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Quantitative Risk Management in R

Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.

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Descrição do Curso

In Quantitative Risk Management (QRM), you will build models to understand the risks of financial portfolios. This is a vital task across the banking, insurance and asset management industries. The first step in the model building process is to collect data on the underlying risk factors that affect portfolio value and analyze their behavior. In this course, you will learn how to work with risk-factor return series, study the empirical properties or so-called "stylized facts" of these data - including their typical non-normality and volatility, and make estimates of value-at-risk for a portfolio.

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1
Exploring Market Risk-Factor Data
Grátis
In this chapter, you will learn how to form return series, aggregate them over longer periods and plot them in different ways. You will look at examples using the qrmdata package.
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Welcome to the course!
50 xp
Exploring risk-factor time series: equity indexes
100 xp
Exploring risk-factor time series: individual equities
100 xp
Exploring risk-factor data: exchange rates
100 xp
Risk-factor returns
50 xp
Exploring return series
100 xp
Different ways of plotting risk-factor and return series
100 xp
Aggregating log-returns
50 xp
Aggregating log-return series
100 xp
A test on aggregation of log-returns
50 xp
Exploring other kinds of risk factors
50 xp
Commodities data
100 xp
Interest-rate data
100 xp
2
Real World Returns are Riskier Than Normal
In this chapter, you will learn about graphical and numerical tests of normality, apply them to different datasets, and consider the alternative Student t model.
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The normal distribution
50 xp
Graphical methods for assessing normality
100 xp
Testing for normality
50 xp
Q-Q plots for assessing normality
100 xp
Skewness, kurtosis and the Jarque-Bera test
50 xp
Numerical tests of normality
100 xp
Testing normality for longer time horizons
100 xp
Overlapping returns
100 xp
Reviewing knowledge of normal distributions and returns
50 xp
The Student t distribution
50 xp
Fitting t distribution to data
100 xp
Testing FX returns for normality
100 xp
Testing interest-rate returns for normality
100 xp
Testing gold price returns for normality
50 xp
3
Real World Returns are Volatile and Correlated
In this chapter, you will learn about volatility and how to detect it using act plots. You will learn how to apply Ljung-Box tests for serial correlation and estimate cross correlations.
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Characteristics of volatile return series
50 xp
Spotting a volatile time series
100 xp
Estimating serial correlations
50 xp
Using acf plots to reveal volatility
100 xp
The Ljung-Box test
50 xp
Applying Ljung-Box tests to return data
100 xp
Applying Ljung-Box tests to longer-interval returns
100 xp
Looking at the extremes in volatile return series
50 xp
Extreme values in volatile time series
100 xp
Cross correlations between risk-factor return series
100 xp
The stylized facts of return series
50 xp
Volatility and correlation of FX returns
100 xp
Volatility and correlation of interest-rate data
100 xp
Reviewing knowledge of volatility and correlation
50 xp
4
Estimating Portfolio Value-at-Risk (VaR)
In this chapter, the concept of value-at-risk and simple methods of estimating VaR based on historical simulation are introduced.
Reproduzir Capítulo Agora
Value-at-risk and expected shortfall
50 xp
Computing VaR and ES for normal distribution
100 xp
International equity portfolio
50 xp
Examining risk factors for international equity portfolio
100 xp
Historical simulation
100 xp
Estimating VaR and ES
100 xp
Option portfolio and Black Scholes
50 xp
Compute Black-Scholes price of an option
100 xp
Equity and implied volatility risk factors
100 xp
Historical simulation for the option example
50 xp
Historical simulation of losses for option portfolio
100 xp
Estimating VaR and ES for option portfolio
100 xp
Computing VaR for weekly losses
50 xp
Wrap-up
50 xp

Para Empresas

Treinar 2 ou mais pessoas?

Obtenha acesso à biblioteca completa do DataCamp, com relatórios, atribuições, projetos e muito mais centralizados

Nas seguintes faixas

Finanças Aplicadas em R

Ir para a trilha

Analista quantitativo com R

Ir para a trilha

Colaboradores

Lore Dirick

Pre Requisitos

Manipulating Time Series Data in R

Alexander J. McNeil

Professor of Actuarial Science at the University of York.

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