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PORTFOLIO PROBE
Portfolio Probe - Portfolio fund management software. Generate random portfolios and perform portfolio optimization. Download free 30 daydemo.
THE TOP 7 PORTFOLIO OPTIMIZATION PROBLEMS Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements).If you are
CONSTRAINTS
The constraints that are implemented in Portfolio Probe are: Monetary Value Constraints Control the amount of money in the portfolio. more about the Monetary Value Constraints Turnover Constraint Control the amount of money (buys plus sells) that can be traded. more about Turnover Constraint Long-Only Constraint Disallow the possibility ofshort positions.
READ A TAB-SEPARATED FILE INTO R Task Create an R object that contains the data from a tab-separated file (which probably has the file extension "txt"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itxassetCountrySector
WINSORIZATION
USING R PACKAGES
Explanation Packages are a basic unit of functionality in R. A package typically contains a number of functions pertinent to a topic. Some packages contain data objects (in addition to or instead of functions). There is a distinction between packages that are installed on your machine and packages that are loaded into a particular R THE BASICS OF VALUE AT RISK AND EXPECTED SHORTFALL Value at Risk and Expected Shortfall are common risk measures. Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of Continue reading → THE DISTRIBUTION OF FINANCIAL RETURNS MADE SIMPLESEE MORE ONPORTFOLIOPROBE.COM
THE EFFECT OF BETA EQUAL 1 VOLATILITY FROM DAILY OR MONTHLY: GARCH EVIDENCESEE MORE ONPORTFOLIOPROBE.COM
PORTFOLIO PROBE
Portfolio Probe - Portfolio fund management software. Generate random portfolios and perform portfolio optimization. Download free 30 daydemo.
THE TOP 7 PORTFOLIO OPTIMIZATION PROBLEMS Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements).If you are
CONSTRAINTS
The constraints that are implemented in Portfolio Probe are: Monetary Value Constraints Control the amount of money in the portfolio. more about the Monetary Value Constraints Turnover Constraint Control the amount of money (buys plus sells) that can be traded. more about Turnover Constraint Long-Only Constraint Disallow the possibility ofshort positions.
READ A TAB-SEPARATED FILE INTO R Task Create an R object that contains the data from a tab-separated file (which probably has the file extension "txt"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itxassetCountrySector
WINSORIZATION
USING R PACKAGES
Explanation Packages are a basic unit of functionality in R. A package typically contains a number of functions pertinent to a topic. Some packages contain data objects (in addition to or instead of functions). There is a distinction between packages that are installed on your machine and packages that are loaded into a particular R THE BASICS OF VALUE AT RISK AND EXPECTED SHORTFALL Value at Risk and Expected Shortfall are common risk measures. Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of Continue reading → THE DISTRIBUTION OF FINANCIAL RETURNS MADE SIMPLESEE MORE ONPORTFOLIOPROBE.COM
THE EFFECT OF BETA EQUAL 1 VOLATILITY FROM DAILY OR MONTHLY: GARCH EVIDENCESEE MORE ONPORTFOLIOPROBE.COM
DEMO OR BUY
Demo A free 30-day demonstration copy is available if you qualify. This is a fully functional version -- the only limitation is the number of days the software will work. A key criterion for qualifying is that you are asking for it as a business and not as an individual. If you would like to Continue reading → APPLICATIONS OF RANDOM PORTFOLIOS There are (at least) three general areas for which random portfolios are useful: Performance Risk Quant Performance The concepts of "benchmark" and "peer group" have been extensively used in performance. Random portfolios are superior because: they are more specific to a particular fund they extract more information from the market The picture is an exampleWINSORIZATION
Winsorization replaces extreme data values with less extreme values. But why Extreme values sometimes have a big effect on statistical operations. That effect is not necessarily a good effect. One approach to the problem is to change the statistical operation — this is the field of robust statistics. An alternative solution is to just changePRICES TO RETURNS
Task Create a matrix of returns given a matrix of prices. Preparation price matrix of assets You need to have your matrix of asset prices available. Assets should be in the columns and times in the rows (earliest first, most recent last). Doing the example pprobeData package To do the example, the pprobeData package must Continuereading →
A BRIEF HISTORY OF S&P 500 BETA Data The data are daily returns starting at the beginning of 2007. There are 477 stocks for which there is full and seemingly reliable data. Estimation The betas are all estimated on one year of data. The times that identify the betas mark the point at which the estimate would become available. So the betas Continue reading → THE EFFECT OF BETA EQUAL 1 Investment Performance Guy had a post about beta equal 1. It made me wonder about the properties of portfolios with beta equal 1. When I looked, I got a bigger answer than I expected. Data I have some S&P 500 data lying about from the post ‘On “Stock correlation has been rising”‘. So laziness dictated Continue reading → ALPHA DECAY IN PORTFOLIOS Regarding the “fine print” issue, it would be possible to do an experiment to see what looks appropriate. Basically just do a backtest (or preferably several runs with different starting portfolios) and compare the achieved expected return in the backtest and the expected return when there is no turnover constraint to the alpha distribution at each point in time. A TALE OF TWO RETURNS It was the best of times, it was the worst of times. As you may have guessed, this is a mashup of a novel by Charles Dickens and an explanation of financial returns. The key plot element of A Tale of Two Cities is that there are two men, Charles Darnay and Sydney Carton, who Continue reading THE MYSTERY OF VOLATILITY ESTIMATES FROM DAILY VERSUS What drives the estimates apart? Previously A post by Investment Performance Guy prompted “Variability of volatility estimates from daily data”. In my comments to the original post I suggested that using daily data to estimate volatility would be equivalent to using monthly data except with less variability. Dave, the Investment Performance Guy, proposed the exquisitely IMPLIED ALPHA AND MINIMUM VARIANCE Under the covers of strange bedfellows. Previously The idea of implied alpha was introduced in “Implied alpha — almost wordless”. In a comment to that post Jeff noticed that the optimal portfolio given for the example is ever so close to the minimum variance portfolio. That is because there is a problem with the example Continue reading →PORTFOLIO PROBE
Portfolio Probe - Portfolio fund management software. Generate random portfolios and perform portfolio optimization. Download free 30 daydemo.
CONSTRAINTS
The constraints that are implemented in Portfolio Probe are: Monetary Value Constraints Control the amount of money in the portfolio. more about the Monetary Value Constraints Turnover Constraint Control the amount of money (buys plus sells) that can be traded. more about Turnover Constraint Long-Only Constraint Disallow the possibility ofshort positions.
GENERATE RANDOM PORTFOLIOS Description Random portfolios are a sample from the population of portfolios that obey some given set of constraints. The constraints are the key ingredient. For example, we may want 5000 portfolios that obey the constraints: long-only 75 to 80 names in the portfolio no asset may contribute a fraction of more than 3% to the Continuereading →
THE TOP 7 PORTFOLIO OPTIMIZATION PROBLEMS Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements).If you are
WINSORIZATION
READ A TAB-SEPARATED FILE INTO R Task Create an R object that contains the data from a tab-separated file (which probably has the file extension "txt"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itxassetCountrySector
THE BASICS OF VALUE AT RISK AND EXPECTED SHORTFALL Value at Risk and Expected Shortfall are common risk measures. Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of Continue reading → THE DISTRIBUTION OF FINANCIAL RETURNS MADE SIMPLESEE MORE ONPORTFOLIOPROBE.COM
THE MYSTERY OF VOLATILITY ESTIMATES FROM DAILY VERSUSSEE MORE ONPORTFOLIOPROBE.COM
VOLATILITY FROM DAILY OR MONTHLY: GARCH EVIDENCESEE MORE ONPORTFOLIOPROBE.COM
PORTFOLIO PROBE
Portfolio Probe - Portfolio fund management software. Generate random portfolios and perform portfolio optimization. Download free 30 daydemo.
CONSTRAINTS
The constraints that are implemented in Portfolio Probe are: Monetary Value Constraints Control the amount of money in the portfolio. more about the Monetary Value Constraints Turnover Constraint Control the amount of money (buys plus sells) that can be traded. more about Turnover Constraint Long-Only Constraint Disallow the possibility ofshort positions.
GENERATE RANDOM PORTFOLIOS Description Random portfolios are a sample from the population of portfolios that obey some given set of constraints. The constraints are the key ingredient. For example, we may want 5000 portfolios that obey the constraints: long-only 75 to 80 names in the portfolio no asset may contribute a fraction of more than 3% to the Continuereading →
THE TOP 7 PORTFOLIO OPTIMIZATION PROBLEMS Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements).If you are
WINSORIZATION
READ A TAB-SEPARATED FILE INTO R Task Create an R object that contains the data from a tab-separated file (which probably has the file extension "txt"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itxassetCountrySector
THE BASICS OF VALUE AT RISK AND EXPECTED SHORTFALL Value at Risk and Expected Shortfall are common risk measures. Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of Continue reading → THE DISTRIBUTION OF FINANCIAL RETURNS MADE SIMPLESEE MORE ONPORTFOLIOPROBE.COM
THE MYSTERY OF VOLATILITY ESTIMATES FROM DAILY VERSUSSEE MORE ONPORTFOLIOPROBE.COM
VOLATILITY FROM DAILY OR MONTHLY: GARCH EVIDENCESEE MORE ONPORTFOLIOPROBE.COM
CONTACT | PORTFOLIO PROBE | GENERATE RANDOM PORTFOLIOS Burns Statistics Limited Mailing address and registered office: 4-b Jodrell Road London E3 2LA United Kingdom Telephone: +44 (0)208 525 0696 Portfolio Probe sales: sales@portfolioprobe.com Portfolio Probe support: support@portfolioprobe.com Other: patrick@burns-stat.com View Patrick's LinkedIn profileLinkedIn Burns Statistics Limited is registered in England and Wales as company 4459872. VATWINSORIZATION
Winsorization replaces extreme data values with less extreme values. But why Extreme values sometimes have a big effect on statistical operations. That effect is not necessarily a good effect. One approach to the problem is to change the statistical operation — this is the field of robust statistics. An alternative solution is to just change A PRACTICAL INTRODUCTION TO GARCH MODELING We look at volatility clustering, and some aspects of modeling it with a univariate GARCH(1,1) model. Volatility clustering Volatility clustering — the phenomenon of there being periods of relative calm and periods of high volatility — is a seemingly universal attribute of market data. There is no universally accepted explanation of it. AN EASY MISTAKE WITH RETURNS When aggregating over both time and assets, the order of aggregation matters. Task We have the weights for a portfolio and we want to use those and a matrix of returns over time to compute the (long-term) portfolio return. “A tale of two returns” tells us that aggregation over time is easiest to do in Continue reading → A BRIEF HISTORY OF S&P 500 BETA Data The data are daily returns starting at the beginning of 2007. There are 477 stocks for which there is full and seemingly reliable data. Estimation The betas are all estimated on one year of data. The times that identify the betas mark the point at which the estimate would become available. So the betas Continue reading → A TALE OF TWO RETURNS It was the best of times, it was the worst of times. As you may have guessed, this is a mashup of a novel by Charles Dickens and an explanation of financial returns. The key plot element of A Tale of Two Cities is that there are two men, Charles Darnay and Sydney Carton, who Continue reading ALPHA DECAY IN PORTFOLIOS Regarding the “fine print” issue, it would be possible to do an experiment to see what looks appropriate. Basically just do a backtest (or preferably several runs with different starting portfolios) and compare the achieved expected return in the backtest and the expected return when there is no turnover constraint to the alpha distribution at each point in time. THE EFFECT OF BETA EQUAL 1 Investment Performance Guy had a post about beta equal 1. It made me wonder about the properties of portfolios with beta equal 1. When I looked, I got a bigger answer than I expected. Data I have some S&P 500 data lying about from the post ‘On “Stock correlation has been rising”‘. So laziness dictated Continue reading → ANOTHER LOOK AT AUTOCORRELATION IN THE S&P 500 Casting doubt on the possibility of mean reversion in the S&P 500 lately. Previously A look at volatility estimates in “The mystery of volatility estimates from daily versus monthly returns” led to considering the possibility of autocorrelation in the returns. I estimated an AR(1) model through time and added a naive confidenceinterval to the
VOLATILITY FROM DAILY OR MONTHLY: GARCH EVIDENCE Should you use daily or monthly returns to estimate volatility? Does garch explain why volatility estimated with daily data tends to be bigger than if it is estimated with monthly data? Previously There are a number of previous posts — with the variance compression tag — that discuss the phenomenon of volatility estimated with dailyPORTFOLIO PROBE
Portfolio Probe - Portfolio fund management software. Generate random portfolios and perform portfolio optimization. Download free 30 daydemo.
CONSTRAINTS
The constraints that are implemented in Portfolio Probe are: Monetary Value Constraints Control the amount of money in the portfolio. more about the Monetary Value Constraints Turnover Constraint Control the amount of money (buys plus sells) that can be traded. more about Turnover Constraint Long-Only Constraint Disallow the possibility ofshort positions.
GENERATE RANDOM PORTFOLIOS Description Random portfolios are a sample from the population of portfolios that obey some given set of constraints. The constraints are the key ingredient. For example, we may want 5000 portfolios that obey the constraints: long-only 75 to 80 names in the portfolio no asset may contribute a fraction of more than 3% to the Continuereading →
THE TOP 7 PORTFOLIO OPTIMIZATION PROBLEMS Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements).If you are
WINSORIZATION
PORTFOLIO PROBE
Portfolio Probe - Portfolio fund management software. Generate random portfolios and perform portfolio optimization. Download free 30 daydemo.
CONSTRAINTS
The constraints that are implemented in Portfolio Probe are: Monetary Value Constraints Control the amount of money in the portfolio. more about the Monetary Value Constraints Turnover Constraint Control the amount of money (buys plus sells) that can be traded. more about Turnover Constraint Long-Only Constraint Disallow the possibility ofshort positions.
GENERATE RANDOM PORTFOLIOS Description Random portfolios are a sample from the population of portfolios that obey some given set of constraints. The constraints are the key ingredient. For example, we may want 5000 portfolios that obey the constraints: long-only 75 to 80 names in the portfolio no asset may contribute a fraction of more than 3% to the Continuereading →
THE TOP 7 PORTFOLIO OPTIMIZATION PROBLEMS Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements).If you are
WINSORIZATION
READ A TAB-SEPARATED FILE INTO R Task Create an R object that contains the data from a tab-separated file (which probably has the file extension "txt"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itxassetCountrySector
THE BASICS OF VALUE AT RISK AND EXPECTED SHORTFALL Value at Risk and Expected Shortfall are common risk measures. Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of Continue reading → THE DISTRIBUTION OF FINANCIAL RETURNS MADE SIMPLESEE MORE ONPORTFOLIOPROBE.COM
THE MYSTERY OF VOLATILITY ESTIMATES FROM DAILY VERSUSSEE MORE ONPORTFOLIOPROBE.COM
VOLATILITY FROM DAILY OR MONTHLY: GARCH EVIDENCESEE MORE ONPORTFOLIOPROBE.COM
CONTACT | PORTFOLIO PROBE | GENERATE RANDOM PORTFOLIOS Burns Statistics Limited Mailing address and registered office: 4-b Jodrell Road London E3 2LA United Kingdom Telephone: +44 (0)208 525 0696 Portfolio Probe sales: sales@portfolioprobe.com Portfolio Probe support: support@portfolioprobe.com Other: patrick@burns-stat.com View Patrick's LinkedIn profileLinkedIn Burns Statistics Limited is registered in England and Wales as company 4459872. VATWINSORIZATION
Winsorization replaces extreme data values with less extreme values. But why Extreme values sometimes have a big effect on statistical operations. That effect is not necessarily a good effect. One approach to the problem is to change the statistical operation — this is the field of robust statistics. An alternative solution is to just change A PRACTICAL INTRODUCTION TO GARCH MODELING We look at volatility clustering, and some aspects of modeling it with a univariate GARCH(1,1) model. Volatility clustering Volatility clustering — the phenomenon of there being periods of relative calm and periods of high volatility — is a seemingly universal attribute of market data. There is no universally accepted explanation of it. AN EASY MISTAKE WITH RETURNS When aggregating over both time and assets, the order of aggregation matters. Task We have the weights for a portfolio and we want to use those and a matrix of returns over time to compute the (long-term) portfolio return. “A tale of two returns” tells us that aggregation over time is easiest to do in Continue reading → A BRIEF HISTORY OF S&P 500 BETA Data The data are daily returns starting at the beginning of 2007. There are 477 stocks for which there is full and seemingly reliable data. Estimation The betas are all estimated on one year of data. The times that identify the betas mark the point at which the estimate would become available. So the betas Continue reading → A TALE OF TWO RETURNS It was the best of times, it was the worst of times. As you may have guessed, this is a mashup of a novel by Charles Dickens and an explanation of financial returns. The key plot element of A Tale of Two Cities is that there are two men, Charles Darnay and Sydney Carton, who Continue reading ALPHA DECAY IN PORTFOLIOS Regarding the “fine print” issue, it would be possible to do an experiment to see what looks appropriate. Basically just do a backtest (or preferably several runs with different starting portfolios) and compare the achieved expected return in the backtest and the expected return when there is no turnover constraint to the alpha distribution at each point in time. THE EFFECT OF BETA EQUAL 1 Investment Performance Guy had a post about beta equal 1. It made me wonder about the properties of portfolios with beta equal 1. When I looked, I got a bigger answer than I expected. Data I have some S&P 500 data lying about from the post ‘On “Stock correlation has been rising”‘. So laziness dictated Continue reading → ANOTHER LOOK AT AUTOCORRELATION IN THE S&P 500 Casting doubt on the possibility of mean reversion in the S&P 500 lately. Previously A look at volatility estimates in “The mystery of volatility estimates from daily versus monthly returns” led to considering the possibility of autocorrelation in the returns. I estimated an AR(1) model through time and added a naive confidenceinterval to the
VOLATILITY FROM DAILY OR MONTHLY: GARCH EVIDENCE Should you use daily or monthly returns to estimate volatility? Does garch explain why volatility estimated with daily data tends to be bigger than if it is estimated with monthly data? Previously There are a number of previous posts — with the variance compression tag — that discuss the phenomenon of volatility estimated with dailyPORTFOLIO PROBE
Portfolio Probe - Portfolio fund management software. Generate random portfolios and perform portfolio optimization. Download free 30 daydemo.
CONSTRAINTS
The constraints that are implemented in Portfolio Probe are: Monetary Value Constraints Control the amount of money in the portfolio. more about the Monetary Value Constraints Turnover Constraint Control the amount of money (buys plus sells) that can be traded. more about Turnover Constraint Long-Only Constraint Disallow the possibility ofshort positions.
GENERATE RANDOM PORTFOLIOS Description Random portfolios are a sample from the population of portfolios that obey some given set of constraints. The constraints are the key ingredient. For example, we may want 5000 portfolios that obey the constraints: long-only 75 to 80 names in the portfolio no asset may contribute a fraction of more than 3% to the Continuereading →
THE TOP 7 PORTFOLIO OPTIMIZATION PROBLEMS Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements).If you are
WINSORIZATION
READ A TAB-SEPARATED FILE INTO R Task Create an R object that contains the data from a tab-separated file (which probably has the file extension "txt"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itxassetCountrySector
THE BASICS OF VALUE AT RISK AND EXPECTED SHORTFALL Value at Risk and Expected Shortfall are common risk measures. Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of Continue reading → IMPLIED ALPHA AND MINIMUM VARIANCE THE MYSTERY OF VOLATILITY ESTIMATES FROM DAILY VERSUSSEE MORE ONPORTFOLIOPROBE.COM
THE HALF VARIANCE APPROXIMATION FOR MEAN RETURNS What’s that thing about arithmetic and geometric returns and the variance? Previously An introduction to the difference between simple and log returns is: A tale of two returns Issue Suppose you are predicting the mean annual return of an asset for some number of years. To simplify the discussion, let’s buy into the fantasy thatPORTFOLIO PROBE
Portfolio Probe - Portfolio fund management software. Generate random portfolios and perform portfolio optimization. Download free 30 daydemo.
CONSTRAINTS
The constraints that are implemented in Portfolio Probe are: Monetary Value Constraints Control the amount of money in the portfolio. more about the Monetary Value Constraints Turnover Constraint Control the amount of money (buys plus sells) that can be traded. more about Turnover Constraint Long-Only Constraint Disallow the possibility ofshort positions.
GENERATE RANDOM PORTFOLIOS Description Random portfolios are a sample from the population of portfolios that obey some given set of constraints. The constraints are the key ingredient. For example, we may want 5000 portfolios that obey the constraints: long-only 75 to 80 names in the portfolio no asset may contribute a fraction of more than 3% to the Continuereading →
THE TOP 7 PORTFOLIO OPTIMIZATION PROBLEMS Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements).If you are
WINSORIZATION
READ A TAB-SEPARATED FILE INTO R Task Create an R object that contains the data from a tab-separated file (which probably has the file extension "txt"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itxassetCountrySector
THE BASICS OF VALUE AT RISK AND EXPECTED SHORTFALL Value at Risk and Expected Shortfall are common risk measures. Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of Continue reading → IMPLIED ALPHA AND MINIMUM VARIANCE THE MYSTERY OF VOLATILITY ESTIMATES FROM DAILY VERSUSSEE MORE ONPORTFOLIOPROBE.COM
THE HALF VARIANCE APPROXIMATION FOR MEAN RETURNS What’s that thing about arithmetic and geometric returns and the variance? Previously An introduction to the difference between simple and log returns is: A tale of two returns Issue Suppose you are predicting the mean annual return of an asset for some number of years. To simplify the discussion, let’s buy into the fantasy that ABOUT | PORTFOLIO PROBE | GENERATE RANDOM PORTFOLIOS. FUND Portfolio Probe is a financial software program from Burns Statistics that can generate random portfolios and optimize portfolio trades. It runs in the R language (and also in S+). About Portfolio Probe Key Functionality Generate random portfolios (obeying a set of constraints) Perform portfolio optimization Perform utility-free portfolio optimization Random Portfolios Random portfolios areDEMO OR BUY
Demo A free 30-day demonstration copy is available if you qualify. This is a fully functional version -- the only limitation is the number of days the software will work. A key criterion for qualifying is that you are asking for it as a business and not as an individual. If you would like to Continue reading → APPLICATIONS OF RANDOM PORTFOLIOS There are (at least) three general areas for which random portfolios are useful: Performance Risk Quant Performance The concepts of "benchmark" and "peer group" have been extensively used in performance. Random portfolios are superior because: they are more specific to a particular fund they extract more information from the market The picture is an example THE BASICS OF VALUE AT RISK AND EXPECTED SHORTFALL Value at Risk and Expected Shortfall are common risk measures. Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of Continue reading → A PRACTICAL INTRODUCTION TO GARCH MODELING We look at volatility clustering, and some aspects of modeling it with a univariate GARCH(1,1) model. Volatility clustering Volatility clustering — the phenomenon of there being periods of relative calm and periods of high volatility — is a seemingly universal attribute of market data. There is no universally accepted explanation of it. AN EASY MISTAKE WITH RETURNS When aggregating over both time and assets, the order of aggregation matters. Task We have the weights for a portfolio and we want to use those and a matrix of returns over time to compute the (long-term) portfolio return. “A tale of two returns” tells us that aggregation over time is easiest to do in Continue reading → THE HALF VARIANCE APPROXIMATION FOR MEAN RETURNS What’s that thing about arithmetic and geometric returns and the variance? Previously An introduction to the difference between simple and log returns is: A tale of two returns Issue Suppose you are predicting the mean annual return of an asset for some number of years. To simplify the discussion, let’s buy into the fantasy that THE EFFECT OF BETA EQUAL 1 Investment Performance Guy had a post about beta equal 1. It made me wonder about the properties of portfolios with beta equal 1. When I looked, I got a bigger answer than I expected. Data I have some S&P 500 data lying about from the post ‘On “Stock correlation has been rising”‘. So laziness dictated Continue reading → A BRIEF HISTORY OF S&P 500 BETA Data The data are daily returns starting at the beginning of 2007. There are 477 stocks for which there is full and seemingly reliable data. Estimation The betas are all estimated on one year of data. The times that identify the betas mark the point at which the estimate would become available. So the betas Continue reading → VOLATILITY FROM DAILY OR MONTHLY: GARCH EVIDENCE Should you use daily or monthly returns to estimate volatility? Does garch explain why volatility estimated with daily data tends to be bigger than if it is estimated with monthly data? Previously There are a number of previous posts — with the variance compression tag — that discuss the phenomenon of volatility estimated with dailyPORTFOLIO PROBE
Portfolio Probe - Portfolio fund management software. Generate random portfolios and perform portfolio optimization. Download free 30 daydemo.
CONSTRAINTS
The constraints that are implemented in Portfolio Probe are: Monetary Value Constraints Control the amount of money in the portfolio. more about the Monetary Value Constraints Turnover Constraint Control the amount of money (buys plus sells) that can be traded. more about Turnover Constraint Long-Only Constraint Disallow the possibility ofshort positions.
GENERATE RANDOM PORTFOLIOS Description Random portfolios are a sample from the population of portfolios that obey some given set of constraints. The constraints are the key ingredient. For example, we may want 5000 portfolios that obey the constraints: long-only 75 to 80 names in the portfolio no asset may contribute a fraction of more than 3% to the Continuereading →
THE TOP 7 PORTFOLIO OPTIMIZATION PROBLEMS Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements).If you are
WINSORIZATION
READ A TAB-SEPARATED FILE INTO R Task Create an R object that contains the data from a tab-separated file (which probably has the file extension "txt"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itxassetCountrySector
THE BASICS OF VALUE AT RISK AND EXPECTED SHORTFALL Value at Risk and Expected Shortfall are common risk measures. Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of Continue reading → IMPLIED ALPHA AND MINIMUM VARIANCE THE MYSTERY OF VOLATILITY ESTIMATES FROM DAILY VERSUSSEE MORE ONPORTFOLIOPROBE.COM
THE HALF VARIANCE APPROXIMATION FOR MEAN RETURNS What’s that thing about arithmetic and geometric returns and the variance? Previously An introduction to the difference between simple and log returns is: A tale of two returns Issue Suppose you are predicting the mean annual return of an asset for some number of years. To simplify the discussion, let’s buy into the fantasy thatPORTFOLIO PROBE
Portfolio Probe - Portfolio fund management software. Generate random portfolios and perform portfolio optimization. Download free 30 daydemo.
CONSTRAINTS
The constraints that are implemented in Portfolio Probe are: Monetary Value Constraints Control the amount of money in the portfolio. more about the Monetary Value Constraints Turnover Constraint Control the amount of money (buys plus sells) that can be traded. more about Turnover Constraint Long-Only Constraint Disallow the possibility ofshort positions.
GENERATE RANDOM PORTFOLIOS Description Random portfolios are a sample from the population of portfolios that obey some given set of constraints. The constraints are the key ingredient. For example, we may want 5000 portfolios that obey the constraints: long-only 75 to 80 names in the portfolio no asset may contribute a fraction of more than 3% to the Continuereading →
THE TOP 7 PORTFOLIO OPTIMIZATION PROBLEMS Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements).If you are
WINSORIZATION
READ A TAB-SEPARATED FILE INTO R Task Create an R object that contains the data from a tab-separated file (which probably has the file extension "txt"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itxassetCountrySector
THE BASICS OF VALUE AT RISK AND EXPECTED SHORTFALL Value at Risk and Expected Shortfall are common risk measures. Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of Continue reading → IMPLIED ALPHA AND MINIMUM VARIANCE THE MYSTERY OF VOLATILITY ESTIMATES FROM DAILY VERSUSSEE MORE ONPORTFOLIOPROBE.COM
THE HALF VARIANCE APPROXIMATION FOR MEAN RETURNS What’s that thing about arithmetic and geometric returns and the variance? Previously An introduction to the difference between simple and log returns is: A tale of two returns Issue Suppose you are predicting the mean annual return of an asset for some number of years. To simplify the discussion, let’s buy into the fantasy that ABOUT | PORTFOLIO PROBE | GENERATE RANDOM PORTFOLIOS. FUND Portfolio Probe is a financial software program from Burns Statistics that can generate random portfolios and optimize portfolio trades. It runs in the R language (and also in S+). About Portfolio Probe Key Functionality Generate random portfolios (obeying a set of constraints) Perform portfolio optimization Perform utility-free portfolio optimization Random Portfolios Random portfolios areDEMO OR BUY
Demo A free 30-day demonstration copy is available if you qualify. This is a fully functional version -- the only limitation is the number of days the software will work. A key criterion for qualifying is that you are asking for it as a business and not as an individual. If you would like to Continue reading → APPLICATIONS OF RANDOM PORTFOLIOS There are (at least) three general areas for which random portfolios are useful: Performance Risk Quant Performance The concepts of "benchmark" and "peer group" have been extensively used in performance. Random portfolios are superior because: they are more specific to a particular fund they extract more information from the market The picture is an example THE BASICS OF VALUE AT RISK AND EXPECTED SHORTFALL Value at Risk and Expected Shortfall are common risk measures. Here is a quick explanation. Ingredients The first two ingredients are each a number: The time horizon — how many days do we look ahead? The probability level — how far in the tail are we looking? Ingredient number 3 is a prediction distribution of Continue reading → A PRACTICAL INTRODUCTION TO GARCH MODELING We look at volatility clustering, and some aspects of modeling it with a univariate GARCH(1,1) model. Volatility clustering Volatility clustering — the phenomenon of there being periods of relative calm and periods of high volatility — is a seemingly universal attribute of market data. There is no universally accepted explanation of it. AN EASY MISTAKE WITH RETURNS When aggregating over both time and assets, the order of aggregation matters. Task We have the weights for a portfolio and we want to use those and a matrix of returns over time to compute the (long-term) portfolio return. “A tale of two returns” tells us that aggregation over time is easiest to do in Continue reading → THE HALF VARIANCE APPROXIMATION FOR MEAN RETURNS What’s that thing about arithmetic and geometric returns and the variance? Previously An introduction to the difference between simple and log returns is: A tale of two returns Issue Suppose you are predicting the mean annual return of an asset for some number of years. To simplify the discussion, let’s buy into the fantasy that THE EFFECT OF BETA EQUAL 1 Investment Performance Guy had a post about beta equal 1. It made me wonder about the properties of portfolios with beta equal 1. When I looked, I got a bigger answer than I expected. Data I have some S&P 500 data lying about from the post ‘On “Stock correlation has been rising”‘. So laziness dictated Continue reading → A BRIEF HISTORY OF S&P 500 BETA Data The data are daily returns starting at the beginning of 2007. There are 477 stocks for which there is full and seemingly reliable data. Estimation The betas are all estimated on one year of data. The times that identify the betas mark the point at which the estimate would become available. So the betas Continue reading → VOLATILITY FROM DAILY OR MONTHLY: GARCH EVIDENCE Should you use daily or monthly returns to estimate volatility? Does garch explain why volatility estimated with daily data tends to be bigger than if it is estimated with monthly data? Previously There are a number of previous posts — with the variance compression tag — that discuss the phenomenon of volatility estimated with dailyPORTFOLIO PROBE
Portfolio Probe - Portfolio fund management software. Generate random portfolios and perform portfolio optimization. Download free 30 daydemo.
CONSTRAINTS
The constraints that are implemented in Portfolio Probe are: Monetary Value Constraints Control the amount of money in the portfolio. more about the Monetary Value Constraints Turnover Constraint Control the amount of money (buys plus sells) that can be traded. more about Turnover Constraint Long-Only Constraint Disallow the possibility ofshort positions.
GENERATE RANDOM PORTFOLIOS Description Random portfolios are a sample from the population of portfolios that obey some given set of constraints. The constraints are the key ingredient. For example, we may want 5000 portfolios that obey the constraints: long-only 75 to 80 names in the portfolio no asset may contribute a fraction of more than 3% to the Continuereading →
THE TOP 7 PORTFOLIO OPTIMIZATION PROBLEMS Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements).If you are
TURNOVER CONSTRAINT
Description Control the amount of money (buys plus sells) that can be traded. Implementation The turnover argument controls this in Portfolio Probe. Generally this is given a single number meaning the maximum turnover allowed. It is possible, though, to give a minimum turnover also. A turnover constraint is sometimes used as a simplisticway of
READ A TAB-SEPARATED FILE INTO R Task Create an R object that contains the data from a tab-separated file (which probably has the file extension "txt"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itxassetCountrySector
READ A COMMA-SEPARATED FILE INTO R Task Create an R object that contains the data from a comma-separated file (which probably has the file extension "csv"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itsuperbowl
THE MYSTERY OF VOLATILITY ESTIMATES FROM DAILY VERSUSSEE MORE ON PORTFOLIOPROBE.COMDAILY VOLATILITY FORMULASTOCKS WITH HIGH DAILYVOLATILITY
A BRIEF HISTORY OF S&P 500 BETA A TALE OF TWO RETURNSPORTFOLIO PROBE
Portfolio Probe - Portfolio fund management software. Generate random portfolios and perform portfolio optimization. Download free 30 daydemo.
CONSTRAINTS
The constraints that are implemented in Portfolio Probe are: Monetary Value Constraints Control the amount of money in the portfolio. more about the Monetary Value Constraints Turnover Constraint Control the amount of money (buys plus sells) that can be traded. more about Turnover Constraint Long-Only Constraint Disallow the possibility ofshort positions.
GENERATE RANDOM PORTFOLIOS Description Random portfolios are a sample from the population of portfolios that obey some given set of constraints. The constraints are the key ingredient. For example, we may want 5000 portfolios that obey the constraints: long-only 75 to 80 names in the portfolio no asset may contribute a fraction of more than 3% to the Continuereading →
THE TOP 7 PORTFOLIO OPTIMIZATION PROBLEMS Stumbling blocks on the trek from theory to practical optimization in fund management. Problem 1: portfolio optimization is too hard If you are using a spreadsheet, then this is indeed a problem. Spreadsheets are dangerous when given a complex task. Portfolio optimization qualifies as complex in this context (complex in data requirements).If you are
TURNOVER CONSTRAINT
Description Control the amount of money (buys plus sells) that can be traded. Implementation The turnover argument controls this in Portfolio Probe. Generally this is given a single number meaning the maximum turnover allowed. It is possible, though, to give a minimum turnover also. A turnover constraint is sometimes used as a simplisticway of
READ A TAB-SEPARATED FILE INTO R Task Create an R object that contains the data from a tab-separated file (which probably has the file extension "txt"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itxassetCountrySector
READ A COMMA-SEPARATED FILE INTO R Task Create an R object that contains the data from a comma-separated file (which probably has the file extension "csv"). We assume the data are rectangular -- that is, that we can think of it as being in rows and columns. Preparation None, other than starting R. Doing itsuperbowl
THE MYSTERY OF VOLATILITY ESTIMATES FROM DAILY VERSUSSEE MORE ON PORTFOLIOPROBE.COMDAILY VOLATILITY FORMULASTOCKS WITH HIGH DAILYVOLATILITY
A BRIEF HISTORY OF S&P 500 BETA A TALE OF TWO RETURNSKEY FEATURES
Random Portfolios Random portfolios are a sample from the population of portfolios that obey some given set of constraints. The constraints are the key ingredient. Random portfolios have a wide range of applications. more about Random Portfolios Portfolio Optimization Portfolio optimization is the process of using predictions about the asset universe to find a suitable ABOUT | PORTFOLIO PROBE | GENERATE RANDOM PORTFOLIOS. FUND Portfolio Probe is a financial software program from Burns Statistics that can generate random portfolios and optimize portfolio trades. It runs in the R language (and also in S+). About Portfolio Probe Key Functionality Generate random portfolios (obeying a set of constraints) Perform portfolio optimization Perform utility-free portfolio optimization Random Portfolios Random portfolios are BLOG | PORTFOLIO PROBE | GENERATE RANDOM PORTFOLIOS. FUND The ultimate aim of the Portfolio Probing blog is to help make fund management more effective, to make savings safer through better tools and better methods. Patrick Burns, the founder of Burns Statistics, offers a unique mix of experience in quantitative finance, statistics, computing and writing.DEMO OR BUY
Demo A free 30-day demonstration copy is available if you qualify. This is a fully functional version -- the only limitation is the number of days the software will work. A key criterion for qualifying is that you are asking for it as a business and not as an individual. If you would like to Continue reading → APPLICATIONS OF RANDOM PORTFOLIOS There are (at least) three general areas for which random portfolios are useful: Performance Risk Quant Performance The concepts of "benchmark" and "peer group" have been extensively used in performance. Random portfolios are superior because: they are more specific to a particular fund they extract more information from the market The picture is an exampleUSING R PACKAGES
Explanation Packages are a basic unit of functionality in R. A package typically contains a number of functions pertinent to a topic. Some packages contain data objects (in addition to or instead of functions). There is a distinction between packages that are installed on your machine and packages that are loaded into a particular R THE DISTRIBUTION OF FINANCIAL RETURNS MADE SIMPLE Why returns have a stable distribution. As “A tale of two returns” points out, the log return of a long period of time is the sum of the log returns of the shorter periods within the long period.. The log return over a year is the sum of the daily log returns in the year. The log return over an hour is the sum of the minute log returnswithin the hour.
THE EFFECT OF BETA EQUAL 1 Investment Performance Guy had a post about beta equal 1. It made me wonder about the properties of portfolios with beta equal 1. When I looked, I got a bigger answer than I expected. Data I have some S&P 500 data lying about from the post ‘On “Stock correlation has been rising”‘. So laziness dictated Continue reading → ALPHA DECAY IN PORTFOLIOS Regarding the “fine print” issue, it would be possible to do an experiment to see what looks appropriate. Basically just do a backtest (or preferably several runs with different starting portfolios) and compare the achieved expected return in the backtest and the expected return when there is no turnover constraint to the alpha distribution at each point in time. HISTORICAL VALUE AT RISK VERSUS HISTORICAL EXPECTED Comparing the behavior of the two on the S&P 500. Previously There have been a few posts about Value at Risk (VaR) and Expected Shortfall (ES) including an introduction to Value at Risk and Expected Shortfall. Data and model The underlying data are daily returns for the S&P 500 from 1950 to the present. The VaR and Continue reading →PORTFOLIO PROBE
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