
- Customer Support
Gracia
3 days agoLeonardo
10 days agoDong
17 days agoBenton
25 days agoAndrew
1 month agoJeanice
1 month agoZack
2 months agoDerick
2 months agoRusty
2 months agoLuther
2 months agoLavera
3 months agoGerry
3 months agoTori
3 months agoGilbert
3 months agoLeanna
4 months agoCatina
4 months agoJules
4 months agoBeckie
4 months agoPansy
5 months agoWhitney
5 months agoAilene
5 months agoBlythe
5 months agoRachael
5 months agoLucia
5 months agoCristen
6 months agoMarjory
6 months agoWinifred
6 months agoRobt
8 months agoAdolph
8 months agoOlen
8 months agoDalene
9 months agoAndree
9 months agoDahlia
10 months agoErnest
11 months agoTonette
11 months agoJosue
12 months agoCamellia
1 year agoKirk
1 year agoAileen
1 year agoPaola
1 year agoTatum
1 year agoTresa
1 year agoHuey
1 year agoChau
1 year agoPatti
1 year agoDorinda
1 year agoMa
1 year agoHyman
1 year agoVeronika
1 year agoBrandon
1 year agoRolande
1 year agoLonna
1 year agoAlline
1 year agoAntonette
1 year agoRose
1 year agoEdison
1 year agoJolene
1 year agoTamala
1 year agoRodrigo
1 year agoKristal
1 year agoLorean
1 year agoReed
1 year agoMicaela
2 years agoOlga
2 years ago