Risk Management Study Guide

In-Person: NY Wall Street
Campus
Duration : 5 Days (Full-time)
Teaching Mode : Live Instructor Classes

View Program

Virtual Live
Duration : 5 Days
Teaching Mode : Live Virtual Sessions

View Program

Self-Paced Online
Duration : 40 Hours (Learn at your pace)
Teaching Mode : Recorded Sessions +
Q&A with Faculty

View Program

Introduction to Risk

Scenario-Based Introduction:

Imagine a financial institution navigating a sudden market downturn. The firm faces multiple risks, including market, credit, and operational risks. The management's ability to quickly identify, assess, and mitigate these risks determines whether the firm will survive or suffer significant losses.

Why Manage Risk?

Protect assets, ensure business stability, and maintain compliance with regulations. In the example above, managing these risks effectively prevented catastrophic losses and ensured continued operations.

Key Risks to Consider:

Market Risk : Fluctuations in stock prices and interest rates.
Credit Risk : Potential default by borrowers.
Operational Risk : Failures in internal processes or systems.

Interconnected

Market Risk

Credit Risk

Operational Risk

Types of Risk

Interconnected Risks:

Market and Credit Risk Interaction : In a financial crisis, a drop in market value can lead to credit downgrades, increasing the likelihood of defaults and exacerbating credit risk.

Practical Example:

During the 2008 financial crisis, falling real estate prices (market risk) led to widespread mortgage defaults (credit risk), causing significant losses for financial institutions.

Behavioral Insights:

Cognitive Biases : Overconfidence, loss aversion, and herd behavior can exacerbate financial risks. For example, overconfidence can lead to underestimating risks, while herd behavior can drive market bubbles and crashes.

This means there is a 5% chance that the portfolio could lose 26% or more over the next 10 days.

VaR = 1.6 × 0.05 × 10 = 0.26

Example : For a portfolio with a standard deviation of 5%, a time horizon of 10 days, and a 95% confidence level (Z-Score = 1.65), the VaR would be:

Quantitative Tools:

Market Risk:

Value at Risk (VaR):

VaR = Z-Score × σ ×

VaR Meter

Low Risk

High Risk

EL = 0.02 × 1,000,000 × 0.60 = 12,000

Example : If a loan has a 2% PD, an EAD of $1 million, and an LGD of 60%, the expected loss would be:

PD: Probability of Default.
EAD: Exposure at Default.
LGD: Loss Given Default.

Components:

Expected Loss (EL):

EL = PD × EAD × LGD

Credit Risk :

Probability of Default

EAD

Exposure at Default

LGD

Loss Given Default

Risk by Asset Class

Scenario-Based Learning:

Equity Risk Management: A portfolio manager anticipates a market downturn and uses options to hedge against potential losses. By buying put options, the manager can limit downside risk while retaining upside potential.

Example : For an asset with a beta of 1.2, a risk-free rate of 2%, and a market return of 8%, the expected return would be:

Capital Asset Pricing Model (CAPM):

Expected Return = Rf +β(Rm− Rf)

Practical Examples:

Equity Risk:

Expected Return = 0.02 + 1.2 × (0.08 − 0.02) = 0.092 = 9.2%

Mean, %

Security
Market Line

S&P 500

MSCI
World
Index

All
Investable
Assets

Market?

0.5

1.5

Fixed Income Risk:

Duration:

Duration =

(t × C

/ (1 + y)

)

Example : For a bond with cash flows of $100 annually, a yield of 5%, and a current price of $950, the duration measures the bond's sensitivity to interest rate changes.

Rising Yields

Log Price

Log(Bond Price)

Increaseing price
sensitivity to yeield changes

Decreasing price
sensitivity to yeield changes

Duration = Slope

y*-2Δy

y*-1Δy

y*+1Δy

y*+2Δy

Yield

Falling Yields

Advanced Risk Metrics and Models

Application and Limitations:

Value at Risk (VaR) : Used daily by trading desks to estimate potential losses. However, during extreme market conditions, VaR may underestimate risk, as seen in the 2008 crisis when actual losses exceeded VaR estimates.

Quantitative Tools:

Expected Shortfall (ES):

ES = E [ L | L > VaR ]

Example : If the VaR is $1 million, and the average loss beyond this threshold is $1.5 million, the ES would be $1.5 million, providing a more comprehensive risk assessment.

Advanced Risk Metrics

Expected Shortfall

VaR

Value at Risk

Advanced Risk Metrics and Models

Backtesting VaR:

Number of Exceedances

Expected Exceedances

Backtest Ratio =

Explanation : This ratio helps validate the accuracy of VaR models by comparing observed losses with expected outcomes.

Back testing VAR

Predicted

losses

Actual

losses

Predicted losses

Actual losses

Frequent
exceedances

VAR

Predicted losses

Actual losses

Derivatives, Futures, Swaps, and Options

Risk Management Tools

Example : For a swap with a notional value of $1 million, a fixed rate of 3%, and a floating rate of SOFR + 1%, the NPV calculation involves determining the sum of the differences between the present values of the fixed and floating payments for each period in the swap.

Fixed Rate / Floating Rate : Agreed fixed or variable rate.
Notional : Principal amount for interest calculations.
y(t) : Discount rate for each period t.

NPV =

t=1

(

( Fixed Rate × Notional )

( 1 + y(t))

−

(

( Floating Rate × Notional )

( 1 + y(t))

Net Present Value (NPV):

Real-World Applications:

Interest Rate Swaps:

SOFR +1.1%

SOFR +1%

Bank A

Dealer Swap

Bank B

Fixed 4.6%

Fixed 4.7%

Fixed 5%

Floating SOFR + 1.25%

The Black-Scholes model is used to price European options. While critical, the full formula is complex, so users are directed to more detailed resources for a complete understanding.

Options :

Note on Black-Scholes Formula :

Input
Variables

Option
Types

Pricing
Models

Option
Pricing

Regulatory Framework

Impact on Day-to-Day Operations:

Basel III Implementation:

Basel III requires banks to maintain higher capital reserves—funds set aside to absorb losses—impacting how much they can lend. This regulation also imposes stricter operational controls, such as closer monitoring of capital levels and more frequent reporting to regulators.

Real-World Compliance Challenges:

Dodd-Frank Act :

The Dodd-Frank Act introduces significant challenges for financial institutions, particularly in derivatives trading, where banks must manage contracts that derive value from underlying assets. The Volcker Rule, a key component of Dodd-Frank, restricts proprietary trading—trading for the bank's own profit. This requires banks to overhaul their trading strategies and implement systems to ensure compliance, leading to increased operational complexity and costs.

Key Regulations:

Focuses on ensuring that banks have adequate capital to absorb losses (capital adequacy), conducting stress tests to simulate extreme economic conditions, and maintaining market liquidity—the ability to quickly convert assets into cash.

Basel III :

Dodd-Frank Act :

Aims to reduce systemic risk—the risk that the failure of one institution could lead to a broader financial collapse—by imposing tighter regulations on financial institutions.

Key Changes

Capital Reserve Requirements
Before basel lll

Capital Reserve Requirements
After basel lll

Integration of Technology in

Risk Management

Use Cases in Action:

Banks use AI models to assess borrower creditworthiness, analyzing vast amounts of data to predict defaults more accurately than traditional models.

AI in Credit Risk :

Practical Example:

Investment firms analyze large datasets to identify emerging risks, such as sector-specific vulnerabilities during economic downturns.

Big Data Analytics :

Technology Overview:

Automates risk identification and assessment processes.

Risk Management

Information Systems

(RMIS) :

AI-Driven Tools :

Enhances predictive accuracy in risk modeling.

Practical Applications
& Case Studies

Recent Market Events:

COVID-19 Pandemic : Financial institutions used stress testing and scenario analysis to navigate unprecedented market volatility. These tools helped them adjust risk management strategies in real-time, demonstrating the importance of flexible frameworks.

Dynamic Case Studies:

Cybersecurity Risk : A large financial institution faced a major cyberattack, prompting an overhaul of its operational risk management practices. This case illustrates the growing importance of integrating cybersecurity into risk management.

Lessons Learned:

Liquidity-Adjusted VaR : During the 2008 financial crisis, traditional VaR models underestimated risk due to the lack of liquidity adjustment. Incorporating liquidity risk into VaR models provides a more accurate picture of potential losses during market stress.

Risk Management Study Guide

In-Person: NY Wall Street

Campus

Duration : 5 Days (Full-time)

Teaching Mode : Live Instructor Classes

View Program

Virtual Live

Duration : 5 Days

Teaching Mode : Live Virtual Sessions

View Program

Self-Paced Online

Duration : 40 Hours (Learn at your pace)

Teaching Mode : Recorded Sessions +

Q&A with Faculty

View Program

Introduction to Risk

Scenario-Based Introduction:

Why Manage Risk?

Key Risks to Consider:

Market Risk : Fluctuations in stock prices and interest rates.
Credit Risk : Potential default by borrowers.
Operational Risk : Failures in internal processes or systems.

Interconnected

Market Risk

Credit Risk

Operational Risk

Types of Risk

Interconnected Risks:

Market and Credit Risk Interaction : In a financial crisis, a drop in market value can lead to credit downgrades, increasing the likelihood of defaults and exacerbating credit risk.

Practical Example:

During the 2008 financial crisis, falling real estate prices (market risk) led to widespread mortgage defaults (credit risk), causing significant losses for financial institutions.

Behavioral Insights:

This means there is a 5% chance that the portfolio could lose 26% or more over the next 10 days.

VaR = 1.6 × 0.05 × 10 = 0.26

Example : For a portfolio with a standard deviation of 5%, a time horizon of 10 days, and a 95% confidence level (Z-Score = 1.65), the VaR would be:

Quantitative Tools:

Market Risk:

Value at Risk (VaR):

VaR = Z-Score × σ ×

VaR Meter

Low Risk

High Risk

EL = 0.02 × 1,000,000 × 0.60 = 12,000

Example : If a loan has a 2% PD, an EAD of $1 million, and an LGD of 60%, the expected loss would be:

PD: Probability of Default.
EAD: Exposure at Default.
LGD: Loss Given Default.

Components:

Expected Loss (EL):

EL = PD × EAD × LGD

Credit Risk :

Probability of Default

EAD

Exposure at Default

LGD

Loss Given Default

Risk by Asset Class

Scenario-Based Learning:

Example : For an asset with a beta of 1.2, a risk-free rate of 2%, and a market return of 8%, the expected return would be:

Capital Asset Pricing Model (CAPM):

Expected Return = Rf +β(Rm− Rf)

Practical Examples:

Equity Risk:

Expected Return = 0.02 + 1.2 × (0.08 − 0.02) = 0.092 = 9.2%

Mean, %

Security
Market Line

S&P 500

MSCI
World
Index

All
Investable
Assets

Market?

0.5

1.5

Fixed Income Risk:

Duration:

Duration =

(t × C

/ (1 + y)

)

Example : For a bond with cash flows of $100 annually, a yield of 5%, and a current price of $950, the duration measures the bond's sensitivity to interest rate changes.

Rising Yields

Log Price

Increaseing price
sensitivity to yeield changes

Decreasing price
sensitivity to yeield changes

Log(Bond Price)

Duration = Slope

y*-2Δy

y*-1Δy

y*+1Δy

y*+2Δy

Yield

Falling Yields

Advanced Risk Metrics and Models

Application and Limitations:

Quantitative Tools:

Expected Shortfall (ES):

ES = E [ L | L > VaR ]

Example : If the VaR is $1 million, and the average loss beyond this threshold is $1.5 million, the ES would be $1.5 million, providing a more comprehensive risk assessment.

Advanced Risk Metrics

Expected Shortfall

VaR

Value at Risk

Backtesting VaR:

Number of Exceedances

Expected Exceedances

Backtest Ratio =

Explanation : This ratio helps validate the accuracy of VaR models by comparing observed losses with expected outcomes.

Back testing VAR

Predicted

losses

Actual

losses

Predicted losses

Actual losses

Frequent
exceedances

VAR

Predicted losses

Actual losses

Risk Management Tools

Fixed Rate / Floating Rate : Agreed fixed or variable rate.
Notional : Principal amount for interest calculations.
y(t) : Discount rate for each period t.

NPV =

t=1

(

( Fixed Rate × Notional )

( 1 + y(t))

−

(

( Floating Rate × Notional )

( 1 + y(t))

Net Present Value (NPV):

Real-World Applications:

Interest Rate Swaps:

SOFR +1.1%

SOFR +1%

Bank A

Dealer Swap

Bank B

Fixed 4.6%

Fixed 4.7%

Fixed 5%

Floating SOFR + 1.25%

The Black-Scholes model is used to price European options. While critical, the full formula is complex, so users are directed to more detailed resources for a complete understanding.

Options :

Note on Black-Scholes Formula :

Input
Variables

Option
Types

Pricing
Models

Option
Pricing

Regulatory Framework

Impact on Day-to-Day Operations:

Basel III Implementation:

Real-World Compliance Challenges:

Dodd-Frank Act :

Key Regulations:

Basel III :

Dodd-Frank Act :

Aims to reduce systemic risk—the risk that the failure of one institution could lead to a broader financial collapse—by imposing tighter regulations on financial institutions.

Key Changes

Capital Reserve Requirements
Before basel lll

Capital Reserve Requirements
After basel lll

Integration of Technology in
Risk Management

Use Cases in Action:

Banks use AI models to assess borrower creditworthiness, analyzing vast amounts of data to predict defaults more accurately than traditional models.

AI in Credit Risk :

Practical Example:

Investment firms analyze large datasets to identify emerging risks, such as sector-specific vulnerabilities during economic downturns.

Big Data Analytics :

Technology Overview:

Automates risk identification and assessment processes.

Risk Management

Information Systems

(RMIS) :

AI-Driven Tools :

Enhances predictive accuracy in risk modeling.

Practical Applications
& Case Studies

Recent Market Events:

Dynamic Case Studies:

Lessons Learned:

Risk Management Study Guide

In-Person: NY Wall Street
Campus
Duration : 5 Days (Full-time)
Teaching Mode : Live Instructor Classes

View Program

Virtual Live
Duration : 5 Days
Teaching Mode : Live Virtual Sessions

View Program

Self-Paced Online
Duration : 40 Hours (Learn at your pace)
Teaching Mode : Recorded Sessions +
Q&A with Faculty

View Program

Introduction to Risk

Scenario-Based Introduction:

Why Manage Risk?

Key Risks to Consider:

Market Risk : Fluctuations in stock prices and interest rates.
Credit Risk : Potential default by borrowers.
Operational Risk : Failures in internal processes or systems.

Interconnected

Market Risk

Credit Risk

Operational Risk

Types of Risk

Interconnected Risks:

Market and Credit Risk Interaction : In a financial crisis, a drop in market value can lead to credit downgrades, increasing the likelihood of defaults and exacerbating credit risk.

Practical Example:

During the 2008 financial crisis, falling real estate prices (market risk) led to widespread mortgage defaults (credit risk), causing significant losses for financial institutions.

Behavioral Insights:

This means there is a 5% chance that the portfolio could lose 26% or more over the next 10 days.

VaR = 1.6 × 0.05 × 10 = 0.26

Example : For a portfolio with a standard deviation of 5%, a time horizon of 10 days, and a 95% confidence level (Z-Score = 1.65), the VaR would be:

Quantitative Tools:

Market Risk:

Value at Risk (VaR):

VaR = Z-Score × σ ×

Components:

PD: Probability of Default.
EAD: Exposure at Default.
LGD: Loss Given Default.

Example : If a loan has a 2% PD, an EAD of $1 million, and an LGD of 60%, the expected loss would be:

Credit Risk :

Expected Loss (EL):

EL = PD × EAD × LGD

EL = 0.02 × 1,000,000 × 0.60 = 12,000

Probability of Default

EAD

Exposure at Default

LGD

Loss Given Default

VaR Meter

Low Risk

High Risk

Risk by Asset Class

Scenario-Based Learning:

Example : For an asset with a beta of 1.2, a risk-free rate of 2%, and a market return of 8%, the expected return would be:

Capital Asset Pricing Model (CAPM):

Expected Return = Rf +β(Rm− Rf)

Practical Examples:

Equity Risk:

Expected Return = 0.02 + 1.2 × (0.08 − 0.02) = 0.092 = 9.2%

Mean, %

Security
Market Line

S&P 500

MSCI
World
Index

All
Investable
Assets

Market?

0.5

1.5

Fixed Income Risk:

Duration:

Duration =

(t × C

/ (1 + y)

)

Example : For a bond with cash flows of $100 annually, a yield of 5%, and a current price of $950, the duration measures the bond's sensitivity to interest rate changes.

Log(Bond Price)

Duration = Slope

Log Price

Increaseing price
sensitivity to yeield changes

Decreasing price
sensitivity to yeield changes

y*-2Δy

y*-1Δy

y*+1Δy

y*+2Δy

Yield

Falling Yields

Rising Yields

Advanced Risk Metrics and Models

Application and Limitations:

Quantitative Tools:

Expected Shortfall (ES):

ES = E [ L | L > VaR ]

Example : If the VaR is $1 million, and the average loss beyond this threshold is $1.5 million, the ES would be $1.5 million, providing a more comprehensive risk assessment.

Advanced Risk Metrics

Expected Shortfall

VaR

Value at Risk

Backtesting VaR:

Number of Exceedances

Expected Exceedances

Backtest Ratio =

Explanation : This ratio helps validate the accuracy of VaR models by comparing observed losses with expected outcomes.

Predicted losses

Actual losses

Back testing VAR

Predicted

losses

Actual

losses

Predicted losses

Actual losses

Frequent
exceedances

VAR

Risk Management Tools

SOFR +1.1%

SOFR +1%

Bank A

Dealer Swap

Bank B

Fixed 4.6%

Fixed 4.7%

Fixed 5%

Floating SOFR + 1.25%

Fixed Rate / Floating Rate : Agreed fixed or variable rate.
Notional : Principal amount for interest calculations.
y(t) : Discount rate for each period t.

NPV =

t=1

(

( Fixed Rate × Notional )

( 1 + y(t))

−

(

( Floating Rate × Notional )

( 1 + y(t))

Net Present Value (NPV):

Real-World Applications:

Interest Rate Swaps:

Input
Variables

Option
Types

Pricing
Models

Option
Pricing

The Black-Scholes model is used to price European options. While critical, the full formula is complex, so users are directed to more detailed resources for a complete understanding.

Options :

Note on Black-Scholes Formula :

Regulatory Framework

Impact on Day-to-Day Operations:

Basel III Implementation:

Real-World Compliance Challenges:

Dodd-Frank Act :

Key Regulations:

Basel III :

Dodd-Frank Act :

Aims to reduce systemic risk—the risk that the failure of one institution could lead to a broader financial collapse—by imposing tighter regulations on financial institutions.

Key Changes