Risk Management Cycle and Control Overview

A method for dividing a company's overall risk budget among its different departments or business units, ensuring that each unit stays within its allocated risk threshold.

A key principle in risk management that states the total risk of a company is less than or equal to the sum of the risks of its individual units, allowing decentralized risk management.

A framework for managing risks effectively, consisting of four main steps: Identify, Assess, Control, and Review.

The process of evaluating the potential impact of a risk on a company's operations or financial performance.

The process of taking actions to reduce or eliminate the likelihood or impact of a risk, including implementing controls and procedures.

A set of limits or thresholds that define the acceptable level of risk for each department or business unit. This system helps ensure that individual risks stay within the overall risk budget.

The process of regularly reviewing and updating the risk management process to ensure its effectiveness and adapt to changing conditions.

The process of identifying potential risks that a company may face, often based on past experiences, industry trends, and external factors.

A method of modeling the value of a fixed-rate coupon bond based on the prices of zero-coupon bonds with varying maturities. It leverages the fact that the present value of a bond can be expressed as a sum of discounted cash flows.

A technique for calculating the present value of a stream of predictable cash flows by discounting each payment based on the corresponding spot rate at the payment date. This assumes discrete compounding of interest.

A statistical technique that explores a linear relationship between a dependent variable (Y) and an independent variable (X). It attempts to find a straight line that best fits the data points.

Expanding linear regression to consider multiple independent variables and their impact on a dependent variable. This allows for a more comprehensive understanding of relationships in complex scenarios.

A process of transforming data to achieve linearity in relationships, allowing for a linear regression analysis even with non-linear patterns. This involves applying mathematical functions to variables.

A graph that compares the quantiles (values that divide a probability distribution into equal parts) of a sample distribution to the quantiles of a theoretical distribution. It helps to visually assess if the data follows a particular distribution.

A QQ plot is used to visually compare the quantiles of an empirical distribution to the quantiles of a reference distribution. This helps in assessing if the data follows the assumed reference distribution.

A QQ plot helps assess if the data follows a specific distribution by comparing the quantiles of the observed data to the quantiles of the chosen theoretical distribution.

An ordered sample of independent and identically distributed random variables. Each variable in the sample has the same distribution.

The probability distribution that describes a random variable.

Machine Learning, Data Mining, Big Data, and Knowledge Discovery in Databases (KDD) are some of the buzzwords used in Data Science.

Data analytics involves statistical methods to understand and interpret data. This is often applied in risk management by analyzing risk factors and identifying potential risks.

Data science is a rapidly evolving field that leverages various techniques like Machine Learning and data mining to extract insights from large datasets. This has wide applications including fraud detection and personalized advertising.

Variables that measure quantities changing over time, like stock prices or temperatures. These observations are treated as realizations of random variables.

Analyzing time series involves describing patterns, predicting future values and understanding the underlying process that generates the data.

The change in a stock index value over time, often expressed as a percentage. It helps understand the growth or decline of the index.

A way to represent stock returns by taking the logarithm of the ratio between consecutive stock index values. It helps simplify calculations and analyze volatility.

The tendency of stock returns to exhibit periods of high and low volatility over time. This pattern is typical of financial time series.

A mathematical function that quantifies the risk associated with a financial position, represented by a random variable X, taking into account the potential for losses and the probability of those losses occurring.

The set of all possible financial positions that satisfy certain integrability conditions, meaning their expected values to a certain power are finite.

It refers to the actual probability distribution of market events, based on real-world observations and historical data.

Risk measures can be defined either in terms of financial positions (X) or in terms of loss variables (L), which represent the negative of financial positions. Ensure consistency when applying results from different sources.

Downside risk measures focus on the potential for unfavorable outcomes, such as losses, while measures of dispersion capture the overall variability, including both positive and negative deviations.

A risk measure is used to assess the risk of a company by comparing the expected value of the financial position to the value of the risk measure itself.

They are often used to establish limits or thresholds for acceptable levels of risk across different business units. This helps ensure that individual risks remain manageable within the overall risk budget.

They are used to compare different financial positions, portfolios, or firms based on their risk profiles. This allows investors to make informed decisions about allocating capital or choosing investments.

A situation where financial institutions rely excessively on complex mathematical models to measure and manage risk, potentially leading to a false sense of security and underestimation of true risks.

A measure of risk that estimates the potential loss in value of a portfolio over a given time period with a specific confidence level. It was widely used during the financial crisis.

A method for managing risk that involves identifying, assessing, controlling, and reviewing risks across an organization. It helps organizations to mitigate potential threats and opportunities.

A situation in which the financial system becomes more vulnerable as the economy moves towards a recession. This can be triggered by a decline in asset prices, leading to further falls and amplifying the downturn.

The process of transferring financial risk from one party to another through financial instruments or contracts. This allows companies to manage their overall risk exposure by passing on certain risks to other entities.

The practice of managing risk across all levels of an organization, from senior management to individual departments. This ensures that risk management is embedded throughout the organization and is aligned with its overall goals.

A situation where financial institutions use excessive leverage, borrowing heavily to amplify their returns. This can lead to significant losses if the investments go wrong.

The process of transforming short-term liabilities into long-term assets. This can create mismatches in the maturity of assets and liabilities, leading to potential liquidity issues.