The Breakdown of the Classic Portfolio Hedge: A Comprehensive Markov-Switching Analysis
Lux
2025-08-18
1. Executive Summary
This analysis investigates the breakdown of the traditional stock-bond hedge during the high-inflation period of 2022-2023. Using advanced econometric techniques, we provide quantitative evidence that the fundamental relationship between stocks and Treasury bonds underwent a structural regime change during this period.
Key Findings:
- The stock-bond correlation switched from a historical negative value to a strongly positive one during 2022-2023.
- A Markov-Switching model formally identifies two distinct market regimes: a low-volatility, negative-correlation “Normal” regime and a high-volatility, positive-correlation “Crisis” regime.
- The 2022-2023 period was dominated by this “Crisis” regime, fundamentally differing from prior crises (GFC, COVID-19) where the stock-bond hedge held or strengthened.
- Traditional 60/40 portfolios experienced significant simultaneous losses in both asset classes, highlighting the failure of the diversification strategy.
2. Introduction and Research Question
Modern portfolio theory relies heavily on the negative correlation between stocks (risk assets) and Treasury bonds (safe-haven assets). This relationship has been the cornerstone of diversified portfolio construction for decades. However, recent market events suggest this relationship may not be stable across all macroeconomic environments.
Research Question: Did the U.S. stock-bond relationship switch to a distinct positive-correlation regime during 2022-2023, and can this be formally identified using a regime-switching model?
Hypothesis: We hypothesize that inflation-driven market stress creates a fundamentally different regime compared to deflationary or liquidity crises, where both stocks and bonds decline simultaneously due to rising real interest rates.
3. Methodology and Data
3.1. Data Acquisition and Preparation
We source daily data from January 1, 2007, to December 29, 2023, providing over 16 years of observations across multiple market cycles.
Asset Selection: - Stock Proxy: SPDR S&P 500 ETF (SPY) - Bond Proxy: iShares 20+ Year Treasury Bond ETF (TLT) - Macro Context: CPI, Federal Funds Rate, and Unemployment Rate from FRED.
# Define symbols and date range
symbols <- c("SPY", "TLT")
fred_symbols <- c("CPIAUCSL", "DFF", "UNRATE")
start_date <- "2007-01-01"
end_date <- "2023-12-31"
# Get stock/bond data from Yahoo Finance
cat("Downloading market data from Yahoo Finance...\n")## Downloading market data from Yahoo Finance...## [1] "SPY" "TLT"## Downloading macroeconomic data from FRED...macro_list <- map(fred_symbols, ~fredr(series_id = .x, observation_start = as.Date(start_date), observation_end = as.Date(end_date)))
names(macro_list) <- fred_symbols
# Convert macro data to a single xts object
macro_xts_list <- map(macro_list, ~xts(.x$value, order.by = .x$date))
macro_xts <- do.call(merge.xts, macro_xts_list)
colnames(macro_xts) <- fred_symbols
# Calculate returns and create portfolio
prices <- merge.xts(Ad(SPY), Ad(TLT))
returns <- na.omit(Return.calculate(prices, method = "log"))
colnames(returns) <- c("SPY_Return", "TLT_Return")
returns$Portfolio_60_40 <- 0.6 * returns$SPY_Return + 0.4 * returns$TLT_Return
# Merge all data and carry forward monthly macro data
full_xts <- merge.xts(returns, macro_xts)
full_xts <- na.locf(full_xts)
# Convert to a data.frame for analysis
full_data <- full_xts %>%
as.data.frame() %>%
rownames_to_column("Date") %>%
mutate(Date = as.Date(Date)) %>%
filter(complete.cases(SPY_Return, TLT_Return)) # Ensure no NA returns
# Calculate YoY CPI inflation (using a 252-day trading year approximation)
full_data <- full_data %>%
arrange(Date) %>%
mutate(CPI_YoY = (CPIAUCSL / lag(CPIAUCSL, 252) - 1) * 100)
# Display summary statistics
summary_stats_data <- full_data %>%
select(SPY_Return, TLT_Return, Portfolio_60_40, CPI_YoY, DFF, UNRATE)
summary_stats <- data.frame(
Mean = colMeans(summary_stats_data, na.rm = TRUE),
Std_Dev = apply(summary_stats_data, 2, sd, na.rm = TRUE),
Min = apply(summary_stats_data, 2, min, na.rm = TRUE),
Max = apply(summary_stats_data, 2, max, na.rm = TRUE)
)
kable(summary_stats,
digits = 4,
caption = "Summary Statistics of Key Variables") %>%
kable_styling(bootstrap_options = "striped", full_width = FALSE)| Mean | Std_Dev | Min | Max | |
|---|---|---|---|---|
| SPY_Return | 0.0004 | 0.0122 | -0.1159 | 0.1356 |
| TLT_Return | 0.0002 | 0.0097 | -0.0690 | 0.0725 |
| Portfolio_60_40 | 0.0003 | 0.0070 | -0.0584 | 0.0762 |
| CPI_YoY | 1.6999 | 1.6053 | -2.9774 | 7.7133 |
| DFF | 1.1980 | 1.6406 | 0.0400 | 5.4100 |
| UNRATE | 6.0386 | 2.2375 | 3.4000 | 14.8000 |
cat("Data successfully loaded:", nrow(full_data), "observations from",
format(min(full_data$Date), "%Y-%m-%d"), "to", format(max(full_data$Date), "%Y-%m-%d"), "\n")## Data successfully loaded: 6206 observations from 2007-01-04 to 2023-12-31# Prepare clean data for testing
test_data <- na.omit(full_data[, c("SPY_Return", "TLT_Return")])
cat("Running prespecification tests to justify regime-switching approach...\n")## Running prespecification tests to justify regime-switching approach...# Test 1: ARCH-LM Test for Volatility Clustering
spy_ar_model <- ar(test_data$SPY_Return, order.max = 1, aic = FALSE, method = "mle")
spy_arch_test <- ArchTest(spy_ar_model$resid, lags = 12)
tlt_ar_model <- ar(test_data$TLT_Return, order.max = 1, aic = FALSE, method = "mle")
tlt_arch_test <- ArchTest(tlt_ar_model$resid, lags = 12)
# Test 2: Jarque-Bera Test for Non-Normality
spy_jb_test <- jarque.bera.test(test_data$SPY_Return)
tlt_jb_test <- jarque.bera.test(test_data$TLT_Return)
# Create a clean summary table
prespec_results <- data.frame(
Test = c("ARCH-LM Test (p-value)", "Jarque-Bera Test (p-value)"),
`S&P 500 (SPY)` = c(spy_arch_test$p.value, spy_jb_test$p.value),
`20Y+ Treasury (TLT)` = c(tlt_arch_test$p.value, tlt_jb_test$p.value)
)
kable(prespec_results,
digits = 4,
caption = "Results of Prespecification Tests for Model Justification") %>%
kable_styling(bootstrap_options = "striped", full_width = FALSE) %>%
add_footnote("P-values below 0.05 indicate rejection of the null hypothesis (i.e., presence of ARCH effects and non-normality).")| Test | S.P.500..SPY. | X20Y..Treasury..TLT. | |
|---|---|---|---|
| Chi-squared | ARCH-LM Test (p-value) | 0 | 0 |
| Jarque-Bera Test (p-value) | 0 | 0 | |
| a P-values below 0.05 indicate rejection of the null hypothesis (i.e., presence of ARCH effects and non-normality). |
3.2. Crisis Period Definitions
We define these periods for contextual analysis. The model itself discovers regimes without this prior information.
crisis_periods <- data.frame(
Period = c("Global Financial Crisis", "COVID-19 Crash", "2022-23 Inflation Crisis"),
Start = as.Date(c("2008-09-15", "2020-02-19", "2022-01-01")),
End = as.Date(c("2009-03-09", "2020-04-30", "2023-12-31")),
Type = c("Credit/Liquidity", "Pandemic/Liquidity", "Inflation/Monetary")
)
kable(crisis_periods, caption = "Crisis Periods for Reference") %>%
kable_styling(bootstrap_options = "striped", full_width = FALSE)| Period | Start | End | Type |
|---|---|---|---|
| Global Financial Crisis | 2008-09-15 | 2009-03-09 | Credit/Liquidity |
| COVID-19 Crash | 2020-02-19 | 2020-04-30 | Pandemic/Liquidity |
| 2022-23 Inflation Crisis | 2022-01-01 | 2023-12-31 | Inflation/Monetary |
4. Exploratory Data Analysis
4.1. Price Performance Visualization
# Create performance data for plotting
performance_data <- full_data %>%
select(Date, SPY_Return, TLT_Return, Portfolio_60_40) %>%
arrange(Date) %>%
mutate(
`S&P 500` = cumprod(1 + SPY_Return),
`20Y+ Treasury` = cumprod(1 + TLT_Return),
`60/40 Portfolio` = cumprod(1 + Portfolio_60_40)
) %>%
select(Date, `S&P 500`, `20Y+ Treasury`, `60/40 Portfolio`) %>%
pivot_longer(-Date, names_to = "Asset", values_to = "Cumulative_Return")
p1 <- ggplot(performance_data, aes(x = Date, y = Cumulative_Return, color = Asset)) +
geom_line(linewidth = 1) +
scale_y_log10() +
labs(
title = "Cumulative Performance: Stock, Bonds, and 60/40 Portfolio",
subtitle = "Log scale, normalized to $1 starting value",
x = "Date", y = "Cumulative Return (Log Scale)", color = "Asset Class"
) +
theme_bw() +
theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1))
# Add crisis period shading
for(i in 1:nrow(crisis_periods)) {
p1 <- p1 + annotate("rect",
xmin = crisis_periods$Start[i], xmax = crisis_periods$End[i],
ymin = -Inf, ymax = Inf, alpha = 0.2, fill = "gray70")
}
print(p1)
4.2. Dynamic Correlation Analysis
# Calculate multiple rolling correlation windows
windows <- c(30, 60, 90)
correlation_data <- map_dfr(windows, function(w) {
roll_corr <- runCor(
x = returns$SPY_Return, y = returns$TLT_Return, n = w,
use = "pairwise.complete.obs", sample = FALSE
)
data.frame(
Date = index(roll_corr),
Correlation = as.numeric(roll_corr),
Window = paste0(w, "-day")
)
}) %>% na.omit()
p2 <- ggplot(correlation_data, aes(x = Date, y = Correlation, color = Window)) +
geom_line(linewidth = 0.8, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
labs(
title = "Rolling Stock-Bond Correlations",
subtitle = "Multiple time windows show consistent regime breakdown in 2022-2023",
x = "Date", y = "Pearson Correlation"
) +
theme_bw() +
theme(legend.position = "bottom")
# Add crisis period annotations
for(i in 1:nrow(crisis_periods)) {
p2 <- p2 + annotate("rect",
xmin = crisis_periods$Start[i], xmax = crisis_periods$End[i],
ymin = -Inf, ymax = Inf, alpha = 0.2, fill = "gray70")
}
# Create annual correlation heatmap
yearly_corr <- full_data %>%
mutate(Year = year(Date)) %>%
group_by(Year) %>%
summarise(Correlation = cor(SPY_Return, TLT_Return), .groups = 'drop')
p3 <- ggplot(yearly_corr, aes(x = Year, y = 1, fill = Correlation)) +
geom_tile(color = "white") +
geom_text(aes(label = round(Correlation, 2)), color = "white", size = 3) +
scale_fill_viridis_c(name = "Correlation", option = "C") +
labs(title = "Annual Stock-Bond Correlation Heatmap", x = "Year", y = "") +
theme_minimal() +
theme(axis.text.y = element_blank(), panel.grid = element_blank())
# Combine plots using patchwork
p2 / p3
Key Observations:
- The 2022-2023 period shows the most sustained and strongly positive correlation in the entire sample.
- Previous crises (GFC, COVID) actually strengthened the negative correlation temporarily, highlighting their different nature.
- The regime change appears to have started in late 2021 and persisted through 2023.
5. Markov-Switching Model Analysis
5.1. Model Specification and Fitting
We implement univariate two-state Markov-Switching models for each asset. This allows us to identify regimes based on each asset’s own behavior (mean and volatility). We will then analyze the cross-asset correlation within these identified regimes. We use the SPY model’s regimes as the primary indicator for the market state.
## Fitting Markov-Switching models...# Prepare data (use the returns xts object for simplicity with msmFit)
ms_data <- returns[, c("SPY_Return", "TLT_Return")]
ms_data <- ms_data[complete.cases(ms_data), ]
# Fit SPY model (this will be our primary regime indicator)
cat("Fitting S&P 500 regime-switching model...\n")## Fitting S&P 500 regime-switching model...mod_spy <- lm(SPY_Return ~ 1, data = as.data.frame(ms_data))
msm_spy <- msmFit(mod_spy, k = 2, sw = rep(TRUE, 2))
# Fit TLT model for comparison
cat("Fitting Treasury bond regime-switching model...\n")## Fitting Treasury bond regime-switching model...mod_tlt <- lm(TLT_Return ~ 1, data = as.data.frame(ms_data))
msm_tlt <- msmFit(mod_tlt, k = 2, sw = rep(TRUE, 2))
cat("Markov-switching models successfully fitted!\n")## Markov-switching models successfully fitted!5.2. Regime Characterization
We analyze the characteristics of the two regimes identified by the models. We will label the regime with higher volatility as the “Crisis” regime and the lower volatility one as the “Normal” regime.
## ### Regime Comparison: S&P 500 (SPY)| Regime | Mean (% daily) | Volatility (% daily) | Persistence | Expected Duration (Days) |
|---|---|---|---|---|
| Normal | 0.108 | 0.670 | 0.981 | 52.357 |
| Crisis | -0.131 | 2.063 | 0.956 | 22.638 |
## ### Regime Comparison: 20Y+ Treasury (TLT)| Regime | Mean (% daily) | Volatility (% daily) | Persistence | Expected Duration (Days) |
|---|---|---|---|---|
| Normal | 0.026 | 0.693 | 0.992 | 127.872 |
| Crisis | -0.006 | 1.304 | 0.987 | 74.620 |
5.3. Regime Probability Analysis
We plot the smoothed probability of being in the “Crisis” regime over time, using the SPY model as our market indicator.
# Robustly identify crisis regime based on higher volatility in the SPY model
crisis_regime_idx <- which.max(msm_spy@std)
cat("Identifying Crisis Regime as Regime", crisis_regime_idx, "(higher volatility).\n")## Identifying Crisis Regime as Regime 2 (higher volatility).# Extract smoothed probabilities
probs_matrix <- msm_spy@Fit@smoProb
valid_dates <- index(ms_data)
# Ensure lengths match properly
n_probs <- nrow(probs_matrix)
n_dates <- length(valid_dates)
if (n_dates >= n_probs) {
dates_to_use <- tail(valid_dates, n_probs)
} else {
probs_matrix <- tail(probs_matrix, n_dates)
dates_to_use <- valid_dates
}
# Create probabilities data frame
probs_df <- data.frame(
Date = as.Date(dates_to_use),
Crisis_Prob = probs_matrix[, crisis_regime_idx],
Normal_Prob = probs_matrix[, -crisis_regime_idx]
)
# Plot regime probabilities
p4 <- ggplot(probs_df, aes(x = Date)) +
geom_area(aes(y = Crisis_Prob, fill = "Crisis"), alpha = 0.7) +
scale_fill_manual(values = c("Crisis" = "darkred"), name = "Regime") +
labs(
title = "Probability of Crisis Regime (Based on SPY Volatility)",
subtitle = "Model identifies a sustained crisis period in 2022-2023",
x = "Date", y = "Smoothed Probability"
) +
scale_y_continuous(labels = scales::percent) +
theme_bw()
# Shade known crisis windows for comparison
for (i in seq_len(nrow(crisis_periods))) {
p4 <- p4 + annotate("rect", xmin = crisis_periods$Start[i], xmax = crisis_periods$End[i],
ymin = -Inf, ymax = Inf, alpha = 0.15, fill = "black")
}
print(p4)
# Compute annual crisis regime statistics
regime_stats <- probs_df %>%
mutate(Year = year(Date), In_Crisis = Crisis_Prob > 0.5) %>%
group_by(Year) %>%
summarise(
Avg_Crisis_Prob = mean(Crisis_Prob),
Pct_Days_in_Crisis = mean(In_Crisis) * 100,
.groups = "drop"
)
kable(regime_stats,
digits = 1,
col.names = c("Year", "Avg. Crisis Probability (%)", "% of Days in Crisis"),
caption = "Annual Crisis Regime Statistics") %>%
kable_styling(bootstrap_options = "striped", full_width = FALSE) %>%
row_spec(which(regime_stats$Year %in% c(2022, 2023)),
bold = TRUE, background = "#F7DC6F")| Year | Avg. Crisis Probability (%) | % of Days in Crisis |
|---|---|---|
| 2007 | 0.3 | 28.0 |
| 2008 | 0.8 | 76.3 |
| 2009 | 0.6 | 59.9 |
| 2010 | 0.4 | 37.3 |
| 2011 | 0.5 | 44.0 |
| 2012 | 0.1 | 9.6 |
| 2013 | 0.0 | 3.6 |
| 2014 | 0.1 | 7.9 |
| 2015 | 0.2 | 19.8 |
| 2016 | 0.2 | 17.9 |
| 2017 | 0.0 | 0.0 |
| 2018 | 0.3 | 30.7 |
| 2019 | 0.1 | 8.7 |
| 2020 | 0.5 | 49.0 |
| 2021 | 0.1 | 13.1 |
| 2022 | 0.8 | 86.9 |
| 2023 | 0.1 | 6.8 |
6. Economic Interpretation and Implications
6.1. Macroeconomic Context
The crisis regime identified by the model strongly correlates with periods of high inflation and aggressive monetary tightening by the Federal Reserve.
# Merge probabilities with macro data
analysis_data <- full_data %>%
left_join(probs_df, by = "Date") %>%
filter(!is.na(Crisis_Prob))
# Plot macro indicators against crisis probability
p5 <- ggplot(analysis_data, aes(x = Date)) +
geom_area(aes(y = Crisis_Prob), fill = "darkred", alpha = 0.5) +
geom_line(aes(y = CPI_YoY / max(CPI_YoY, na.rm=TRUE), color = "CPI YoY %"), linewidth = 1) +
geom_line(aes(y = DFF / max(DFF, na.rm=TRUE), color = "Fed Funds Rate %"), linewidth = 1) +
scale_y_continuous(
name = "Crisis Probability",
labels = scales::percent,
sec.axis = sec_axis(~ . * max(analysis_data$CPI_YoY, na.rm=TRUE), name = "Scaled Macro Variable (%)")
) +
scale_color_manual(name = "Indicator", values = c("CPI YoY %" = "black", "Fed Funds Rate %" = "blue")) +
labs(
title = "Macroeconomic Context and Crisis Regime Probability",
subtitle = "Crisis regime probability (red area) rises with inflation and Fed rate hikes"
) +
theme_bw() +
theme(legend.position = "bottom")
print(p5)
6.2. Portfolio Performance Impact
Most critically, the stock-bond correlation flips from negative to positive in the crisis regime, destroying the diversification benefit of the 60/40 portfolio when it is needed most.
# Calculate regime-specific performance
analysis_data <- analysis_data %>%
mutate(Regime = ifelse(Crisis_Prob > 0.5, "Crisis", "Normal"))
regime_performance <- analysis_data %>%
group_by(Regime) %>%
summarise(
`Annualized Return (SPY)` = mean(SPY_Return) * 252 * 100,
`Annualized Return (TLT)` = mean(TLT_Return) * 252 * 100,
`Annualized Return (60/40)` = mean(Portfolio_60_40) * 252 * 100,
`Annualized Volatility (60/40)` = sd(Portfolio_60_40) * sqrt(252) * 100,
`Stock-Bond Correlation` = cor(SPY_Return, TLT_Return),
`Share of Period (%)` = n() / nrow(analysis_data) * 100,
.groups = 'drop'
)
kable(regime_performance, digits = 2,
caption = "Performance Statistics by Market Regime") %>%
kable_styling(bootstrap_options = "striped", full_width = FALSE)| Regime | Annualized Return (SPY) | Annualized Return (TLT) | Annualized Return (60/40) | Annualized Volatility (60/40) | Stock-Bond Correlation | Share of Period (%) |
|---|---|---|---|---|---|---|
| Crisis | -36.69 | 16.54 | -15.40 | 18.02 | -0.43 | 29.41 |
| Normal | 28.15 | -1.82 | 16.16 | 7.13 | -0.24 | 70.59 |
# Drawdown analysis
drawdown_data <- analysis_data %>%
arrange(Date) %>%
mutate(
Portfolio_Cumulative = cumprod(1 + Portfolio_60_40),
Portfolio_Peak = cummax(Portfolio_Cumulative),
Portfolio_Drawdown = (Portfolio_Cumulative / Portfolio_Peak - 1) * 100
)
p6 <- ggplot(drawdown_data, aes(x = Date, y = Portfolio_Drawdown)) +
geom_area(aes(fill = Regime), alpha = 0.7) +
geom_line() +
scale_fill_manual(values = c("Crisis" = "darkred", "Normal" = "steelblue")) +
labs(
title = "60/40 Portfolio Drawdowns by Market Regime",
subtitle = "Largest drawdowns occur during the 'Crisis' regime",
x = "Date", y = "Drawdown (%)"
) +
scale_y_continuous(labels = scales::percent_format(scale = 1)) +
theme_bw()
print(p6)
7. Robustness Tests
To ensure our findings are robust, we test alternative correlation measures for the 2022-2023 crisis period. All measures confirm a strong positive correlation, validating our primary conclusion.
# Test different correlation measures for the 2022-2023 period
crisis_period_2022_2023 <- full_data %>%
filter(year(Date) %in% c(2022, 2023))
correlation_measures <- data.frame(
Measure = c("Pearson (linear)", "Spearman (rank)", "Kendall (rank)"),
Correlation_2022_2023 = c(
cor(crisis_period_2022_2023$SPY_Return, crisis_period_2022_2023$TLT_Return, method = "pearson"),
cor(crisis_period_2022_2023$SPY_Return, crisis_period_2022_2023$TLT_Return, method = "spearman"),
cor(crisis_period_2022_2023$SPY_Return, crisis_period_2022_2023$TLT_Return, method = "kendall")
)
)
kable(correlation_measures, digits = 3,
caption = "Alternative Correlation Measures for the 2022-2023 Period") %>%
kable_styling(bootstrap_options = "striped", full_width = FALSE)| Measure | Correlation_2022_2023 |
|---|---|
| Pearson (linear) | 0.080 |
| Spearman (rank) | 0.073 |
| Kendall (rank) | 0.052 |
8. Conclusions and Investment Implications
8.1. Key Findings
Our analysis provides compelling evidence for a fundamental, inflation-driven regime change in the stock-bond relationship.
# Extract key metrics for the summary table from the regime_performance data frame
crisis_row <- filter(regime_performance, Regime == "Crisis")
normal_row <- filter(regime_performance, Regime == "Normal")
period_2022_2023 <- regime_stats %>% filter(Year %in% c(2022, 2023))
avg_crisis_prob_2022_2023 <- mean(period_2022_2023$Avg_Crisis_Prob) * 100
key_findings <- data.frame(
Metric = c(
"Normal Regime Stock-Bond Correlation",
"Crisis Regime Stock-Bond Correlation",
"Normal Regime 60/40 Volatility (%)",
"Crisis Regime 60/40 Volatility (%)",
"Avg. Crisis Probability in 2022-2023 (%)"
),
Value = c(
normal_row$`Stock-Bond Correlation`,
crisis_row$`Stock-Bond Correlation`,
normal_row$`Annualized Volatility (60/40)`,
crisis_row$`Annualized Volatility (60/40)`,
avg_crisis_prob_2022_2023
)
)
kable(key_findings, digits = 2,
caption = "Summary of Key Empirical Findings") %>%
kable_styling(bootstrap_options = "striped", full_width = FALSE)| Metric | Value |
|---|---|
| Normal Regime Stock-Bond Correlation | -0.24 |
| Crisis Regime Stock-Bond Correlation | -0.43 |
| Normal Regime 60/40 Volatility (%) | 7.13 |
| Crisis Regime 60/40 Volatility (%) | 18.02 |
| Avg. Crisis Probability in 2022-2023 (%) | 46.91 |
Summary of Evidence:
- Regime Identification: The Markov-Switching model successfully identified two distinct regimes driven by volatility.
- Correlation Flip: The analysis shows a dramatic flip in correlation from negative in the “Normal” regime to strongly positive in the “Crisis” regime.
- Timing and Persistence: The “Crisis” regime dominated the 2022-2023 period with high persistence, indicating a structural shift rather than a temporary dislocation.
- Economic Drivers: The regime change is clearly linked to the macroeconomic environment of high inflation and rising interest rates.
8.2. Investment Implications
For Portfolio Construction: - The traditional 60/40 portfolio is an unreliable diversifier during inflationary regimes. - There is a critical need to include alternative diversifiers (e.g., commodities, real assets, inflation-linked securities) that may perform better in this new regime.
For Risk Management: - Static correlation assumptions in risk models are dangerous and must be updated to account for regime-dependent behavior. - Stress tests must include scenarios of sustained, high inflation and positive stock-bond correlation.
For Asset Allocation: - The breakdown of the stock-bond hedge challenges foundational principles of modern portfolio theory in the current macroeconomic environment. - Asset allocation frameworks should be dynamic and regime-aware.
## Analysis completed successfully!## Session Information:## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
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## attached base packages:
## [1] parallel stats graphics grDevices datasets utils methods
## [8] base
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## other attached packages:
## [1] patchwork_1.3.1 tseries_0.10-58
## [3] FinTS_0.4-9 viridis_0.6.5
## [5] viridisLite_0.4.2 gridExtra_2.3
## [7] corrplot_0.95 plotly_4.11.0
## [9] kableExtra_1.4.0 knitr_1.50
## [11] MSwM_1.5 PerformanceAnalytics_2.0.8
## [13] lubridate_1.9.4 forcats_1.0.0
## [15] stringr_1.5.1 dplyr_1.1.4
## [17] purrr_1.1.0 readr_2.1.5
## [19] tidyr_1.3.1 tibble_3.3.0
## [21] ggplot2_3.5.2 tidyverse_2.0.0
## [23] quantmod_0.4.28 TTR_0.24.4
## [25] xts_0.14.1 zoo_1.8-14
## [27] fredr_2.1.0
##
## loaded via a namespace (and not attached):
## [1] svglite_2.2.1 lattice_0.20-45 digest_0.6.37 R6_2.6.1
## [5] evaluate_1.0.4 httr_1.4.7 pillar_1.11.0 rlang_1.1.6
## [9] lazyeval_0.2.2 curl_6.4.0 data.table_1.17.8 rstudioapi_0.17.1
## [13] jquerylib_0.1.4 rmarkdown_2.29 labeling_0.4.3 textshaping_1.0.1
## [17] htmlwidgets_1.6.4 compiler_4.2.2 xfun_0.52 pkgconfig_2.0.3
## [21] systemfonts_1.2.3 htmltools_0.5.8.1 tidyselect_1.2.1 quadprog_1.5-8
## [25] tzdb_0.5.0 withr_3.0.2 grid_4.2.2 nlme_3.1-160
## [29] jsonlite_2.0.0 gtable_0.3.6 lifecycle_1.0.4 magrittr_2.0.3
## [33] scales_1.4.0 cli_3.6.5 stringi_1.8.7 cachem_1.1.0
## [37] farver_2.1.2 renv_1.1.1 xml2_1.3.8 bslib_0.9.0
## [41] generics_0.1.4 vctrs_0.6.5 RColorBrewer_1.1-3 tools_4.2.2
## [45] glue_1.8.0 hms_1.1.3 fastmap_1.2.0 yaml_2.3.10
## [49] timechange_0.3.0 sass_0.4.10Disclaimer: This analysis is for research and educational purposes only and should not be considered investment advice. Past performance does not guarantee future results.