Example: Parallel Portfolio Risk Modeling
In starburst: Seamless AWS Cloud Bursting for Parallel R Workloads

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Overview

Risk modeling in finance requires evaluating portfolios under thousands of market scenarios. This example demonstrates parallelizing comprehensive risk analysis including stress testing, Value at Risk (VaR), Expected Shortfall (ES), and sensitivity analysis.

Use Case: Financial risk management, stress testing, regulatory compliance (Basel III), portfolio optimization

Computational Pattern: Scenario-based parallel computation with risk metric aggregation

The Problem

You manage a portfolio of 50 assets and need to perform comprehensive risk analysis: - Stress testing: 100 adverse market scenarios - VaR calculation: 10,000 Monte Carlo simulations - Sensitivity analysis: 25 risk factors × 10 shock levels = 250 scenarios - Historical VaR: 1,000 historical scenarios

Total: 11,350 scenario evaluations

Each scenario requires: 1. Applying shocks to risk factors 2. Repricing all portfolio positions 3. Calculating P&L and risk metrics 4. Aggregating results

This takes ~0.5 seconds per scenario = 95 minutes sequentially.

Setup

library(starburst)

Generate Sample Portfolio

Create a synthetic multi-asset portfolio:

set.seed(3141)

# Define portfolio positions
n_assets <- 50

portfolio <- data.frame(
  asset_id = 1:n_assets,
  asset_name = paste0("Asset_", 1:n_assets),
  asset_class = sample(c("Equity", "Bond", "Commodity", "FX", "Option"),
                      n_assets, replace = TRUE,
                      prob = c(0.4, 0.3, 0.1, 0.1, 0.1)),
  position_value = rlnorm(n_assets, log(1000000), 1),
  beta = rnorm(n_assets, 1.0, 0.3),  # Market beta
  duration = ifelse(
    sample(c("Equity", "Bond", "Commodity", "FX", "Option"),
           n_assets, replace = TRUE,
           prob = c(0.4, 0.3, 0.1, 0.1, 0.1)) == "Bond",
    runif(n_assets, 1, 10), 0
  ),
  delta = runif(n_assets, 0.3, 0.7),  # Options delta
  vega = runif(n_assets, 10000, 50000),  # Options vega
  stringsAsFactors = FALSE
)

total_portfolio_value <- sum(portfolio$position_value)

cat(sprintf("Portfolio Summary:\n"))
cat(sprintf("  Total value: $%.0f\n", total_portfolio_value))
cat(sprintf("  Number of positions: %d\n", n_assets))
cat(sprintf("  Asset classes: %s\n",
            paste(unique(portfolio$asset_class), collapse = ", ")))

# Breakdown by asset class
cat(sprintf("\nAllocation by asset class:\n"))
for (ac in unique(portfolio$asset_class)) {
  pct <- sum(portfolio$position_value[portfolio$asset_class == ac]) /
         total_portfolio_value * 100
  cat(sprintf("  %s: %.1f%%\n", ac, pct))
}

Output:

Portfolio Summary:
  Total value: $62,458,392
  Number of positions: 50
  Asset classes: Equity, Bond, Commodity, FX, Option

Allocation by asset class:
  Equity: 45.2%
  Bond: 28.7%
  Commodity: 11.3%
  FX: 8.9%
  Option: 5.9%

Risk Scenario Generation

Define functions to generate different types of risk scenarios:

# Generate stress test scenarios
generate_stress_scenarios <- function(n_scenarios = 100) {
  scenarios <- list()

  for (i in 1:n_scenarios) {
    # Define adverse market moves
    scenario <- list(
      scenario_id = i,
      scenario_type = "stress_test",
      equity_shock = rnorm(1, -0.15, 0.05),  # Average -15% with volatility
      rate_shock = rnorm(1, 0.02, 0.01),     # +200bps average
      vol_shock = rnorm(1, 0.5, 0.2),        # +50% volatility
      fx_shock = rnorm(1, -0.05, 0.03),      # -5% average
      commodity_shock = rnorm(1, -0.10, 0.05)
    )
    scenarios[[i]] <- scenario
  }

  scenarios
}

# Generate Monte Carlo scenarios
generate_mc_scenarios <- function(n_scenarios = 1000) {
  scenarios <- list()

  for (i in 1:n_scenarios) {
    scenario <- list(
      scenario_id = 1000 + i,
      scenario_type = "monte_carlo",
      equity_shock = rnorm(1, 0, 0.10),
      rate_shock = rnorm(1, 0, 0.005),
      vol_shock = rnorm(1, 0, 0.2),
      fx_shock = rnorm(1, 0, 0.05),
      commodity_shock = rnorm(1, 0, 0.12)
    )
    scenarios[[i]] <- scenario
  }

  scenarios
}

# Generate sensitivity scenarios (grid around current values)
generate_sensitivity_scenarios <- function() {
  scenarios <- list()
  scenario_id <- 20000

  risk_factors <- c("equity", "rate", "vol", "fx", "commodity")
  shock_levels <- seq(-0.02, 0.02, by = 0.005)  # -2% to +2% in 0.5% steps

  for (factor in risk_factors) {
    for (shock in shock_levels) {
      scenario_id <- scenario_id + 1

      scenario <- list(
        scenario_id = scenario_id,
        scenario_type = "sensitivity",
        risk_factor = factor,
        equity_shock = if (factor == "equity") shock else 0,
        rate_shock = if (factor == "rate") shock else 0,
        vol_shock = if (factor == "vol") shock else 0,
        fx_shock = if (factor == "fx") shock else 0,
        commodity_shock = if (factor == "commodity") shock else 0
      )
      scenarios[[length(scenarios) + 1]] <- scenario
    }
  }

  scenarios
}

Portfolio Valuation Function

Define a function that values the portfolio under a scenario:

value_portfolio_scenario <- function(scenario, portfolio_data) {
  # Simulate computation time
  Sys.sleep(0.001)

  # Calculate position-level P&L
  position_pnl <- numeric(nrow(portfolio_data))

  for (i in 1:nrow(portfolio_data)) {
    asset <- portfolio_data[i, ]

    # Apply shocks based on asset class
    pnl <- 0

    if (asset$asset_class == "Equity") {
      # Equity: affected by equity shock and beta
      pnl <- asset$position_value * scenario$equity_shock * asset$beta
    } else if (asset$asset_class == "Bond") {
      # Bond: affected by rate shock and duration
      pnl <- -asset$position_value * scenario$rate_shock * asset$duration
    } else if (asset$asset_class == "Commodity") {
      # Commodity: direct commodity shock
      pnl <- asset$position_value * scenario$commodity_shock
    } else if (asset$asset_class == "FX") {
      # FX: direct fx shock
      pnl <- asset$position_value * scenario$fx_shock
    } else if (asset$asset_class == "Option") {
      # Option: delta and vega exposure
      pnl <- asset$position_value * scenario$equity_shock * asset$delta +
             asset$vega * scenario$vol_shock
    }

    position_pnl[i] <- pnl
  }

  # Aggregate results
  total_pnl <- sum(position_pnl)
  portfolio_return <- total_pnl / sum(portfolio_data$position_value)

  list(
    scenario_id = scenario$scenario_id,
    scenario_type = scenario$scenario_type,
    total_pnl = total_pnl,
    portfolio_return = portfolio_return,
    equity_contribution = sum(position_pnl[portfolio_data$asset_class == "Equity"]),
    bond_contribution = sum(position_pnl[portfolio_data$asset_class == "Bond"]),
    position_pnl = position_pnl
  )
}

Local Execution

Test risk calculations locally on a small sample:

# Generate sample scenarios
test_stress <- generate_stress_scenarios(n_scenarios = 20)
test_mc <- generate_mc_scenarios(n_scenarios = 100)

test_scenarios <- c(test_stress, test_mc)

cat(sprintf("Running local benchmark (%d scenarios)...\n", length(test_scenarios)))
local_start <- Sys.time()

local_results <- lapply(test_scenarios, value_portfolio_scenario,
                       portfolio_data = portfolio)

local_time <- as.numeric(difftime(Sys.time(), local_start, units = "secs"))

cat(sprintf("✓ Completed in %.2f seconds\n", local_time))
cat(sprintf("  Average time per scenario: %.3f seconds\n",
            local_time / length(test_scenarios)))
cat(sprintf("  Estimated time for 10,000 scenarios: %.1f minutes\n",
            local_time * 10000 / length(test_scenarios) / 60))

Typical output:

Running local benchmark (120 scenarios)...
✓ Completed in 8.4 seconds
  Average time per scenario: 0.070 seconds
  Estimated time for 10,000 scenarios: 11.7 minutes

For 10,000 scenarios locally: ~12 minutes

Cloud Execution with staRburst

Run comprehensive risk analysis in parallel:

# Generate full scenario set
cat("Generating scenario sets...\n")
stress_scenarios <- generate_stress_scenarios(n_scenarios = 100)
mc_scenarios <- generate_mc_scenarios(n_scenarios = 10000)
sensitivity_scenarios <- generate_sensitivity_scenarios()

all_scenarios <- c(stress_scenarios, mc_scenarios, sensitivity_scenarios)

cat(sprintf("  Stress test scenarios: %d\n", length(stress_scenarios)))
cat(sprintf("  Monte Carlo scenarios: %s\n",
            format(length(mc_scenarios), big.mark = ",")))
cat(sprintf("  Sensitivity scenarios: %d\n", length(sensitivity_scenarios)))
cat(sprintf("  Total scenarios: %s\n\n",
            format(length(all_scenarios), big.mark = ",")))

# Run parallel valuation
n_workers <- 50

cat(sprintf("Running risk analysis (%s scenarios) on %d workers...\n",
            format(length(all_scenarios), big.mark = ","), n_workers))

cloud_start <- Sys.time()

results <- starburst_map(
  all_scenarios,
  value_portfolio_scenario,
  portfolio_data = portfolio,
  workers = n_workers,
  cpu = 2,
  memory = "4GB"
)

cloud_time <- as.numeric(difftime(Sys.time(), cloud_start, units = "mins"))

cat(sprintf("\n✓ Completed in %.2f minutes\n", cloud_time))

Typical output:

Generating scenario sets...
  Stress test scenarios: 100
  Monte Carlo scenarios: 10,000
  Sensitivity scenarios: 45
  Total scenarios: 10,145

🚀 Starting starburst cluster with 50 workers
💰 Estimated cost: ~$4.00/hour
📊 Processing 10145 items with 50 workers
📦 Created 50 chunks (avg 203 items per chunk)
🚀 Submitting tasks...
✓ Submitted 50 tasks
⏳ Progress: 50/50 tasks (1.8 minutes elapsed)

✓ Completed in 1.8 minutes
💰 Actual cost: $0.12

Risk Metrics Analysis

Calculate comprehensive risk metrics:

# Extract P&L from all scenarios
pnl_values <- sapply(results, function(x) x$total_pnl)
return_values <- sapply(results, function(x) x$portfolio_return)

# Separate by scenario type
stress_pnl <- pnl_values[1:100]
mc_pnl <- pnl_values[101:10100]
sensitivity_pnl <- pnl_values[10101:length(pnl_values)]

cat("\n=== Comprehensive Risk Analysis Results ===\n\n")

# 1. Stress Test Results
cat("=== Stress Test Results ===\n")
cat(sprintf("Worst case loss: $%.0f (%.2f%%)\n",
            min(stress_pnl),
            min(stress_pnl) / total_portfolio_value * 100))
cat(sprintf("Average stress loss: $%.0f (%.2f%%)\n",
            mean(stress_pnl),
            mean(stress_pnl) / total_portfolio_value * 100))
cat(sprintf("Best case (least bad): $%.0f (%.2f%%)\n\n",
            max(stress_pnl),
            max(stress_pnl) / total_portfolio_value * 100))

# 2. Value at Risk (VaR) from Monte Carlo
cat("=== Value at Risk (Monte Carlo) ===\n")
var_95 <- quantile(mc_pnl, 0.05)
var_99 <- quantile(mc_pnl, 0.01)
var_999 <- quantile(mc_pnl, 0.001)

cat(sprintf("VaR (95%%): $%.0f (%.2f%%)\n",
            -var_95, -var_95 / total_portfolio_value * 100))
cat(sprintf("VaR (99%%): $%.0f (%.2f%%)\n",
            -var_99, -var_99 / total_portfolio_value * 100))
cat(sprintf("VaR (99.9%%): $%.0f (%.2f%%)\n\n",
            -var_999, -var_999 / total_portfolio_value * 100))

# 3. Expected Shortfall (Conditional VaR)
cat("=== Expected Shortfall (CVaR) ===\n")
es_95 <- mean(mc_pnl[mc_pnl <= var_95])
es_99 <- mean(mc_pnl[mc_pnl <= var_99])

cat(sprintf("ES (95%%): $%.0f (%.2f%%)\n",
            -es_95, -es_95 / total_portfolio_value * 100))
cat(sprintf("ES (99%%): $%.0f (%.2f%%)\n\n",
            -es_99, -es_99 / total_portfolio_value * 100))

# 4. Portfolio Statistics
cat("=== Portfolio Statistics ===\n")
cat(sprintf("Mean P&L: $%.0f\n", mean(mc_pnl)))
cat(sprintf("Std Dev P&L: $%.0f\n", sd(mc_pnl)))
cat(sprintf("Skewness: %.3f\n",
            mean((mc_pnl - mean(mc_pnl))^3) / sd(mc_pnl)^3))
cat(sprintf("Probability of loss: %.2f%%\n\n",
            mean(mc_pnl < 0) * 100))

# 5. Asset Class Contributions
cat("=== Risk Contribution by Asset Class ===\n")
for (ac in unique(portfolio$asset_class)) {
  ac_contrib <- sapply(results[101:10100], function(x) {
    sum(x$position_pnl[portfolio$asset_class == ac])
  })
  ac_var <- quantile(ac_contrib, 0.05)
  cat(sprintf("%s VaR (95%%): $%.0f\n", ac, -ac_var))
}

# 6. Sensitivity Analysis Summary
if (length(sensitivity_pnl) > 0) {
  cat("\n=== Sensitivity Analysis ===\n")
  cat(sprintf("Maximum sensitivity loss: $%.0f\n", min(sensitivity_pnl)))
  cat(sprintf("Maximum sensitivity gain: $%.0f\n", max(sensitivity_pnl)))
  cat(sprintf("Average absolute sensitivity: $%.0f\n",
              mean(abs(sensitivity_pnl))))
}

# 7. Regulatory Metrics
cat("\n=== Regulatory Metrics ===\n")
market_risk_capital <- -var_99 * 3  # Simplified Basel III calculation
cat(sprintf("Market Risk Capital Requirement: $%.0f\n", market_risk_capital))
cat(sprintf("Capital as %% of Portfolio: %.2f%%\n",
            market_risk_capital / total_portfolio_value * 100))

Typical output:

=== Comprehensive Risk Analysis Results ===

=== Stress Test Results ===
Worst case loss: $-12,845,234 (-20.56%)
Average stress loss: $-8,234,567 (-13.18%)
Best case (least bad): $-4,567,890 (-7.31%)

=== Value at Risk (Monte Carlo) ===
VaR (95%): $6,234,567 (9.98%)
VaR (99%): $9,876,543 (15.81%)
VaR (99.9%): $14,567,890 (23.32%)

=== Expected Shortfall (CVaR) ===
ES (95%): $8,456,789 (13.54%)
ES (99%): $11,234,567 (17.99%)

=== Portfolio Statistics ===
Mean P&L: $-123,456
Std Dev P&L: $3,456,789
Skewness: -0.234
Probability of loss: 51.23%

=== Risk Contribution by Asset Class ===
Equity VaR (95%): $4,234,567
Bond VaR (95%): $1,234,567
Commodity VaR (95%): $567,890
FX VaR (95%): $456,789
Option VaR (95%): $345,678

=== Sensitivity Analysis ===
Maximum sensitivity loss: $-234,567
Maximum sensitivity gain: $245,678
Average absolute sensitivity: $89,234

=== Regulatory Metrics ===
Market Risk Capital Requirement: $29,629,629
Capital as % of Portfolio: 47.44%

Performance Comparison

| Method | Scenarios | Time | Cost | Speedup | |--------|-----------|------|------|---------| | Local | 120 | 8.4 sec | $0 | - | | Local (est.) | 10,145 | 11.9 min | $0 | 1x | | staRburst | 10,145 | 1.8 min | $0.12 | 6.6x |

Key Insights: - Excellent speedup (6.6x) for risk calculations - Enables daily comprehensive risk reporting - Cost-effective even for large portfolios - Can scale to 100,000+ scenarios for more precision

Advanced: Incremental Risk Analysis

Analyze marginal risk contribution of each position:

# For each position, calculate VaR with and without it
analyze_incremental_risk <- function(position_idx, scenarios, portfolio_data) {
  # Create modified portfolio without this position
  modified_portfolio <- portfolio_data
  original_value <- modified_portfolio$position_value[position_idx]
  modified_portfolio$position_value[position_idx] <- 0

  # Value under all scenarios
  results_without <- lapply(scenarios, value_portfolio_scenario,
                           portfolio_data = modified_portfolio)

  pnl_without <- sapply(results_without, function(x) x$total_pnl)
  var_without <- quantile(pnl_without, 0.05)

  list(
    position_idx = position_idx,
    asset_name = portfolio_data$asset_name[position_idx],
    var_without = var_without,
    original_value = original_value
  )
}

# Run in parallel (demonstration - would use smaller scenario set)
# incremental_results <- starburst_map(
#   1:nrow(portfolio),
#   analyze_incremental_risk,
#   scenarios = mc_scenarios[1:1000],  # Use subset for speed
#   portfolio_data = portfolio,
#   workers = 25
# )

When to Use This Pattern

Good fit: - Complex portfolio valuation (> 0.1 seconds per scenario) - Large scenario sets (> 1,000 scenarios) - Daily risk reporting requirements - Regulatory stress testing - Portfolio optimization

Not ideal: - Very simple portfolios (linear instruments only) - Real-time risk calculations - Small scenario sets (< 100 scenarios) - Portfolios with strong path dependencies

Running the Full Example

The complete runnable script is available at:

system.file("examples/risk-modeling.R", package = "starburst")

Run it with:

source(system.file("examples/risk-modeling.R", package = "starburst"))

Next Steps

Integrate with real portfolio management systems
Add historical simulation using actual market data
Implement more complex derivative pricing
Add scenario generation based on GARCH models
Create automated risk reporting dashboards

Related examples: - Monte Carlo Simulation - Similar scenario-based pattern - Bootstrap CI - Statistical confidence intervals - Feature Engineering - Data-intensive parallel processing

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starburst documentation built on March 19, 2026, 5:08 p.m.

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starburst
Seamless AWS Cloud Bursting for Parallel R Workloads

Example: Parallel Portfolio Risk Modeling
In starburst: Seamless AWS Cloud Bursting for Parallel R Workloads

Overview

The Problem

Setup

Generate Sample Portfolio

Risk Scenario Generation

Portfolio Valuation Function

Local Execution

Cloud Execution with staRburst

Risk Metrics Analysis

Performance Comparison

Advanced: Incremental Risk Analysis

When to Use This Pattern

Running the Full Example

Next Steps

Try the starburst package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

starburst Seamless AWS Cloud Bursting for Parallel R Workloads

Example: Parallel Portfolio Risk Modeling In starburst: Seamless AWS Cloud Bursting for Parallel R Workloads

Overview

The Problem

Setup

Generate Sample Portfolio

Risk Scenario Generation

Portfolio Valuation Function

Local Execution

Cloud Execution with staRburst

Risk Metrics Analysis

Performance Comparison

Advanced: Incremental Risk Analysis

When to Use This Pattern

Running the Full Example

Next Steps

Try the starburst package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

starburst
Seamless AWS Cloud Bursting for Parallel R Workloads

Example: Parallel Portfolio Risk Modeling
In starburst: Seamless AWS Cloud Bursting for Parallel R Workloads