Kapitel 3 Portfolios Flashcards
What is a portfolio?
A portfolio is a collection of different asset positions which are owned by the same investor, group of investors or the same company.
How does the risk of a portfolio differ from the risk of the individual positions ?
A portfolio’s risk is lower than the risk of its individual positions because the positions tend to move in different directions, reducing the overall risk.
What is the volatility of a portfolio?
The volatility of a portfolio is a measure of how much the value of the portfolio fluctuates over time. It is calculated by taking the standard deviation of the returns of the portfolio.
Why is the volatility of a portfolio not equal to the weighted average of the volatilities of the individual portfolio constituents?
A portfolio’s volatility is not the weighted average of its constituents’ volatilities due to compensation effects between positions. This can result in a lower overall portfolio volatility than the weighted average of individual positions.
How are covariance and correlation coefficient used to measure the interdependencies between the positions in a portfolio?
Covariance is a measure of how much the returns of two assets move together. Correlation coefficient is a measure of how closely the prices of two assets move together. Both covariance and correlation coefficient can be used to measure the interdependencies between the positions in a portfolio.
What is portfolio selection?
Portfolio selection is the process of choosing the assets that will make up a portfolio. The goal of portfolio selection is to create a portfolio that has the desired level of risk and return.
How does portfolio selection impact the return of the investment?
Portfolio selection can impact the return of the investment in two ways. First, the choice of assets can impact the expected return of the portfolio. Second, the diversification of the portfolio can impact the volatility of the portfolio.
What is the beta of a portfolio?
The beta of a portfolio is the weighted average of the betas of the assets in the portfolio.
What is the importance of beta?
Beta is an important measure of risk because it can be used to estimate the risk of a portfolio. A higher beta means that the portfolio is more risky, while a lower beta means that the portfolio is less risky.
What is historical simulation?
Historical simulation is a non-parametric approach to estimating value-at-risk (VaR). It does not require any distributional assumptions about the underlying risk factors. Instead, it uses historical data to generate a distribution of possible outcomes.
How does historical simulation work?
Historical simulation works by first collecting historical data on the risk factors that drive the value of the portfolio. These risk factors can include stock prices, interest rates, and exchange rates. The next step is to calculate the resulting returns for each historical data point. These returns are then sorted from worst to best. The quantile of interest is then determined by counting the number of returns that are worse than the quantile of interest.
What are the benefits of historical simulation?
The benefits of historical simulation include:
It is relatively easy to implement.
It does not require any distributional assumptions.
It can be used to estimate VaR for any type of asset or portfolio.
What are the limitations of historical simulation?
The limitations of historical simulation include:
It is only as good as the historical data that is used.
It is not as accurate as parametric methods when the underlying risk factors are not normally distributed.
It can be sensitive to outliers in the historical data.
What is backtesting?
Backtesting is a method of evaluating the accuracy of a VaR model by comparing the model’s predictions to actual losses.
What is the purpose of backtesting?
The purpose of backtesting is to ensure that a VaR model is providing accurate and reliable estimates of risk.
How is backtesting done?
Backtesting is done by using historical data to generate a series of simulated losses. The model’s predictions are then compared to the actual losses to see how closely they match.
What are the limitations of backtesting?
There are a number of limitations to backtesting, including:
-The accuracy of backtesting is dependent on the quality of the historical data used.
-Backtesting cannot account for changes in market conditions or the composition of a portfolio over time.
-Backtesting can be computationally expensive, especially for large portfolios.
What are Lower Partial Moments (LPMs)?
Lower Partial Moments (LPMs) are a set of risk measures that focus on the downside risk of an asset or portfolio. LPMs are calculated by taking the n-th percentile of the asset or portfolio’s return distribution and then measuring the average loss below that percentile.
Why are LPMs important?
LPMs are important because they can provide a more accurate measure of risk than traditional risk measures, such as variance and standard deviation. This is because LPMs focus on the losses that are most likely to occur, rather than the average loss.
What are some of the benefits of using LPMs?
Some of the benefits of using LPMs include:
-LPMs can provide a more accurate measure of risk than traditional risk measures.
-LPMs can be used to identify assets or portfolios that are more likely to experience losses.
-LPMs can be used to construct risk-adjusted performance measures.
What are some of the limitations of using LPMs?
Some of the limitations of using LPMs include:
-LPMs can be more difficult to interpret than traditional risk measures.
-LPMs can be more sensitive to changes in the data.
-LPMs can be more computationally expensive to calculate than traditional risk measures.
What are the two alternative approaches for obtaining portfolio VaR using historical portfolio values?
The two alternative approaches for obtaining portfolio VaR using historical portfolio values are hybrid approaches that combine non-parametric and parametric methods.
What are scoring models and how do they offer a solution in risk assessment?
Scoring models, also known as scoring models or Nutzwertanalysen, offer a solution in cases where direct monetary measurement and probability specification are not possible.
How do scoring models assist in assessing credit risk?
Scoring models, such as credit scoring models, are used to evaluate credit risk by assigning scores or ratings to borrowers based on various predetermined criteria. These criteria can include factors such as credit history, income level, and other relevant indicators.
