[Returnanalytics-commits] r3074 - in pkg/PortfolioAnalytics/sandbox/symposium2013: . docs

Thu Sep 12 21:38:05 CEST 2013

Author: peter_carl
Date: 2013-09-12 21:38:04 +0200 (Thu, 12 Sep 2013)
New Revision: 3074

Added:
   pkg/PortfolioAnalytics/sandbox/symposium2013/docs/
   pkg/PortfolioAnalytics/sandbox/symposium2013/docs/symposium-slides-2013.Rmd
Log:
- initial project setup
- draft of slides without any code


Added: pkg/PortfolioAnalytics/sandbox/symposium2013/docs/symposium-slides-2013.Rmd
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--- pkg/PortfolioAnalytics/sandbox/symposium2013/docs/symposium-slides-2013.Rmd	                        (rev 0)
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+% Constructing Portfolios of Dynamic Strategies using Downside Risk Measures
+% Peter Carl, Hedge Fund Strategies, William Blair & Co.
+% November 11, 2013
+
+<!---
+# HOWTO
+To create PDF of slides:
+$ pandoc slides.Rmd -t beamer -o slides.pdf
+
+This is an R Markdown document. Markdown is a simple formatting syntax for authoring web pages (click the **MD** toolbar button for help on Markdown).  Or see: http://daringfireball.net/projects/markdown/syntax
+
+When you click the **Knit HTML** button a web page will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
+
+```{r}
+summary(cars)
+```
+
+You can also embed plots, for example:
+
+```{r fig.width=7, fig.height=6}
+plot(cars)
+```
+-->
+
+# Introduction
+
+- Discuss the challenges of constructing hedge fund portfolios
+- Offer a framework for considering strategic allocation of dynamic strategies
+- Show the relative performance of multiple objectives
+- Discuss extensions to the framework
+
+# Objectives
+
+- Visualization can help you build intuition about your objectives and constraints
+- Rebalancing periodically and examining out of sample performance will help refine objectives
+- Analytic solvers and parallel computation are valuable as problems get more complex
+
+# Process
+Insert process diagram here? Optional
+
+# Strategic allocation
+...broadly described as periodically reallocating the portfolio to achieve a long-term goal
+
+- Understanding the nature and sources of investment risk within the portfolio
+- Manage the resulting balance of risk and return of the portfolio
+- Apply within the cotext of the current economic and market situation
+- Think systematically about preferences and constraints
+
+# Selected strategies
+Daily data from the ...
+
+<!-- 
+Alternative 1: Use hedge fund styles
+
+Relative Value
+* Fixed Income Arb
+* Convertible Arb
+* Equity Market Neutral
+* Event Driven
+
+Directional
+* Equity Long/Short
+* Macro
+* CTA
+
+Alternative 2: Use existing rule-based strategies
+
+Basically 4 broad categories of strategy:
+- Momentum
+- Carry
+- Value
+- Volatility
+
+Across major asset classes:
+- Equities
+- Fixed Income
+- Commodities
+- FX
+
+Rules-based strategies designed to operate within a pre-defined set of rules and procedures. eliminates concerns about style drift and other issues about how various strategies perform in different states of the world. All of this data is available via Bloomberg (tickers are listed):
+Momentum - Equity - AIJPMEUU (USD)
+Momentum - FX - AIJPMF1U (USD)
+Momentum - Commodity (Energy) - AIJPMCEU (USD) - Morningstar instead?
+Momentum - Fixed Income (short dated) - AIJPMMUU
+Carry - FX (G10)- AIJPCF1U
+Carry - Fixed Income (2yr) - AIJPCB1U
+Carry - Commodity - AIJPCC1U
+Carry - All - GCSCS2UE
+Volatility - Equity (Imp vs. Realized) - AIJPSV1U
+Volatility - FX - CSVILEUS (long only)
+Volatility - Equities (CS RVIX) - CSEARVIX
+Value - Emerging Markets (bonds) - EMFXSEUS
+Value - Equities (CS HOLT RAII) - RAIIHRVU
+Value - Commodities (CS GAINS) - CSGADLSE
+
+Alternative 3: Create rule based strategies
+Use continuous series for commodities contracts and construct trend and counter trend strategies.  More limited sources of edge than in the prior alternative, but more leveragable work.  Could add hedging pressure as a third example.  
+-->
+
+# Performance of strategies
+Cumulative returns and drawdowns chart
+
+# Performance of strategies
+BarVaR charts
+
+# Performance of strategies
+Rolling 36-month Performance
+
+# Performance of strategies
+Pair of scatterplots since inception/last 36 months
+
+# Performance of strategies
+Comparison of distributions
+
+# Correlation of strategies
+Correlation charts, from inception and trailing 36-months
+
+# Portfolio issues
+Markowitz (1952) described an investor's objectives as:
+
+* maximizing some measure of gain while
+* minimizing some measure of risk
+
+Many approaches follow Markowitz by using mean return and standard devation of returns for "risk"
+
+# Portfolio issues
+Most investors would prefer:
+
+* to be approximately correct rather than precisely wrong
+* to define risk as potential loss rather than volatility
+* the flexibility to define any kind of objective and combine the constraints
+* a framework for considering different sets of portfolio constraints for comparison through time
+* to intuitively understand optimization through visualization
+
+# Portfolio issues
+Construct a portfolio that:
+
+* maximizes return,
+* with per-asset conditional constraints,
+* with a specific univariate risk limit,
+* while minimizing component risk concentration,
+* and limiting drawdowns to a threshhold value.
+
+Not a quadratic (or linear, or conical) problem any more.
+
+# Risk rather than volatility
+
+* Expected Tail Loss (ETL) is also called Conditional Value-at-Risk (CVaR) and Expected Shortfall (ES)
+* ETL is the mean expected loss when the loss exceeds the VaR
+* ETL has all the properties a risk measure should have to be coherent and is a convex function of the portfolio weights
+* Returns are skewed and/or kurtotic, so we use Cornish-Fisher (or "modified") estimates of ETL instead
+
+# Use Random Portfolios
+[Burns (2009)](http://www.portfolioprobe.com/blog/) describes Random Portfolios
+
+* From a portfolio seed, generate random pemutations of weights that meet your constraints on each asset
+* add more here
+
+Sampling can help provide insight into the goals and constraints of the optimization
+
+* Covers the 'edge case' (min/max) constraints well
+* Covers the 'interior' portfolios
+* Useful for finding the search space for an optimizer
+* Allows arbitrary number o fsamples
+* Allows massively parallel execution
+
+# Add general constraints
+Constraints specified for each asset in the portfolio:
+
+* Maximum position:
+* Minimum position:
+* Weights sum to 100%
+* Weights step size of 0.5%
+
+Other settings:
+
+* Confidence for VaR/ETL set at
+* Random portfolios with X000 permutations
+* Rebalancing quarterly (or monthly?)
+
+# Estimation
+
+* Optimizer chooses portfolios based on forward looking estimates of risk and return based on the portfolio moments
+* Estimates use the first four moments and co-moments
+* Historical sample moments work fine as predictors in normal market regimes, but poorly when the market regime shifts
+
+One of the largest challenges in optimization is improving the estimates of return and volatility
+
+# Forecasting
+## Returns
+
+## Volatility
+
+
+# Forecasting correlation
+
+# Equal-weight portfolio
+
+* Provides a benchmark to evaluate the performance of an optimized portfolio against
+* Each asset in the portfolio is purchased in the same quantity at the beginning of the period
+* The portfolio is rebalanced back to equal weight at the beginning of the next period
+* Implies no information about return or risk
+* Is the re-weighting adding or subtracting value?
+* Do we have a useful view of return and risk?
+
+# Sampled portfolios
+scatter chart with equal weight portfolio
+
+# Turnover from equal-weight
+scatter chart colored by degree of turnover
+
+# Multiple objectives
+
+Equal contribution to:
+
+* weight
+* variance
+* risk
+  
+Reward to risk:
+
+* mean-variance
+* mean-modified ETL
+
+Minimum:
+
+* variance
+* modified ETL
+
+# Equal contribution...
+...to Weight
+
+* Implies diversification but has nothing to say about returns or risk
+
+...to Variance
+
+* Allocates portfolio variance equally across the portfolio components
+
+...to Risk
+
+* Use (percentage) ETL contributions to directly diversify downside risk among components
+* Actually the minimum component risk contribution concentration portfolio
+
+# Reward to risk ratios...
+...mean/variance
+
+* A traditional reward to risk objective that penalizes upside volatility as risk
+
+...mean/modified ETL
+
+* A reward-to-downside-risk objective that uses higher moments to estimate the tail
+
+# Minimum...
+...variance
+
+* The portfolio with the forecasted variance of return
+
+...ETL
+
+* The portfolio with the minimum forecasted ETL
+
+Minimum risk portfolios generally suffer from the drawback of portfolio concentration.
+
+# Ex-ante results
+Unstacked bar chart comparing allocations across objectives
+
+# Ex-ante results
+scatter plot with objectives
+
+# Ex-ante vs. ex-post results
+scatter plot with both overlaid
+
+# Out-of-sample results
+timeseries charts for cumulative return and drawdown
+
+# Risk contribution
+stacked bar chart of risk contribution through time (ex ante and ex post)
+
+# Conclusions
+As a framework for strategic allocation:
+
+* Random Portfolios can help you build intuition about your objectives and constraints
+* Rebalancing periodically and examining out of sample performance can help you refine objectives
+* Differential Optimization and parallelization are valuable as objectives get more complicated
+
+# References
+Figure out bibtex links in markup
+
+# Appendix
+Slides after this point are not likely to be included in the final presentation
+
+# _PortfolioAnalytics_
+
+- Provides numerical solutions to portfolios with complex constraints and objectives
+- Unifies the interface across different numerical and closed-form optimizers
+- Implements a front-end to two analytical solvers: **Differential Evolution** and **Random Portfolios**
+- Preserves the flexibility to define any kind of objective and constraint
+- Work-in-progress, available on R-Forge in the _ReturnAnalytics_ project
+
+# Other packages
+
+* _PerformanceAnalytics_
+  * Library of econometric functions for performance and risk analysis of financial instruments and portfolios
+
+* _rugarch_ and _rmgarch_
+  * By Alexios Ghalanos
+  * The univariate and multivariate GARCH parts of the rgarch project on R-Forge
+
+* _xts_
+  * By Jeff Ryan and Jush Ulrich
+  * Time series package specifically for finance
+
+# Scratch
+Slides likely to be deleted after this point
\ No newline at end of file

