Combating Volatility Laundering: Unsmoothing Artificially Smoothed Returns
Introduction It is common knowledge that returns to hedge funds and other alternative investments [like private equity or real estate] are often highly seri...
Posts by Tag
- portfolio optimization 14
- correlation matrix 14
- covariance matrix 11
- volatility 6
- systemic risk 5
- google-sheets 4
- mean-variance 4
- hierarchical clustering 3
- principal component analysis 3
- turbulence index 3
- monte carlo simulation 3
- mutual funds 3
- tactical asset allocation 2
- adjusted prices 2
- bootstrap 2
- monte carlo 2
- sharpe ratio 2
- probabilistic sharpe ratio 2
- portfolio analysis 2
- gaussian mixture model 2
- value at risk 2
- index tracking 2
- clustering 2
- web page 1
- investable portfolio 1
- excel 1
- crypto-assets 1
- factor analysis 1
- absorption ratio 1
- stock market data web api 1
- maximum drawdown 1
- brownian motion 1
- hierarchical risk parity 1
- most diversified portfolio 1
- diversification ratio 1
- bonds 1
- etfs 1
- modified value at risk 1
- gaussian value at risk 1
- cornish-fisher 1
- gerber statistic 1
- multivariate gaussian distribution 1
- random orthogonal matrices 1
- missing asset returns 1
- random portfolio 1
- hedge funds 1
- risk parity 1
- moment-matching 1
- capital market assumptions 1
- portfolio return 1
- mean-variance optimization 1
- machine learning 1
portfolio optimization
Cluster Risk Parity: Equalizing Risk Contributions Between and Within Asset Classes
The equal risk contribution (ERC) portfolio, introduced in Maillard et al.1, is a portfolio aiming to equalize the risk contributions from [its] different co...
Sparse Index Tracking: Limiting the Number of Assets in an Index Tracking Portfolio
In the previous post, I introduced the index tracking problem1, which consists in finding a portfolio that tracks as closely as possible2 a given financial ...
Beyond Modified Value-at-Risk: Application of Gaussian Mixtures to the Computation of Value-at-Risk
In a previous post, I described a parametric approach to computing Value-at-Risk (VaR) - called modified VaR12 - that adjusts Gaussian VaR for asymmetry and...
Index Tracking: Reproducing the Performance of a Financial Market Index (and more)
An index tracking portfolio1 is a portfolio designed to track as closely2 as possible a financial market index when its exact replication3 is either impracti...
Managing Missing Asset Returns in Portfolio Analysis and Optimization: Backfilling through Residuals Recycling
In a multi-asset portfolio, it is usual that some assets have shorter return histories than others1. Problem is, the presence of assets whose return histori...
The Turbulence Index: Regime-based Partitioning of Asset Returns
The turbulence index, introduced in the previous blog post, is a measure of statistical unusualness of asset returns popularized by Kritzman and Li1. It pro...
The Diversification Ratio: Measuring Portfolio Diversification
Continuing the series of blog posts on diversification indicators, I describe in this post a correlation-based measure of portfolio diversification called th...
Mean-Variance Optimization in Practice: Subset Resampling-based Efficient Portfolios
In a previous post, I introduced near efficient portfolios, which are portfolios equivalent to mean-variance efficient portfolios in terms of risk-return bu...
Mean-Variance Optimization in Practice: Well Diversified (Near) Efficient Portfolios
One well-known stylized fact of the Markowitz’s mean-variance framework is that, irrespective of the quality of the estimates of asset returns and (co)varian...
Ulcer Performance Index Portfolio Optimization
The Ulcer Performance Index1 (UPI) is a portfolio reward-risk measure introduced by G. Martin2 similar in spirit to the Sharpe Ratio, but using the Ulcer In...
Beyond Hierarchical Risk Parity: Hierarchical Clustering-Based Risk Parity
In a previous post, I introduced the Hierarchical Risk Parity portfolio optimization algorithm1. In this post, I will present one of its variations, called ...
Hierarchical Risk Parity: Introducing Graph Theory and Machine Learning in Portfolio Optimizer
In this short post, I will introduce the Hierarchical Risk Parity portfolio optimization algorithm, initially described by Marcos Lopez de Prado1, and recen...
The Portfolio Optimization Machine
In 2018, guys at ReSolve Asset Management published the paper Portfolio Optimization: A General Framework for Portfolio Choice in which they describe a s...
correlation matrix
Correlation-Based Clustering: Spectral Clustering Methods
Clustering consists in trying to identify groups of “similar behavior”1 - called clusters - from a dataset, according to some chosen characteristics. An exa...
Covariance Matrix Forecasting: Iterated Exponentially Weighted Moving Average Model
In the previous post of this series on covariance matrix forecasting, I reviewed both the simple and the exponentially weighted moving average covariance mat...
From Volatility Forecasting to Covariance Matrix Forecasting: The Return of Simple and Exponentially Weighted Moving Average Models
In the initial post of the series on volatility forecasting, I described the simple and the exponentially weighted moving average forecasting models, that a...
Bootstrap Simulations with Exact Sample Mean Vector and Sample Covariance Matrix
Bootstrapping is a statistical method which consists in sampling with replacement from an original data set to compute the distribution of a desired statist...
Correlation Matrix Stress Testing: Random Perturbations of a Correlation Matrix
In the previous posts of this series, I detailed a methodology to perform stress tests on a correlation matrix by linearly shrinking a baseline correlation m...
The Gerber Statistic: A Robust Co-Movement Measure for Correlation Matrix Estimation
The Gerber statistic is a measure of co-movement similar in spirit to the Kendall’s Tau coefficient that has been introduced in Gerber et al.1 to estimate c...
The Informativeness: Measuring the Homogeneity of a Universe of Assets
In this post, I will describe a measure of the homogeneity of a universe of assets, called the informativeness, introduced by Brockmeier et al.1 in their pap...
Correlation Matrices Denoising: Results from Random Matrix Theory
The estimation of empirical correlation matrices in finance is known to be affected by noise, in the form of measurement error, due in part to the short leng...
Computation of Theory-Implied Correlation Matrices: Overview and Example
In this short post, I will provide an overview of the TIC algorithm1 introduced by Marcos Lopez de Prado in his paper Estimation of Theory-Implied Correlatio...
The Effective Number of Bets: Measuring Portfolio Diversification
Many different measures of portfolio diversification have been developed in the financial literature, from asset weights-based diversification measures like ...
Correlation Matrix Stress Testing: Shrinkage Toward the Lower and Upper Bounds of a Correlation Matrix
I previously described on this blog an intuitive way of performing stress tests on a correlation matrix, which consists in shrinking a baseline correlation ...
The Matrix Effective Rank: Measuring the Dimensionality of a Universe of Assets
Quantifying how diversified is a universe of assets is an open problem in quantitative finance, partly because there is no definite formula for diversificati...
Correlation Matrix Stress Testing: Shrinkage Toward an Equicorrelation Matrix
Financial research has consistently shown that correlations between assets tend to increase during crises and tend to decrease during recoveries1. The recen...
When a Correlation Matrix is not a Correlation Matrix: The Nearest Correlation Matrix Problem
Estimating how individual assets are moving together is an important part of many financial applications1 and the most commonly used measure for this is the...
covariance matrix
Covariance Matrix Forecasting: Iterated Exponentially Weighted Moving Average Model
In the previous post of this series on covariance matrix forecasting, I reviewed both the simple and the exponentially weighted moving average covariance mat...
From Volatility Forecasting to Covariance Matrix Forecasting: The Return of Simple and Exponentially Weighted Moving Average Models
In the initial post of the series on volatility forecasting, I described the simple and the exponentially weighted moving average forecasting models, that a...
Combating Volatility Laundering: Unsmoothing Artificially Smoothed Returns
Introduction It is common knowledge that returns to hedge funds and other alternative investments [like private equity or real estate] are often highly seri...
Bootstrap Simulations with Exact Sample Mean Vector and Sample Covariance Matrix
Bootstrapping is a statistical method which consists in sampling with replacement from an original data set to compute the distribution of a desired statist...
The Gerber Statistic: A Robust Co-Movement Measure for Correlation Matrix Estimation
The Gerber statistic is a measure of co-movement similar in spirit to the Kendall’s Tau coefficient that has been introduced in Gerber et al.1 to estimate c...
The Turbulence Index: Regime-based Partitioning of Asset Returns
The turbulence index, introduced in the previous blog post, is a measure of statistical unusualness of asset returns popularized by Kritzman and Li1. It pro...
The Diversification Ratio: Measuring Portfolio Diversification
Continuing the series of blog posts on diversification indicators, I describe in this post a correlation-based measure of portfolio diversification called th...
The Effective Number of Bets: Measuring Portfolio Diversification
Many different measures of portfolio diversification have been developed in the financial literature, from asset weights-based diversification measures like ...
The Absorption Ratio: Measuring Financial Risk, Part 2
In the previous post, I reviewed the turbulence index, an indicator of financial market stress periods based on the Mahalanobis distance, introduced by Chow...
The Turbulence Index: Measuring Financial Risk
One of the challenges in portfolio management is the timely detection of financial market stress periods, typically characterized by an increase in volatilit...
The Matrix Effective Rank: Measuring the Dimensionality of a Universe of Assets
Quantifying how diversified is a universe of assets is an open problem in quantitative finance, partly because there is no definite formula for diversificati...
volatility
Volatility Forecasting: HExp Model
In this series on volatility forecasting, I previously detailed the Heterogeneous AutoRegressive (HAR) volatility forecasting model that has become the workh...
From Volatility Forecasting to Covariance Matrix Forecasting: The Return of Simple and Exponentially Weighted Moving Average Models
In the initial post of the series on volatility forecasting, I described the simple and the exponentially weighted moving average forecasting models, that a...
Volatility Forecasting: HAR Model
Among the different members of the family of volatility forecasting models by weighted moving average1 like the simple and the exponentially weighted moving...
Volatility Forecasting: GARCH(1,1) Model
In the previous post of this series on volatility forecasting, I described the simple and the exponentially weighted moving average volatility forecasting m...
Volatility Forecasting: Simple and Exponentially Weighted Moving Average Models
One of the simplest and most pragmatic approach to volatility forecasting is to model the volatility of an asset as a weighted moving average of its past sq...
Range-Based Volatility Estimators: Overview and Examples of Usage
Volatility estimation and forecasting plays a crucial role in many areas of finance. For example, standard risk-based portfolio allocation methods (minimum ...
systemic risk
The Turbulence Index: Regime-based Partitioning of Asset Returns
The turbulence index, introduced in the previous blog post, is a measure of statistical unusualness of asset returns popularized by Kritzman and Li1. It pro...
The Informativeness: Measuring the Homogeneity of a Universe of Assets
In this post, I will describe a measure of the homogeneity of a universe of assets, called the informativeness, introduced by Brockmeier et al.1 in their pap...
The Absorption Ratio: Measuring Financial Risk, Part 2
In the previous post, I reviewed the turbulence index, an indicator of financial market stress periods based on the Mahalanobis distance, introduced by Chow...
The Turbulence Index: Measuring Financial Risk
One of the challenges in portfolio management is the timely detection of financial market stress periods, typically characterized by an increase in volatilit...
The Matrix Effective Rank: Measuring the Dimensionality of a Universe of Assets
Quantifying how diversified is a universe of assets is an open problem in quantitative finance, partly because there is no definite formula for diversificati...
google-sheets
Residualization of Risk Factors: Examples and Pitfalls
The most common approach to measuring portfolio (risk) factor exposures is linear regression analysis, which describes the relationship between a dependent ...
Replicating the J.P. Morgan Efficiente Index
The J.P. Morgan Efficiente 5 Index is a tactical asset allocation strategy designed by J.P. Morgan based on a broad universe of 13 ETFs. This post will illu...
Crypto-assets portfolio optimization and trade order creation with Google Sheets
Crypto-assets (Bitcoin, Ethereum…) have recently been attracting the attention of more and more investors, with for example JPMorgan Chase & Co. suggesti...
Integration in Google Sheets
In this post, I will show you how to integrate the Portfolio Optimizer Web API in Google Sheets. As a working example, I will display the mean-variance mini...
mean-variance
Combating Volatility Laundering: Unsmoothing Artificially Smoothed Returns
Introduction It is common knowledge that returns to hedge funds and other alternative investments [like private equity or real estate] are often highly seri...
Index Tracking: Reproducing the Performance of a Financial Market Index (and more)
An index tracking portfolio1 is a portfolio designed to track as closely2 as possible a financial market index when its exact replication3 is either impracti...
Mean-Variance Optimization in Practice: Subset Resampling-based Efficient Portfolios
In a previous post, I introduced near efficient portfolios, which are portfolios equivalent to mean-variance efficient portfolios in terms of risk-return bu...
Mean-Variance Optimization in Practice: Well Diversified (Near) Efficient Portfolios
One well-known stylized fact of the Markowitz’s mean-variance framework is that, irrespective of the quality of the estimates of asset returns and (co)varian...
hierarchical clustering
Computation of Theory-Implied Correlation Matrices: Overview and Example
In this short post, I will provide an overview of the TIC algorithm1 introduced by Marcos Lopez de Prado in his paper Estimation of Theory-Implied Correlatio...
Beyond Hierarchical Risk Parity: Hierarchical Clustering-Based Risk Parity
In a previous post, I introduced the Hierarchical Risk Parity portfolio optimization algorithm1. In this post, I will present one of its variations, called ...
Hierarchical Risk Parity: Introducing Graph Theory and Machine Learning in Portfolio Optimizer
In this short post, I will introduce the Hierarchical Risk Parity portfolio optimization algorithm, initially described by Marcos Lopez de Prado1, and recen...
principal component analysis
The Effective Number of Bets: Measuring Portfolio Diversification
Many different measures of portfolio diversification have been developed in the financial literature, from asset weights-based diversification measures like ...
The Absorption Ratio: Measuring Financial Risk, Part 2
In the previous post, I reviewed the turbulence index, an indicator of financial market stress periods based on the Mahalanobis distance, introduced by Chow...
The Matrix Effective Rank: Measuring the Dimensionality of a Universe of Assets
Quantifying how diversified is a universe of assets is an open problem in quantitative finance, partly because there is no definite formula for diversificati...
turbulence index
Beyond Modified Value-at-Risk: Application of Gaussian Mixtures to the Computation of Value-at-Risk
In a previous post, I described a parametric approach to computing Value-at-Risk (VaR) - called modified VaR12 - that adjusts Gaussian VaR for asymmetry and...
The Turbulence Index: Regime-based Partitioning of Asset Returns
The turbulence index, introduced in the previous blog post, is a measure of statistical unusualness of asset returns popularized by Kritzman and Li1. It pro...
The Turbulence Index: Measuring Financial Risk
One of the challenges in portfolio management is the timely detection of financial market stress periods, typically characterized by an increase in volatilit...
monte carlo simulation
Random Portfolio Benchmarking: Simulation-based Performance Evaluation in Finance
As noted in Surz1, the question “Is [a mutual fund’s]2 performance good?” can only be answered relative to something1, typically by comparing that fund to a ...
Simulation from a Multivariate Normal Distribution with Exact Sample Mean Vector and Sample Covariance Matrix
In the research report Random rotations and multivariate normal simulation1, Robert Wedderburn introduced an algorithm to simulate i.i.d. samples from a mu...
The Turbulence Index: Regime-based Partitioning of Asset Returns
The turbulence index, introduced in the previous blog post, is a measure of statistical unusualness of asset returns popularized by Kritzman and Li1. It pro...
mutual funds
Random Portfolio Benchmarking: Simulation-based Performance Evaluation in Finance
As noted in Surz1, the question “Is [a mutual fund’s]2 performance good?” can only be answered relative to something1, typically by comparing that fund to a ...
Sparse Index Tracking: Limiting the Number of Assets in an Index Tracking Portfolio
In the previous post, I introduced the index tracking problem1, which consists in finding a portfolio that tracks as closely as possible2 a given financial ...
Index Tracking: Reproducing the Performance of a Financial Market Index (and more)
An index tracking portfolio1 is a portfolio designed to track as closely2 as possible a financial market index when its exact replication3 is either impracti...
tactical asset allocation
Random Portfolio Benchmarking: Simulation-based Performance Evaluation in Finance
As noted in Surz1, the question “Is [a mutual fund’s]2 performance good?” can only be answered relative to something1, typically by comparing that fund to a ...
Replicating the J.P. Morgan Efficiente Index
The J.P. Morgan Efficiente 5 Index is a tactical asset allocation strategy designed by J.P. Morgan based on a broad universe of 13 ETFs. This post will illu...
adjusted prices
Adjusted Prices Without Look-Ahead Bias
When backtesting an investment strategy, that is, when simulating an investment strategy using historical prices to test how this strategy would have behaved...
Selecting a Stock Market Data (Web) API: Not So Simple
I am sometimes asked if I recommend any stock market data (web) API for a personal use, especially because I mention Alpha Vantage and Tiingo in a couple of ...
bootstrap
Bootstrap Simulations with Exact Sample Mean Vector and Sample Covariance Matrix
Bootstrapping is a statistical method which consists in sampling with replacement from an original data set to compute the distribution of a desired statist...
Bootstrap Simulation with Portfolio Optimizer: Usage for Financial Planning
In statistics, a bootstrap method, also called bootstrapping, is a compute-intensive procedure that allows to estimate the distribution of a statistic throu...
monte carlo
Bootstrap Simulations with Exact Sample Mean Vector and Sample Covariance Matrix
Bootstrapping is a statistical method which consists in sampling with replacement from an original data set to compute the distribution of a desired statist...
Bootstrap Simulation with Portfolio Optimizer: Usage for Financial Planning
In statistics, a bootstrap method, also called bootstrapping, is a compute-intensive procedure that allows to estimate the distribution of a statistic throu...
probabilistic sharpe ratio
The Probabilistic Sharpe Ratio: Hypothesis Testing and Minimum Track Record Length for the Difference of Sharpe Ratios
In the first post of this series about the Sharpe ratio considered as a statistical estimator, I introduced a probabilistic framework to answer the question...
The Probabilistic Sharpe Ratio: Bias-Adjustment, Confidence Intervals, Hypothesis Testing and Minimum Track Record Length
The Sharpe ratio1 is one of the most commonly used measure of financial portfolio performance, but because it is deeply rooted in mean-variance theory, its ...
portfolio analysis
Managing Missing Asset Returns in Portfolio Analysis and Optimization: Backfilling through Residuals Recycling
In a multi-asset portfolio, it is usual that some assets have shorter return histories than others1. Problem is, the presence of assets whose return histori...
The Diversification Ratio: Measuring Portfolio Diversification
Continuing the series of blog posts on diversification indicators, I describe in this post a correlation-based measure of portfolio diversification called th...
gaussian mixture model
Beyond Modified Value-at-Risk: Application of Gaussian Mixtures to the Computation of Value-at-Risk
In a previous post, I described a parametric approach to computing Value-at-Risk (VaR) - called modified VaR12 - that adjusts Gaussian VaR for asymmetry and...
The Turbulence Index: Regime-based Partitioning of Asset Returns
The turbulence index, introduced in the previous blog post, is a measure of statistical unusualness of asset returns popularized by Kritzman and Li1. It pro...
value at risk
Beyond Modified Value-at-Risk: Application of Gaussian Mixtures to the Computation of Value-at-Risk
In a previous post, I described a parametric approach to computing Value-at-Risk (VaR) - called modified VaR12 - that adjusts Gaussian VaR for asymmetry and...
Corrected Cornish-Fisher Expansion: Improving the Accuracy of Modified Value-at-Risk
Modified Value-at-Risk (mVaR) is a parametric approach to computing Value-at-Risk introduced by Zangari1 that adjusts Gaussian Value-at-Risk for asymmetry a...
index tracking
Sparse Index Tracking: Limiting the Number of Assets in an Index Tracking Portfolio
In the previous post, I introduced the index tracking problem1, which consists in finding a portfolio that tracks as closely as possible2 a given financial ...
Index Tracking: Reproducing the Performance of a Financial Market Index (and more)
An index tracking portfolio1 is a portfolio designed to track as closely2 as possible a financial market index when its exact replication3 is either impracti...
clustering
Correlation-Based Clustering: Spectral Clustering Methods
Clustering consists in trying to identify groups of “similar behavior”1 - called clusters - from a dataset, according to some chosen characteristics. An exa...
Cluster Risk Parity: Equalizing Risk Contributions Between and Within Asset Classes
The equal risk contribution (ERC) portfolio, introduced in Maillard et al.1, is a portfolio aiming to equalize the risk contributions from [its] different co...
web page
Integration in a Web Page
In this post, I will show how to integrate the Portfolio Optimizer Web API in a web page. As a working example, I will display the mean-variance minimum var...
investable portfolio
Implementing an Investable Portfolio From an Optimal Portfolio
As an investor, have you ever wondered how to convert an optimal portfolio1, possibly made of real-valued weights with dozens of decimals (e.g., 12.3456789%)...
excel
Integration in Excel
In this post, I will show you how to integrate the Portfolio Optimizer Web API in Excel. As a working example, I will display the assets weights $(w_1, w_2)...
crypto-assets
Crypto-assets portfolio optimization and trade order creation with Google Sheets
Crypto-assets (Bitcoin, Ethereum…) have recently been attracting the attention of more and more investors, with for example JPMorgan Chase & Co. suggesti...
factor analysis
Residualization of Risk Factors: Examples and Pitfalls
The most common approach to measuring portfolio (risk) factor exposures is linear regression analysis, which describes the relationship between a dependent ...
absorption ratio
The Absorption Ratio: Measuring Financial Risk, Part 2
In the previous post, I reviewed the turbulence index, an indicator of financial market stress periods based on the Mahalanobis distance, introduced by Chow...
stock market data web api
Selecting a Stock Market Data (Web) API: Not So Simple
I am sometimes asked if I recommend any stock market data (web) API for a personal use, especially because I mention Alpha Vantage and Tiingo in a couple of ...
maximum drawdown
Trading Strategy Monitoring: Modeling the PnL as a Geometric Brownian Motion
Systematic trading strategies have the unfortunate habit of exhibiting worse performances in real-life than in backtests, partially due to backtest overfitti...
brownian motion
Trading Strategy Monitoring: Modeling the PnL as a Geometric Brownian Motion
Systematic trading strategies have the unfortunate habit of exhibiting worse performances in real-life than in backtests, partially due to backtest overfitti...
hierarchical risk parity
The Informativeness: Measuring the Homogeneity of a Universe of Assets
In this post, I will describe a measure of the homogeneity of a universe of assets, called the informativeness, introduced by Brockmeier et al.1 in their pap...
most diversified portfolio
The Diversification Ratio: Measuring Portfolio Diversification
Continuing the series of blog posts on diversification indicators, I describe in this post a correlation-based measure of portfolio diversification called th...
diversification ratio
The Diversification Ratio: Measuring Portfolio Diversification
Continuing the series of blog posts on diversification indicators, I describe in this post a correlation-based measure of portfolio diversification called th...
bonds
The Mathematics of Bonds: Simulating the Returns of Constant Maturity Government Bond ETFs
With more than $1.2 trillion under management in the U.S. as of mid-July 20221, investors are more and more using bond ETFs as building blocks in their asset...
etfs
The Mathematics of Bonds: Simulating the Returns of Constant Maturity Government Bond ETFs
With more than $1.2 trillion under management in the U.S. as of mid-July 20221, investors are more and more using bond ETFs as building blocks in their asset...
modified value at risk
Corrected Cornish-Fisher Expansion: Improving the Accuracy of Modified Value-at-Risk
Modified Value-at-Risk (mVaR) is a parametric approach to computing Value-at-Risk introduced by Zangari1 that adjusts Gaussian Value-at-Risk for asymmetry a...
gaussian value at risk
Corrected Cornish-Fisher Expansion: Improving the Accuracy of Modified Value-at-Risk
Modified Value-at-Risk (mVaR) is a parametric approach to computing Value-at-Risk introduced by Zangari1 that adjusts Gaussian Value-at-Risk for asymmetry a...
cornish-fisher
Corrected Cornish-Fisher Expansion: Improving the Accuracy of Modified Value-at-Risk
Modified Value-at-Risk (mVaR) is a parametric approach to computing Value-at-Risk introduced by Zangari1 that adjusts Gaussian Value-at-Risk for asymmetry a...
gerber statistic
The Gerber Statistic: A Robust Co-Movement Measure for Correlation Matrix Estimation
The Gerber statistic is a measure of co-movement similar in spirit to the Kendall’s Tau coefficient that has been introduced in Gerber et al.1 to estimate c...
multivariate gaussian distribution
Simulation from a Multivariate Normal Distribution with Exact Sample Mean Vector and Sample Covariance Matrix
In the research report Random rotations and multivariate normal simulation1, Robert Wedderburn introduced an algorithm to simulate i.i.d. samples from a mu...
random orthogonal matrices
Simulation from a Multivariate Normal Distribution with Exact Sample Mean Vector and Sample Covariance Matrix
In the research report Random rotations and multivariate normal simulation1, Robert Wedderburn introduced an algorithm to simulate i.i.d. samples from a mu...
missing asset returns
Managing Missing Asset Returns in Portfolio Analysis and Optimization: Backfilling through Residuals Recycling
In a multi-asset portfolio, it is usual that some assets have shorter return histories than others1. Problem is, the presence of assets whose return histori...
random portfolio
Random Portfolio Benchmarking: Simulation-based Performance Evaluation in Finance
As noted in Surz1, the question “Is [a mutual fund’s]2 performance good?” can only be answered relative to something1, typically by comparing that fund to a ...
hedge funds
Random Portfolio Benchmarking: Simulation-based Performance Evaluation in Finance
As noted in Surz1, the question “Is [a mutual fund’s]2 performance good?” can only be answered relative to something1, typically by comparing that fund to a ...
risk parity
Cluster Risk Parity: Equalizing Risk Contributions Between and Within Asset Classes
The equal risk contribution (ERC) portfolio, introduced in Maillard et al.1, is a portfolio aiming to equalize the risk contributions from [its] different co...
moment-matching
Bootstrap Simulations with Exact Sample Mean Vector and Sample Covariance Matrix
Bootstrapping is a statistical method which consists in sampling with replacement from an original data set to compute the distribution of a desired statist...
capital market assumptions
Capital Market Assumptions: Combining Institutions’ Forecasts for Improved Accuracy
Capital market assumptions1 (CMAs) are forecasts of future risk/return characteristics for broad asset classes over the next 5 to 20 years produced by leadi...
portfolio return
The Mathematics of Portfolio Return: Simple Return, Money-Weighted Return and Time-Weighted Return
Whether we manage our own investment assets or choose to hire others to manage the assets on our behalf we are keen to know how well our […] portfolio of ass...
mean-variance optimization
Supervised Portfolios: A Supervised Machine Learning Approach to Portfolio Optimization
Standard portfolio allocation algorithms like Markowitz mean-variance optimization or Choueffati diversification ratio optimization usually take in input as...
machine learning
Supervised Portfolios: A Supervised Machine Learning Approach to Portfolio Optimization
Standard portfolio allocation algorithms like Markowitz mean-variance optimization or Choueffati diversification ratio optimization usually take in input as...