Organizing the rules of data quality into dimensions not only improves the specification and measurement of the data quality, it also provides the framework under which quality can be measured and reported. This in turn enables better governance of data quality. Tools can then be built around this to determine the minimum levels required to [...]
Archive | FRM Exam
RSS feed for this sectionRisk Taking: A Corporate Governance Perspective
This entry is part 1 of 1 in the series Risk Taking: A Corporate Governance PerspectiveThis reading is a part of the syllabus for FRM Part 1 Exam in the section ‘Foundations of Risk Management’. In the first two sections the book lays out the scope of risk management by defining risk and exploring risk governance. The [...]
Standard Error in Linear Regression
This entry is part 6 of 8 in the series Linear RegressionA simple (two-variable) regression has three standard errors: one for each coefficient (slope, intercept) and one for the predicted Y (standard error of regression). While the population regression function (PRF) is singular, sample regression functions (SRF) are plural. Each sample produces a different SRF. [...]
Type I and Type II Errors
This entry is part 2 of 6 in the series Introduction to StatisticsWhen drawing an inference (from a sample statistic, about a population parameter), there can be two types of errors: Type I and Type II. Type I error, also known as error of the first kind, occurs when the null hypothesis is true, but [...]
Bootstrapping Value at Risk (VaR)
This is an illustration, using a simple portfolio of four stocks over one week, of the bootstrap method. Like the Monte Carlo, we want to simulate each stock (in the portfolio) forward in time. If today is time t, then we want to simulate the stock on t+1, t+2, t+3, etc. The key difference is: [...]
Extreme Value Theory
This entry is part 6 of 6 in the series Introduction to Quantitative FinanceExtreme value theory (EVT) aims to remedy a deficiency with value at risk (i.e., it gives no information about losses that exceed the VaR) and glaring weakness of delta normal value at risk (VaR): the dreaded-fat tails. The key is the idea [...]
How to Scale Autocorrelated Returns?
This entry is part 4 of 6 in the series Introduction to Quantitative FinanceWe know that the square root rule can be used to scale volatility with time. This rule assumes that the returns are independent and identically distributed. However, this assumption is not very realistic. This video illustrates a scaling factor that adjusts the [...]
How to Forecast Volatility Using GARCH (1,1)
This entry is part 7 of 8 in the series VolatilityThis video discusses how to use GARCH(1,1) to forecast future volatility. The key parameter is persistence (alpha + beta): high persistence implies slow decay toward the long run average. GARCH models were developed by Robert Engle to deal with the problem of auto-correlated residuals (which [...]
Using GARCH (1,1) Approach to Estimate Volatility
This entry is part 6 of 8 in the series VolatilityThis video provides an introduction to the GARCH approach to estimating volatility, i.e., Generalized AutoRegressive Conditional Heteroskedasticity. GARCH is a preferred method for finance professionals as it provides a more real-life estimate while predicting parameters such as volatility, prices and returns. GARCH(1,1) estimates volatility in [...]
Volatility: Exponentially Weighted Moving Average (EWMA)
This entry is part 5 of 8 in the series VolatilityThe EWMA approach to volatility is an improvement over simple volatility because it assigns greater weight to more recent observations (in fact, the weights are proportional). This video explains the EWMA approach. This video is developed by David from Bionic Turtle.