815 search results for "maps"

Minimizing Downside Risk

November 1, 2011
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Minimizing Downside Risk

In the Maximum Loss and Mean-Absolute Deviation risk measures, and Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) posts I started the discussion about alternative risk measures we can use to construct efficient frontier. Another alternative risk measure I want to discuss is Downside Risk. In the traditional mean-variance optimization both returns above and

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Halloween 2011 count

October 31, 2011
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Halloween 2011 count

We don’t get many kids seeking candy at our house. I’m not sure if there just aren’t many kids in the neighborhood, or if it’s our location (next to the pond, with a big gap before the next house). I decided to keep track. As usual, we bought a huge bag of candy, and we

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Japan Quake Map

October 31, 2011
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Japan Quake Map

Japan Quake Map with R, ggplot2, and FFmpeg   1 Introduction As a follow-up to ‘Analysis of Japanes

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The Most Diversified or The Least Correlated Efficient Frontier

October 27, 2011
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The Most Diversified or The Least Correlated Efficient Frontier

The “Minimum Correlation Algorithm” is a term I stumbled at the CSS Analytics blog. This is an Interesting Risk Measure that in my interpretation means: minimizing Average Portfolio Correlation with each Asset Class for a given level of return. One might try to use Correlation instead of Covariance matrix in mean-variance optimization, but this approach,

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Controlling multiple risk measures during construction of efficient frontier

October 26, 2011
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Controlling multiple risk measures during construction of efficient frontier

In the last few posts I introduced Maximum Loss, Mean-Absolute Deviation, and Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) risk measures. These risk measures can be formulated as linear constraints and thus can be combined with each other to control multiple risk measures during construction of efficient frontier. Let’s examine efficient frontiers computed

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Machine Learning Ex 5.2 – Regularized Logistic Regression

October 25, 2011
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Machine Learning Ex 5.2 – Regularized Logistic Regression

Now we move on to the second part of the Exercise 5.2, which requires to implement regularized logistic regression using Newton's Method. Plot the data:

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Mapping Hotspots with R: The GAM

October 25, 2011
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Mapping Hotspots with R: The GAM

I've been getting a lot of questions about the method used to map the hotspots in the seasonal drunk-driving risk maps.  It uses the GAM (Geographical Analysis Machine), a way of detecting spatial clusters from two data inputs: the data of interes...

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Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) risk measures

October 25, 2011
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Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) risk measures

In the Maximum Loss and Mean-Absolute Deviation risk measures post I started the discussion about alternative risk measures we can use to construct efficient frontier. Another alternative risk measures I want to discuss are Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR). I will use methods presented in Comparative Analysis of Linear Portfolio Rebalancing

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Spatial correlation in designed experiments

October 20, 2011
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Spatial correlation in designed experiments

Last Wednesday I had a meeting with the folks of the New Zealand Drylands Forest Initiative in Blenheim. In addition to sitting in a conference room and having nice sandwiches we went to visit one of our progeny trials at … Continue reading →

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Minimum Investment and Number of Assets Portfolio Cardinality Constraints

October 19, 2011
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Minimum Investment and Number of Assets Portfolio Cardinality Constraints

The Minimum Investment and Number of Assets Portfolio Cardinality Constraints are practical constraints that are not easily incorporated in the standard mean-variance optimization framework. To help us impose these real life constraints, I will introduce extra binary variables and will use mixed binary linear and quadratic programming solvers. Let’s continue with our discussion from Introduction

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