Democratic Governance and Equity Valuations (2026) — The Quarterly Review of Economics and Finance.
Recent research by Bahram Adrangi and coauthors examines whether democratic institutions are associated with higher equity-market valuations across countries. Using data from 24 countries over 2006–2023, the study finds that countries with stronger democratic institutions tend to have higher cyclically adjusted price-to-earnings (CAPE) ratios and lower required rates of return. These relationships persist after accounting for economic growth, development, legal systems, corruption perceptions, and other financial and market characteristics. The findings suggest that democratic institutions may reduce investors’ perceptions of institutional risk and contribute to a valuation premium in global equity markets.
Productivity, Crude Oil Supply Shocks and the Economy of Iran in a Dynamic Stochastic General Equilibrium Framework (2026) — Econometrics.
Bahram Adrangi and coauthors examine how productivity and crude oil supply shocks affect the Iranian economy using a Real Business Cycle–Dynamic Stochastic General Equilibrium (RBC–DSGE) model and quarterly data from 1975–2024. The study finds that positive productivity and oil supply shocks initially increase real GDP, consumption, investment, employment, wages, and capital formation, but these gains gradually dissipate as the economy returns toward its long-run equilibrium. The results demonstrate the important role of oil dependence, productivity, sanctions, and external shocks in shaping Iran's macroeconomic fluctuations and suggest that lasting economic growth requires mechanisms that transform temporary oil and productivity gains into sustained investment and development.
The Roles of Political and Economic Freedoms in Global Equity Valuations. (2025) — The American Economist.
Bahram Adrangi and coauthors examine how political freedom, economic freedom, and institutional differences are associated with stock market valuations across 23 countries representing more than 90% of global equity market capitalization. Using the Shiller Cyclically Adjusted Price-to-Earnings ratio (CAPE), the Democracy Index, and measures of economic freedom over 2006–2023, the study employs international panel-data and annual cross-sectional analysis to explain differences in global equity valuations. The results show a strong positive association between political freedom and stock market valuation, while economic freedom and property rights have substantially less explanatory power. The findings suggest that democratic institutions may influence equity valuations through greater institutional trust, transparency, policy stability, and lower political risk.
Publicly Subsidized Childcare and the Labor Force Participation Rates of Men and Women: Evidence From OECD (2025) — The American Economist.
Bahram Adrangi and Krisztian Jeszenszki examine how publicly subsidized childcare and family-friendly policies affect male and female labor force participation across 22 OECD countries. Published in The American Economist, the study uses panel-data econometric methods to analyze full-time and part-time employment and the effects of public childcare expenditures, paid parental leave, and the right to part-time work. The findings indicate that publicly funded childcare alone may not be sufficient to increase labor force participation. Instead, childcare support combined with parental leave, paid leave, flexible and part-time work opportunities, and other family-friendly policies is more effective in supporting employment for both women and men.
The Expanded Child Tax Credit: Impacts on Food Insufficiency Across Demographic Subgroups in the United States (2024) — The American Economist.
Bahram Adrangi, William Barnes, and Derek Berning — The American Economist, 2024
Bahram Adrangi and coauthors examineshow the 2021 Expanded Child Tax Credit (CTC) affected food insufficiency among low-income U.S. households during the COVID-19 pandemic. Using microdata from the U.S. Census Bureau Household Pulse Survey and difference-in-differences models estimated with panel probit methods, the research evaluates whether the CTC reduced food hardship among families with annual incomes below $35,000.
The findings show that eligibility for the Expanded Child Tax Credit was associated with a significant reduction in food insufficiency among low-income families with children. Importantly, the effects differed across demographic and income groups. The final CTC payments produced particularly strong benefits for Hispanic households and families earning less than $25,000 per year, while the estimated reductions were not statistically significant for Black households or families earning between $25,000 and $35,000.
The study contributes to research on the Child Tax Credit, food insecurity, child poverty, and U.S. social policy by demonstrating that the effectiveness of income-support programs can vary substantially across demographic groups. The results also show that the reduction in food insufficiency persisted through the later stages of the 2021 CTC program, providing evidence on the role of refundable tax credits in supporting economically vulnerable households during periods of economic disruption.
S&P 500 Volatility, Volatility Regimes, and Economic Uncertainty (2023) — Bulletin of Economic Research. Bahram Adrangi, Arjun Chatrath, and Kambiz Raffiee
Bahram Adrangi and coauthors examine the relationship between U.S. stock market volatility, economic policy uncertainty, and investor sentiment. Using S&P 500 data, the research investigates how the Economic Policy Uncertainty Index (EPU), the CBOE Volatility Index (VIX), and the S&P 500 bull-bear sentiment spread are associated with short-term and long-term stock market volatility.
The study applies a two-covariate GARCH-MIDAS model to separate S&P 500 volatility into short-run and long-run components. Markov regime-switching and quantile regression models are then used to examine whether the relationships between economic uncertainty, investor sentiment, and stock market volatility change across low- and high-volatility market conditions. This combination of methods allows the analysis to capture nonlinear behavior, volatility regimes, and asymmetric relationships that may not be evident in conventional volatility models.
The empirical findings show that economic policy uncertainty and investor sentiment are associated with both short-term and long-term S&P 500 volatility, but their importance varies considerably across market regimes. EPU, VIX, and sentiment measures are more informative during relatively low-volatility periods and play a smaller role when market volatility becomes unusually high. The results also identify an important relationship between VIX and EPU and persistent long-run stock market volatility.
The research contributes to the literature on stock market volatility, financial market uncertainty, behavioral finance, and volatility forecasting by showing that the relationship between uncertainty and equity market risk is regime dependent. The findings have implications for investors, portfolio managers, risk analysts, and researchers using economic policy uncertainty, VIX, investor sentiment, GARCH-MIDAS, Markov switching, and quantile regression models to understand and forecast S&P 500 volatility.
Established research
Crude Oil Price Volatility Spillovers into Major Equity Markets (2015) — Journal of Energy Markets.
Bahram Adrangi, Arjun Chatrath, Joseph Macri, and Kambiz Raffiee
Journal of Energy Markets, Vol. 8, No. 1, pp. 77–95
Bahram Adrangi and coauthors examine the relationship between crude oil price volatility and major international stock markets, focusing on how oil price shocks are transmitted to equity market volatility. Using daily West Texas Intermediate (WTI) crude oil prices and major equity indexes—including the S&P 500, FTSE 100, DAX, CAC 40, and Nikkei—the research analyzes volatility spillovers across the United States, Europe, and Japan.
The study applies bivariate VAR-GARCH and VAR-EGARCH models to investigate nonlinear relationships, volatility transmission, and asymmetric responses to positive and negative oil price shocks. The analysis finds significant bidirectional volatility spillovers between crude oil prices and major equity markets. Although both oil and stock market returns exhibit nonlinear behavior, the evidence indicates that these nonlinearities are associated primarily with GARCH-type volatility rather than chaotic behavior.
An important finding is that crude oil price shocks affect stock market volatility asymmetrically. Negative oil market shocks generate substantially larger volatility responses than positive shocks of comparable magnitude. On average, the effect of negative crude oil price shocks on conditional stock market volatility is approximately five times as large as the effect of positive shocks. The S&P 500 and Nikkei show particularly strong volatility responses to negative crude oil price innovations.
Nonlinear Granger causality tests provide further evidence of a two-way relationship between crude oil and equity markets. Crude oil prices Granger-cause movements in major developed equity markets, while equity market volatility also feeds back into crude oil price volatility. These findings highlight the close connection between energy markets, investor expectations, financial market risk, and economic activity.
The research contributes to the literature on crude oil prices, oil price shocks, stock market volatility, energy economics, financial markets, volatility spillovers, GARCH and EGARCH models, nonlinear Granger causality, and international equity markets. The findings are relevant to investors, portfolio managers, risk analysts, energy economists, and policymakers seeking to understand how crude oil market shocks are transmitted to the S&P 500 and other major global stock markets.
Economic Activity, Inflation, and Hedging: The Case of Gold and Silver Investments (2003) — Journal of Wealth Management.
Gold and Silver as Inflation Hedges: Economic Activity, Inflation, and Precious-Metal Investment
In “Economic Activity, Inflation, and Hedging: The Case of Gold and Silver Investments,” Bahram Adrangi, Arjun Chatrath, and Kambiz Raffiee examine whether gold and silver provide effective hedges against inflation and how precious-metal prices interact with U.S. real economic activity. The study analyzes monthly gold and silver prices, inflation, and industrial production and employs time-series methods including unit-root tests, instrumental-variable estimation, cointegration analysis, and error-correction models. The empirical results show that gold returns are positively related to expected inflation and that gold can serve as a hedge against inflationary pressures, while silver also exhibits inflation-hedging properties because its real returns are not adversely related to inflation. The long-run analysis finds a positive relationship between the general price level and gold and silver prices, providing additional evidence that precious metals can function as long-run inflation hedges. Gold and silver prices also adjust to changes in inflation and real economic activity, with silver exhibiting a faster adjustment toward long-run equilibrium. The findings support the Fisherian hypothesis for precious-metal investments but provide less consistent support for Fama’s proxy hypothesis. The study contributes to research on gold, silver, inflation hedging, safe-haven assets, precious-metal investment, portfolio management, and the relationship between financial markets and macroeconomic activity.
REIT Investments and Hedging Against Inflation
Bahram Adrangi, Arjun Chatrath, and Kambiz Raffiee
Journal of Real Estate Portfolio Management, Vol. 10, No. 2, 2004, pp. 97–112
Bahram Adrangi and coauthors examine whether Real Estate Investment Trusts (REITs) provide investors with an effective hedge against inflation. The study focuses on both equity REITs and mortgage REITs and investigates their relationships with expected inflation, unexpected inflation, and real economic activity. The central question is whether securitized real estate investments protect investors’ purchasing power during inflationary periods.
Using monthly data on equity and mortgage REITs from January 1972 through June 1999, the study analyzes REIT returns together with the Consumer Price Index and industrial production. The empirical analysis employs Augmented Dickey-Fuller and Phillips-Perron unit-root tests, regression analysis, Chow structural-break tests, Granger causality tests, and Johansen cointegration methods to examine both short-run and long-run relationships among REIT returns, inflation, and real economic activity. Expected and unexpected inflation are separated using the Hodrick-Prescott filter, with ARIMA estimates used as a robustness check.
The results indicate an important distinction between expected and unexpected inflation. Real returns on both equity and mortgage REITs are not significantly related to expected inflation, suggesting that predictable increases in the general price level may be incorporated into property values, rents, leases, and mortgage rates. In contrast, real REIT returns are negatively related to unexpected inflation. Consequently, equity and mortgage REITs may not provide investors with a reliable safe haven when inflation unexpectedly accelerates.
The study also examines whether the inflation-hedging characteristics of REITs changed as REITs became less closely connected to the general stock market. Subperiod analysis shows that both equity and mortgage REITs exhibited inflation-hedging characteristics during 1991–1994, while after 1994 mortgage REITs showed stronger evidence of continuing to provide an inflation hedge. These results suggest that the relationship among REITs, inflation, and financial markets can change over time as the characteristics of securitized real estate investments evolve.
The paper further investigates Fama’s proxy hypothesis, which proposes that the negative relationship between asset returns and inflation may reflect the effects of inflation on real economic activity. Although inflation is negatively associated with real economic activity, the evidence linking real economic activity to real REIT returns is relatively weak. Granger causality tests indicate that real economic activity and inflation help predict REIT returns, but the overall results do not provide strong support for the proxy hypothesis as an explanation for the negative relationship between REIT returns and inflation.
The long-run evidence reinforces the principal finding that REITs should not automatically be viewed as reliable inflation hedges. Even after controlling for the relationship between inflation and real economic activity, the study finds a strong negative relationship between real REIT returns and unexpected inflation. One possible explanation is that during periods of unexpectedly high inflation, property management and maintenance costs may rise more rapidly than rental revenues, while mortgage REITs may be constrained by longer-term mortgage contracts whose returns cannot adjust immediately to changing inflation conditions.
This research contributes to the literature on real estate finance, REIT investments, inflation hedging, equity REITs, mortgage REITs, real estate portfolio management, inflation risk, expected and unexpected inflation, real estate returns, portfolio diversification, asset allocation, cointegration, and financial economics. The findings are relevant to investors, portfolio managers, real estate professionals, financial economists, and researchers seeking to understand whether real estate investment trusts can protect investment portfolios against inflation and preserve purchasing power during inflationary periods.
Are Commodity Prices Chaotic? (2002) — Agricultural Economics.
Arjun Chatrath, Bahram Adrangi, and Kanwalroop Kathy Dhanda
Agricultural Economics, Volume 27, 2002, pp. 123–137
Bahram Adrangi and coauthors examine whether agricultural commodity futures prices exhibit chaotic behavior or whether their nonlinear movements can be better explained by conventional financial time-series models. The study analyzes daily futures prices for four major U.S. agricultural commodities—soybeans, corn, wheat, and cotton—using long price histories extending from the late 1960s and early 1970s through 1995.
The research applies several methods for detecting nonlinear and chaotic behavior, including correlation dimension analysis, the BDS test, and Kolmogorov entropy. The authors also employ autoregressive models and ARCH-type volatility models while explicitly controlling for seasonality and futures contract maturity effects. These methods allow the study to distinguish between true low-dimensional chaos and nonlinear price behavior arising from time-varying volatility.
The results provide strong evidence of nonlinear dependence in agricultural commodity futures prices but do not support the existence of persistent low-dimensional chaotic behavior. Instead, much of the observed nonlinearity can be explained by ARCH and GARCH-type volatility processes. The analysis also shows that adjusting commodity prices for seasonal patterns is important because failure to account for seasonality can lead researchers to incorrectly identify nonlinear price movements as evidence of chaos.
The study further finds that the exponential GARCH (EGARCH) model performs particularly well in explaining nonlinear dynamics in agricultural commodity prices. The results also provide support for the Samuelson hypothesis concerning maturity effects in futures markets, showing the importance of time to contract maturity in understanding commodity futures price volatility.
This research contributes to the literature on agricultural economics, commodity prices, commodity futures markets, nonlinear time-series analysis, chaos theory, price volatility, ARCH and GARCH models, EGARCH, futures contract maturity, the Samuelson hypothesis, and agricultural price forecasting. The findings are relevant to researchers, commodity traders, agricultural economists, risk managers, and policymakers interested in understanding and forecasting volatility in soybean, corn, wheat, and cotton futures markets.
Chaos in Oil Prices? Evidence from Futures Markets
Bahram Adrangi, Arjun Chatrath, Kanwalroop Kathy Dhanda, and Kambiz Raffiee — Energy Economics, Vol. 23, 2001, pp. 405–425
Bahram Adrangi and coauthors examine the nonlinear behavior of crude oil, heating oil, and unleaded gasoline futures prices and investigate whether energy price movements exhibit low-dimensional chaotic behavior. Using daily futures-market data from the New York Mercantile Exchange (NYMEX), the study analyzes more than a decade of energy futures returns to distinguish true chaos from nonlinear volatility commonly observed in oil and energy markets.
The research applies several techniques for identifying chaos and nonlinear dependence, including correlation dimension analysis, the BDS test, and Kolmogorov entropy. It then evaluates whether the detected nonlinearities can instead be explained by ARCH, GARCH, EGARCH, and asymmetric GARCH volatility models, while controlling for seasonality and the time remaining until futures-contract maturity.
The results reveal strong nonlinear dependence in energy futures prices, but the evidence does not support low-dimensional chaos. Instead, ARCH-type volatility processes explain much of the nonlinear behavior in crude oil, heating oil, and gasoline futures. The analysis also demonstrates that controlling for seasonal price movements is important because failure to account for seasonality can produce misleading evidence of chaotic behavior.
The study also provides evidence concerning the Samuelson hypothesis and futures contract maturity. The GARCH results indicate that volatility in energy futures returns tends to increase as contracts approach maturity, with particularly clear statistical evidence for heating oil and unleaded gasoline futures. The principal findings remain robust after controlling for the major oil-price shocks of 1986 and 1991.
This research contributes to the literature on energy economics, crude oil prices, oil price volatility, energy futures markets, nonlinear time-series analysis, chaos theory, ARCH and GARCH models, EGARCH, commodity price forecasting, and futures market volatility. The findings have implications for energy-market researchers, commodity traders, risk managers, policymakers, and analysts seeking to understand and forecast volatility in crude oil and petroleum-product markets.
Alaska North Slope Crude Oil Price and the Behavior of Diesel Prices in California (2001)
Bahram Adrangi, Arjun Chatrath, Kambiz Raffiee, and Ronald D. Ripple
Energy Economics, Vol. 23, 2001, pp. 29–42
Bahram Adrangi and coauthors examine the price relationship between Alaska North Slope (ANS) crude oil and diesel fuel prices in Los Angeles, California. The study investigates price discovery, information transmission, and volatility spillovers between crude oil and refined petroleum-product markets on the U.S. West Coast. The analysis is particularly relevant to understanding how changes in crude oil prices are transmitted to downstream diesel fuel prices.
Using daily Alaska North Slope crude oil and Los Angeles diesel prices, the study applies time-series econometric methods including vector autoregression (VAR), error-correction models, cointegration analysis, Granger causality tests, impulse-response analysis, and a bivariate GARCH model. These methods allow the authors to examine both price transmission and volatility spillovers while accounting for time-varying volatility in energy markets.
The empirical results show a long-run cointegrating relationship between Alaska North Slope crude oil prices and Los Angeles diesel prices. More importantly, the evidence indicates a strong unidirectional causal relationship running from Alaska crude oil prices to California diesel prices. When the price spread between the two markets widens, the Los Angeles diesel price bears most of the adjustment required to restore the long-run relationship. Granger causality and impulse-response results confirm that changes in crude oil prices lead changes in diesel fuel prices rather than the reverse.
The bivariate GARCH results also identify significant volatility transmission from the Alaska North Slope crude oil market to the Los Angeles diesel market. Crude oil market shocks therefore transmit not only price information but also volatility to diesel fuel prices. The study finds no comparable volatility spillover in the opposite direction, reinforcing the conclusion that the crude oil market plays the leading role in the price-discovery process.
The findings suggest that the greater liquidity and contestability of the West Coast crude oil market allow crude oil prices to respond more rapidly to market information, while the less contestable California diesel market adjusts more slowly to crude oil price signals. The results also suggest that the conventional derived-demand relationship between input and output prices may not fully describe this market, since Alaska North Slope crude oil prices are found to drive changes in Los Angeles diesel prices.
This research contributes to the literature on energy economics, Alaska North Slope crude oil, California diesel prices, crude oil price transmission, petroleum markets, energy price discovery, oil price volatility, volatility spillovers, cointegration, Granger causality, VAR models, error-correction models, and bivariate GARCH models. The findings are relevant to energy economists, petroleum-market researchers, refiners, traders, risk managers, and policymakers interested in how crude oil price changes and volatility are transmitted to refined fuel markets on the U.S. West Coast.
The Impact of Margins in Futures Markets: Evidence from the Gold and Silver Markets (2001) — The Quarterly Review of Economics and Finance.
Arjun Chatrath, Bahram Adrangi, and Mary Allender
The Quarterly Review of Economics and Finance, Vol. 41, 2001, pp. 279–294
Bahram Adrangi and coauthors examine how margin requirements affect trading activity, trader behavior, and market participation in gold and silver futures markets. The study focuses particularly on whether the effect of futures margins changes as contracts approach maturity and what this reveals about the transaction and opportunity costs imposed on futures traders.
Using daily price, trading volume, open interest, and margin data for COMEX gold and silver futures from January 1986 through March 1995, together with Commodity Futures Trading Commission commitment-of-traders data, the research examines nearby and more distant futures contracts and distinguishes among commercial traders, large speculators, small traders, and spreaders.
The empirical analysis employs vector autoregression (VAR), two-stage least squares (2SLS), measures of extreme price movements, and GARCH-based conditional volatility. These methods allow the authors to investigate the relationships among futures margin requirements, market volatility, trading volume, open interest, contract maturity, and the composition of traders in the gold and silver futures markets.
The results show that trading activity becomes more sensitive to margin changes as futures contracts approach maturity. Gold and silver trading volume and open interest are relatively insensitive to margin changes when contracts are farther from expiration but respond more strongly in nearby contracts. This evidence suggests that futures margins impose important transaction costs rather than substantial opportunity costs on market participants.
Margin requirements also affect the composition of futures-market participants. Higher margins reduce trader commitments, with particularly strong effects on speculators and small traders, who tend to be more sensitive to liquidity constraints. The results indicate that increases in margins can reduce nonhedging participation relative to hedging activity, thereby changing the makeup of participants in gold and silver futures markets.
The study also finds that futures exchanges tend to increase margin requirements following periods of unusually high price volatility and reduce margins following periods of relative market stability. This supports the interpretation that margin adjustments serve an important risk-management and insurance function for futures exchanges rather than being a primary cause of market volatility.
This research contributes to the literature on financial economics, commodity futures markets, gold prices, silver prices, futures margin requirements, market volatility, trading volume, open interest, contract maturity, transaction costs, hedging and speculation, GARCH models, VAR models, and futures-market regulation. The findings are relevant to investors, commodity traders, risk managers, futures exchanges, financial economists, and regulators seeking to understand how margin requirements influence trading activity and participation in precious-metals futures markets.
The Demand for U.S. Air Transport Services: A Chaos and Nonlinearity Investigation (2001) — Transportation Research Part E.
Bahram Adrangi, Arjun Chatrath, and Kambiz Raffiee
Transportation Research Part E: Logistics and Transportation Review, Vol. 37, 2001, pp. 337–353
Bahram Adrangi and coauthors examine the behavior and forecasting of demand for U.S. airline and air transportation services. Using monthly aggregate data for the U.S. airline industry, the study investigates whether air transport demand exhibits nonlinear or chaotic behavior and whether nonlinear time-series models can improve the modeling and forecasting of airline service demand.
The analysis focuses on major measures of U.S. air transportation activity, including revenue passenger miles, freight revenue ton miles, and mail revenue ton miles. Because airline passenger and freight demand are highly seasonal and subject to domestic and international economic forces, accurately modeling air transportation demand can be difficult. The study therefore examines whether conventional linear time-series models adequately capture the dynamics of airline demand or whether nonlinear models provide a better representation.
The research applies statistical techniques designed to identify nonlinear dependence and deterministic chaos in time-series data. The analysis distinguishes between genuine chaotic dynamics and nonlinear behavior caused by time-varying volatility. GARCH-type models are then used to determine whether conditional volatility can explain the nonlinear patterns observed in U.S. airline service demand.
The results provide strong evidence of nonlinear dependence in U.S. air transportation demand but do not support the hypothesis that airline service demand is generated by a chaotic process. Instead, GARCH models successfully explain much of the nonlinear structure in the airline industry data. The findings therefore suggest that the complex movements in air transport demand are better characterized by changing conditional volatility than by deterministic chaos.
An important practical finding concerns forecasting. Within-sample forecasts generated by GARCH models outperform forecasts from simpler autoregressive models, indicating that explicitly modeling changing volatility can improve forecasts of U.S. air transportation demand. These results have implications for airlines, airports, transportation planners, government agencies, and other organizations that depend on reliable forecasts of passenger and freight activity.
This research contributes to the literature on air transportation economics, U.S. airline demand, airline passenger traffic, air freight demand, transportation forecasting, nonlinear time-series analysis, chaos theory, conditional volatility, ARCH and GARCH models, and airline industry planning. The findings demonstrate how nonlinear econometric methods can improve understanding and forecasting of passenger, freight, and mail demand in the U.S. air transportation industry.