## Backtesting Asset Allocation portfolios

In the last post, Portfolio Optimization: Specify constraints with GNU MathProg language, Paolo and MC raised a question: “How would you construct an equal risk contribution portfolio?” Unfortunately, this problem cannot be expressed as a Linear or Quadratic Programming problem.

The outline for this post:

- I will show how Equal Risk Contribution portfolio can be formulated and solved using a non-linear solver.
- I will backtest Equal Risk Contribution portfolio and other Asset Allocation portfolios based on various risk measures I described in the Asset Allocation series of post.

Pat Burns wrote an excellent post: Unproxying weight constraints that explains Risk Contribution – partition the variance of a portfolio into pieces attributed to each asset. The Equal Risk Contribution portfolio is a portfolio that splits total portfolio risk equally among its assets. (The concept is similar to 1/N portfolio – a portfolio that splits total portfolio weight equally among its assets.)

Risk Contributions (risk fractions) can be expressed in terms of portfolio weights (w) and covariance matrix (V):

Our objective is to find portfolio weights (w) such that Risk Contributions are equal for all assets. This objective function can be easily coded in R:

risk.contribution = w * (cov %*% w) sum( abs(risk.contribution - mean(risk.contribution)) )

I recommend following references for a detailed discussion of Risk Contributions:

- Equally-weighted risk contributions: a new method to build risk balanced diversified portfolios by S. Maillard, T. Roncalli and J. Teiletche (2008)
- On the property of equally-weighted risk contributions portfolios by S. Maillard, T. Roncalli and J. Teiletche (2008)
- Analytical Solution for the Equal-Risk-Contribution Portfolio
- Matlab code for Equal Risk Contribution Portfolio by Farid Moussaoui

I will use a Nonlinear programming solver, Rdonlp2, which is based on donlp2 routine developed and copyright by Prof. Dr. Peter Spellucci to solve for Equal Risk Contribution portfolio. [Please note that following code might not properly execute on your computer because Rdonlp2 package is required and not available on CRAN]

#-------------------------------------------------------------------------- # Equal Risk Contribution portfolio #-------------------------------------------------------------------------- ia = aa.test.create.ia() n = ia$n # 0 <= x.i <= 1 constraints = new.constraints(n, lb = 0, ub = 1) # SUM x.i = 1 constraints = add.constraints(rep(1, n), 1, type = '=', constraints) # find Equal Risk Contribution portfolio w = find.erc.portfolio(ia, constraints) # compute Risk Contributions risk.contributions = portfolio.risk.contribution(w, ia)

Next, I want to expand on the Backtesting Minimum Variance portfolios post to include Equal Risk Contribution portfolio and and other Asset Allocation portfolios based on various risk measures I described in the Asset Allocation series of post.

############################################################################### # Load Systematic Investor Toolbox (SIT) # http://systematicinvestor.wordpress.com/systematic-investor-toolbox/ ############################################################################### con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb')) source(con) close(con) #***************************************************************** # Load historical data #****************************************************************** load.packages('quantmod,quadprog,corpcor,lpSolve') tickers = spl('SPY,QQQ,EEM,IWM,EFA,TLT,IYR,GLD') data <- new.env() getSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T) for(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T) bt.prep(data, align='remove.na', dates='1990::2011') #***************************************************************** # Code Strategies #****************************************************************** prices = data$prices n = ncol(prices) # find week ends period.ends = endpoints(prices, 'weeks') period.ends = period.ends[period.ends > 0] #***************************************************************** # Create Constraints #***************************************************************** constraints = new.constraints(n, lb = 0, ub = 1) # SUM x.i = 1 constraints = add.constraints(rep(1, n), 1, type = '=', constraints) #***************************************************************** # Create Portfolios #***************************************************************** ret = prices / mlag(prices) - 1 start.i = which(period.ends >= (63 + 1))[1] weight = NA * prices[period.ends,] weights = list() # Equal Weight 1/N Benchmark weights$equal.weight = weight weights$equal.weight[] = ntop(prices[period.ends,], n) weights$equal.weight[1:start.i,] = NA weights$min.var = weight weights$min.maxloss = weight weights$min.mad = weight weights$min.cvar = weight weights$min.cdar = weight weights$min.cor.insteadof.cov = weight weights$min.mad.downside = weight weights$min.risk.downside = weight # following optimizations use a non-linear solver weights$erc = weight weights$min.avgcor = weight risk.contributions = list() risk.contributions$erc = weight # construct portfolios for( j in start.i:len(period.ends) ) { i = period.ends[j] # one quarter = 63 days hist = ret[ (i- 63 +1):i, ] # create historical input assumptions ia = create.historical.ia(hist, 252) s0 = apply(coredata(hist),2,sd) ia$correlation = cor(coredata(hist), use='complete.obs',method='pearson') ia$cov = ia$correlation * (s0 %*% t(s0)) # construct portfolios based on various risk measures weights$min.var[j,] = min.risk.portfolio(ia, constraints) weights$min.maxloss[j,] = min.maxloss.portfolio(ia, constraints) weights$min.mad[j,] = min.mad.portfolio(ia, constraints) weights$min.cvar[j,] = min.cvar.portfolio(ia, constraints) weights$min.cdar[j,] = min.cdar.portfolio(ia, constraints) weights$min.cor.insteadof.cov[j,] = min.cor.insteadof.cov.portfolio(ia, constraints) weights$min.mad.downside[j,] = min.mad.downside.portfolio(ia, constraints) weights$min.risk.downside[j,] = min.risk.downside.portfolio(ia, constraints) # following optimizations use a non-linear solver constraints$x0 = weights$erc[(j-1),] weights$erc[j,] = find.erc.portfolio(ia, constraints) constraints$x0 = weights$min.avgcor[(j-1),] weights$min.avgcor[j,] = min.avgcor.portfolio(ia, constraints) risk.contributions$erc[j,] = portfolio.risk.contribution(weights$erc[j,], ia) }

Next let’s backtest these portfolios and create summary statistics:

#***************************************************************** # Create strategies #****************************************************************** models = list() for(i in names(weights)) { data$weight[] = NA data$weight[period.ends,] = weights[[i]] models[[i]] = bt.run.share(data, clean.signal = F) } #***************************************************************** # Create Report #****************************************************************** models = rev(models) # Plot perfromance plotbt(models, plotX = T, log = 'y', LeftMargin = 3) mtext('Cumulative Performance', side = 2, line = 1) # Plot Strategy Statistics Side by Side plotbt.strategy.sidebyside(models) # Plot transition maps layout(1:len(models)) for(m in names(models)) { plotbt.transition.map(models[[m]]$weight, name=m) legend('topright', legend = m, bty = 'n') } # Plot risk contributions layout(1:len(risk.contributions)) for(m in names(risk.contributions)) { plotbt.transition.map(risk.contributions[[m]], name=paste('Risk Contributions',m)) legend('topright', legend = m, bty = 'n') } # Compute portfolio concentration and turnover stats based on the # On the property of equally-weighted risk contributions portfolios by S. Maillard, # T. Roncalli and J. Teiletche (2008), page 22 # http://www.thierry-roncalli.com/download/erc.pdf out = compute.stats( rev(weights), list(Gini=function(w) mean(portfolio.concentration.gini.coefficient(w), na.rm=T), Herfindahl=function(w) mean(portfolio.concentration.herfindahl.index(w), na.rm=T), Turnover=function(w) 52 * mean(portfolio.turnover(w), na.rm=T) ) ) out[] = plota.format(100 * out, 1, '', '%') plot.table(t(out))

The minimum variance (min.risk) portfolio performed very well during that period with 10.5% CAGR and 14% maximum drawdown. The Equal Risk Contribution portfolio (find.erc) also fares well with 10.5% CAGR and 19% maximum drawdown. The 1/N portfolio (equal.weight) is the worst strategy with 7.8% CAGR and 45% maximum drawdown.

One interesting way to modify this strategy is to consider different measures of volatility used to construct a covariance matrix. For example TTR package provides functions for the Garman Klass – Yang Zhang and the Yang Zhang volatility estimation methods. For more details, please have a look at the Different Volatility Measures Effect on Daily MR by Quantum Financier post.

Inspired by the I Dream of Gini by David Varadi, I will show how to create Gini efficient frontier in the next post.

To view the complete source code for this example, please have a look at the bt.aa.test() function in bt.test.r at github.

Did you have a look at nloptr (a wrapper to NLopt) for constrained non-linear optimization? And do you know how it compares to Rdonlp2?

Awesome work.

Nicely done. In the case of the ERC, the risk contributions remain equal, the weights are constrained to being greater than 0 and sum to 1 (essentially) and the expected return on the portfolio is whatever it is. Can you suggest where changes would need to be made to allow for the risk contributions to remain equal, the weights would be whatever they would be within a wider range (e.g. -1 to +3, i.e. allow shorting and/or leverage) but the expected return on the portfolio is now constrained? It is easy enough to see where the weight constraints would be changed in the above code. But I have yet to dig through your routines to see where expected value is/could be calculated and added to the mix. Also, my intuition tells me that there might not be a single resulting portfolio, but instead a region or set of portfolios that could satisfy the constraints.

MC, if you allow negative weights (i.e. shorting), risk contribution for the short positions will be negative. So if you want to create an equal risk contributions portfolio, all weights will be positive.

Alternatively you can solve for portfolio that has

absolute valueof all risk contributions equal.To do that, you need to modify the objective function from

to

Ok, got that – thanks. Am I correct though in thinking that targeting an expected return level (while allowing for 0<= weights <= some_positive_number) is a non-trivial modification of your routines?