diff --git a/quantitative_finance/risk_metrics.r b/quantitative_finance/risk_metrics.r
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+# Value at Risk (VaR) and Expected Shortfall (ES) Calculator
+# Implements multiple VaR calculation methods and Expected Shortfall
+# Features: Historical, Parametric, and Monte Carlo VaR/ES calculations
+
+library(R6)
+
+#' RiskMetrics Class
+#' @description R6 class for calculating Value at Risk and Expected Shortfall
+#' @details Implements multiple methods for VaR and ES calculation:
+#' - Historical simulation (non-parametric)
+#' - Parametric (variance-covariance)
+#' - Monte Carlo simulation
+#' Time complexity varies by method, documented per method
+RiskMetrics <- R6Class(
+  "RiskMetrics",
+  
+  public = list(
+    #' @description Initialize risk calculator
+    #' @param returns Historical returns data
+    #' @param confidence_level Confidence level for VaR/ES (default 0.95)
+    #' @param time_horizon Time horizon in days (default 1)
+    initialize = function(returns = NULL, confidence_level = 0.95, time_horizon = 1) {
+      private$validate_parameters(confidence_level, time_horizon)
+      
+      self$returns <- returns
+      self$confidence_level <- confidence_level
+      self$time_horizon <- time_horizon
+      
+      if (!is.null(returns)) {
+        private$fit_distribution()
+      }
+    },
+    
+    #' @description Calculate Historical VaR
+    #' @param portfolio_value Current portfolio value
+    #' @param method Calculation method ('historical', 'parametric', or 'monte_carlo')
+    #' @param n_simulations Number of simulations for Monte Carlo method
+    calculate_var = function(portfolio_value, method = "historical", n_simulations = 10000) {
+      if (is.null(self$returns)) {
+        stop("No returns data available. Please initialize with returns data.")
+      }
+      
+      method <- match.arg(method, c("historical", "parametric", "monte_carlo"))
+      
+      var_value <- switch(method,
+        "historical" = private$calculate_historical_var(portfolio_value),
+        "parametric" = private$calculate_parametric_var(portfolio_value),
+        "monte_carlo" = private$calculate_monte_carlo_var(portfolio_value, n_simulations)
+      )
+      
+      # Scale VaR to time horizon
+      var_value * sqrt(self$time_horizon)
+    },
+    
+    #' @description Calculate Expected Shortfall (Conditional VaR)
+    #' @param portfolio_value Current portfolio value
+    #' @param method Calculation method ('historical', 'parametric', or 'monte_carlo')
+    #' @param n_simulations Number of simulations for Monte Carlo method
+    calculate_es = function(portfolio_value, method = "historical", n_simulations = 10000) {
+      if (is.null(self$returns)) {
+        stop("No returns data available. Please initialize with returns data.")
+      }
+      
+      method <- match.arg(method, c("historical", "parametric", "monte_carlo"))
+      
+      es_value <- switch(method,
+        "historical" = private$calculate_historical_es(portfolio_value),
+        "parametric" = private$calculate_parametric_es(portfolio_value),
+        "monte_carlo" = private$calculate_monte_carlo_es(portfolio_value, n_simulations)
+      )
+      
+      # Scale ES to time horizon
+      es_value * sqrt(self$time_horizon)
+    },
+    
+    #' @description Generate risk report with multiple metrics
+    #' @param portfolio_value Current portfolio value
+    #' @param include_methods Which methods to include in report
+    generate_risk_report = function(portfolio_value, 
+                                  include_methods = c("historical", "parametric", "monte_carlo")) {
+      results <- list()
+      
+      for (method in include_methods) {
+        results[[method]] <- list(
+          var = self$calculate_var(portfolio_value, method),
+          es = self$calculate_es(portfolio_value, method)
+        )
+      }
+      
+      # Add distribution statistics
+      results$statistics <- list(
+        mean_return = mean(self$returns),
+        volatility = sd(self$returns),
+        skewness = private$calculate_skewness(),
+        kurtosis = private$calculate_kurtosis()
+      )
+      
+      return(results)
+    },
+    
+    #' @description Update returns data and recalculate distribution parameters
+    #' @param new_returns New returns data to use
+    update_returns = function(new_returns) {
+      self$returns <- new_returns
+      private$fit_distribution()
+      invisible(self)
+    },
+    
+    # Public fields
+    returns = NULL,
+    confidence_level = NULL,
+    time_horizon = NULL
+  ),
+  
+  private = list(
+    # Distribution parameters
+    mean_return = NULL,
+    volatility = NULL,
+    
+    #' @description Fit distribution to returns data
+    fit_distribution = function() {
+      private$mean_return <- mean(self$returns)
+      private$volatility <- sd(self$returns)
+    },
+    
+    #' @description Calculate Historical VaR
+    calculate_historical_var = function(portfolio_value) {
+      sorted_returns <- sort(self$returns)
+      index <- floor((1 - self$confidence_level) * length(sorted_returns))
+      -sorted_returns[index] * portfolio_value
+    },
+    
+    #' @description Calculate Parametric VaR
+    calculate_parametric_var = function(portfolio_value) {
+      z_score <- stats::qnorm(self$confidence_level)
+      portfolio_value * (z_score * private$volatility - private$mean_return)
+    },
+    
+    #' @description Calculate Monte Carlo VaR
+    calculate_monte_carlo_var = function(portfolio_value, n_simulations) {
+      simulated_returns <- stats::rnorm(
+        n_simulations, 
+        mean = private$mean_return, 
+        sd = private$volatility
+      )
+      sorted_returns <- sort(simulated_returns)
+      index <- floor((1 - self$confidence_level) * n_simulations)
+      -sorted_returns[index] * portfolio_value
+    },
+    
+    #' @description Calculate Historical Expected Shortfall
+    calculate_historical_es = function(portfolio_value) {
+      sorted_returns <- sort(self$returns)
+      var_index <- floor((1 - self$confidence_level) * length(sorted_returns))
+      tail_returns <- sorted_returns[1:var_index]
+      -mean(tail_returns) * portfolio_value
+    },
+    
+    #' @description Calculate Parametric Expected Shortfall
+    calculate_parametric_es = function(portfolio_value) {
+      z_score <- stats::qnorm(self$confidence_level)
+      phi_z <- stats::dnorm(z_score)
+      lambda <- phi_z / (1 - self$confidence_level)
+      portfolio_value * (lambda * private$volatility - private$mean_return)
+    },
+    
+    #' @description Calculate Monte Carlo Expected Shortfall
+    calculate_monte_carlo_es = function(portfolio_value, n_simulations) {
+      simulated_returns <- stats::rnorm(
+        n_simulations, 
+        mean = private$mean_return, 
+        sd = private$volatility
+      )
+      sorted_returns <- sort(simulated_returns)
+      var_index <- floor((1 - self$confidence_level) * n_simulations)
+      tail_returns <- sorted_returns[1:var_index]
+      -mean(tail_returns) * portfolio_value
+    },
+    
+    #' @description Calculate distribution skewness
+    calculate_skewness = function() {
+      r <- self$returns
+      n <- length(r)
+      m3 <- sum((r - mean(r))^3) / n
+      s3 <- sd(r)^3
+      m3 / s3
+    },
+    
+    #' @description Calculate distribution kurtosis
+    calculate_kurtosis = function() {
+      r <- self$returns
+      n <- length(r)
+      m4 <- sum((r - mean(r))^4) / n
+      s4 <- sd(r)^4
+      m4 / s4 - 3  # Excess kurtosis (normal = 0)
+    },
+    
+    #' @description Validate input parameters
+    validate_parameters = function(confidence_level, time_horizon) {
+      if (confidence_level <= 0 || confidence_level >= 1) {
+        stop("Confidence level must be between 0 and 1")
+      }
+      if (time_horizon <= 0) {
+        stop("Time horizon must be positive")
+      }
+    }
+  )
+)
+
+# Demonstration
+demonstrate_risk_metrics <- function() {
+  cat("=== Value at Risk and Expected Shortfall Demo ===\n\n")
+  
+  # Generate sample returns data
+  set.seed(42)
+  n_days <- 1000
+  returns <- rnorm(n_days, mean = 0.0001, sd = 0.01)
+  
+  # Initialize calculator
+  risk_calc <- RiskMetrics$new(
+    returns = returns,
+    confidence_level = 0.95,
+    time_horizon = 1
+  )
+  
+  # Portfolio parameters
+  portfolio_value <- 1000000  # $1 million portfolio
+  
+  cat("Portfolio Parameters:\n")
+  cat(sprintf("Value: $%d\n", portfolio_value))
+  cat(sprintf("Confidence Level: %.1f%%\n", risk_calc$confidence_level * 100))
+  cat(sprintf("Time Horizon: %d day(s)\n\n", risk_calc$time_horizon))
+  
+  # Calculate VaR using different methods
+  methods <- c("historical", "parametric", "monte_carlo")
+  
+  cat("Value at Risk (VaR) Results:\n")
+  for (method in methods) {
+    var_value <- risk_calc$calculate_var(portfolio_value, method)
+    cat(sprintf("%s VaR: $%.2f\n", tools::toTitleCase(method), var_value))
+  }
+  cat("\n")
+  
+  cat("Expected Shortfall (ES) Results:\n")
+  for (method in methods) {
+    es_value <- risk_calc$calculate_es(portfolio_value, method)
+    cat(sprintf("%s ES: $%.2f\n", tools::toTitleCase(method), es_value))
+  }
+  cat("\n")
+  
+  # Generate and display comprehensive risk report
+  cat("Comprehensive Risk Report:\n")
+  report <- risk_calc$generate_risk_report(portfolio_value)
+  
+  cat("\nDistribution Statistics:\n")
+  cat(sprintf("Mean Return: %.6f\n", report$statistics$mean_return))
+  cat(sprintf("Volatility: %.6f\n", report$statistics$volatility))
+  cat(sprintf("Skewness: %.6f\n", report$statistics$skewness))
+  cat(sprintf("Excess Kurtosis: %.6f\n", report$statistics$kurtosis))
+  
+  cat("\n=== Demo Complete ===\n")
+}
+
+# Run demonstration if not in interactive mode
+if (!interactive()) {
+  demonstrate_risk_metrics()
+}
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