AESTIMATIO

Health care costs represent a large and growing component of business and consumer expenditures in the US. Medical inflation represents these costs, and it differs from aggregate inflation and other market factors with respect to its rate of growth, statistical properties and the extent to which it can be hedged by households and firms. Using multiple model specifications for the 25-year period from 1985 to 2009, we find medical inflation is robustly priced in the cross-section of US stock returns. It commands a risk premium of between 31 and 51 basis points per annum per unit change in beta. Medical inflation is also unique in that it represents the only inflationary component that robustly explains the cross-section of stock returns in this manner and is not subsumed by other common factors in the literature. These results quantify the health care industry’s unique and significant role in the US economy and stock market, further rationalizing the substantial attention this industry receives.

Impact of US unconventional monetary policy on dynamic stock-bond correlations: Portfolio rebalancing and signalling channel effects

The stability of the financial cycle is paramount for the effective formulation and implementation of macroprudential policy in South Africa. The South African Reserve Bank (SARB) and the Prudential Authority strive to mitigate excessive fluctuations in the financial cycle, recognising that a stable cycle provides more reliable signals for financial sector activity and anchors macroprudential policy decisions. However, the tightening of macroprudential policy by the SARB and the Prudential Authority during the post-2009 recovery period, despite mild signs of recovery from the global financial crisis, raises concerns about the stability of the South African financial cycle. This study aims to construct a financial cycle volatility index to assess its stability and identify the key macroeconomic drivers of financial instability in South Africa. Employing monthly data from 1970 to 2024, the study utilises a dynamic conditional correlation model and a Markov switching regression model to analyse the relationship between macroeconomic variables and financial stability. The findings reveal heightened financial cycle volatility around crisis periods and demonstrate that macroeconomic variables such as exchange rate fluctuations, price level changes, and implementing monetary and macroprudential policies can significantly increase financial instability. These results suggest a need for proactive and aggressive macroprudential policy measures in the years preceding potential crises. Moreover, the study’s findings emphasise the importance of considering macroeconomic conditions when calibrating financial cycle policies.

This study examines the interdependence of US dollar exchange rates expressed in five emerging currencies. Focusing on different phases of the global financial and European sovereign debt crises, the aim of this paper is to examine how the dynamics of correlations between emerging exchange markets evolved from January 04, 2000 to July 11, 2014. To this end, we adopt a dynamic conditional correlation model into a multivariate Fractionally Integrated Asymmetric Power ARCH framework, which accounts for long memory, power effects, leverage terms and time varying correlations. The empirical findings indicate a general pattern of decrease in exchange rates correlations across the phases of the global financial crisis and the European sovereign debt crisis, suggesting the depreciation against US dollar and different vulnerability of the currencies. Moreover, our analysis supports the existence of a general pattern of increase in dynamic correlations across several phases of the two crises, indicating the existence of a “contagion effect”.

Purpose – This paper aims to employ GARCH-class models (GARCH, IGARCH and CGARCH) to estimate the volatility persistence on crude oil, US, Gulf Corporation Council (GCC), Brazil, Russia, India and China (BRIC) stock markets. Also, the paper investigates the volatility spillover and the dynamic conditional correlation between crude oil, US stock index and stock indices of GCC and BRIC countries. The results prove a high degree of volatility persistence in the crude oil and stock markets. Based on the BEKK-GARCH and DCC-GARCH results, the paper finds strong evidence of the contagion effect of the oil shock and US financial crisis of 2008 on GCC and BRIC stock markets. Design/methodology/approach – In the beginning, the paper uses univariate GARCH models to estimate the volatility persistence of the oil market, US stock market, and GCC and BRIC stock markets. Then, the paper uses a trivariate BEKK-GARCH model of Malik and Hammoudeh to examine the volatility spillover between oil market, US stock market and stock markets for GCC and BRIC countries. Finally, the paper analyses the dynamic conditional correlation between US market and each stock market of GCC and BRIC countries using the DCC-GARCH model. Also, the paper estimates the dynamic conditional correlation between oil market and all stock markets. Findings – The results indicate the contagion effect of the oil shock and US financial crisis of 2008 on the GCC stock markets which are among the most important oil-exporting countries and also on BRIC stock markets which are among the emergent countries which are characterized by high economic growth level. Originality/value – The contribution of this paper is to investigate the existence of contagion effect between oil market, US stock market and two panels of emerging stock markets which have different economic characteristics, the GCC and BRIC countries, during the oil shock and US financial crisis period of 2008-2009.

US Stock return predictability with high dimensional models

Purpose – The aim of this study is to empirically examine the impact of COVID-19 on the dynamic correlation among the stock markets of Turkey, the United States, and China, and demonstrate the effects of search-based investor attention and newspaper-based infectious disease equity market volatility on the correlation among these markets. Design/methodology/approach – In this study, VAR(1)-DCC-GARCH(1,1) methodology is employed to examine the changes in the variances and dynamic correlations among markets after the outbreak of Covid-19. Then, least square regression analyses are done to examine the influences of search-based sentiment and newspaper-based infectious disease equity market volatility on the correlations obtained from VAR(1)-DCC-GARCH(1,1) model. Findings – Findings of this study demonstrate that the integration of the China stock market with Turkey and the US markets diminishes after the outbreak of a pandemic, while the dynamic correlation between the US and Turkey stock markets does not change significantly after Covid-19. Moreover, we present that increase in the media coverage of the Covid-19 related equity market volatility and search-based sentiment have explanatory power on the correlation between Turkey and US markets, especially after the Covid-19 is pronounced as a pandemic. Likewise, individual attention to Covid-19 negatively influences the correlation between Turkey and China. Discussion – This study presents that stock market integration is highly related to human health. Therefore, the results of this study offer inputs to investors and policymakers that can be used during infectious disease periods. Moreover, as public attention has a significant impact on the international stock market correlation, media coverages and information releases during low frequency, high severity events should be managed wisely.

Predictable variation and profitable trading of US equities: a trading simulation using neural networks

Implied volatility relationships between crude oil and the U.S. stock markets: Dynamic correlation and spillover effects

Purpose– This study aims to analyze the spillover effects of external real and financial shocks on the Korean economy during January 2000 and December 2019. Design/methodology/approach–Using the Bayesian VAR model, we analyze whether there were substantial changes in the effects of U.S. monetary policy and macroeconomic shocks after the global financial crisis. Findings–First, an increase in U.S. industrial production had long-term positive effects on Korea’s real and financial variables, except for Korea’s call rate. Second, the U.S. monetary policy shock measured by an increase in federal fund rate had significant negative effects on Korea’s current account balance and stock prices. Third, the Chicago Board Options Exchange (CBOE) Volatility Index (VIX) shock had a negative effect on Korea’s stock prices and the value of Korean won against the U.S. dollar. To sum it up, the spillover effects of external shocks have increased after the global financial crisis, implying that decoupling of domestic and foreign economies may be halted or reversed. Research implications or Originality– As the impact of the global financial crisis subsided and the global economy recovered, the spillover effects of external shocks on the domestic economy became also more significant. It should be noted that policy effectiveness may vary depending on interaction of internal and external economic conditions under the increasing uncertainty of the global economy.

This article investigates whether the HML, SMB along with the long-term reversal and the momentum factors exhibit both in-sample and out-of-sample forecasting ability for the US stock returns. Our findings suggest that these factors contain significantly more information for future stock market returns than the typically employed financial variables. We also go one step further and test whether these variables can proxy for the aforementioned factors and find that the default spread and to a lesser extent the term spread contain important information for the evolution of the factors examined. Finally, we show that appropriate decompositions of the factors in their size and value components can enhance predictability.

PurposeThe purpose of this paper is to investigate the time series behavior of co‐movements among 11 European real estate securities markets, with each other as well as between country‐averages, over the sample period from January 1999 to January 2010 by utilizing the asymmetric dynamic conditional correlation (ADCC) technique, long‐memory tests and multiple structural break methodology.Design/methodology/approachFirst the ADCC from the multivariate GJR‐GARCH model is used to estimate the pair‐wise conditional correlations between the 11 securitized real estate markets. Then, the 11 country‐average conditional correlation series is subject to a battery of four long‐memory tests to form an “on the balance of evidence” picture; the semi‐parametric Geweke and Porter‐Hudak procedure and Robinson test, as well as the non‐parametric Hurst‐Mandelbrot R/S and Lo's modified R/S tests. Finally, the Bai and Perron's multiple structural break methodology seeks to test whether the average conditional correlations are subject to regime switching via the detection of breaks in the co‐movements of real estate securities returns.FindingsLow to moderate conditional correlations are found for these European real estate securities market and a higher level of correlation in the aftermath of the global financial crisis. The long‐memory correlation effect is present for nine European real estate securities markets. In addition, the conditional correlations are subject to regime switching with two structural breaks in four country‐average correlation series. Across the regimes, a higher level of correlation is linked to a higher level of volatility and a lower level of return, and this happened around the global financial crisis period.Research limitations/implicationsThe findings that national real estate securities correlations exhibit time‐varying and asymmetric behavior can help investors understand how real estate securities will co‐move in different market scenarios (e.g. “crisis” and “non‐crisis” times). Moreover, the process of dynamic covariance analysis and forecasting (the ultimate objective in portfolio management) should not rely too much on short‐term autoregressive moving average models. Instead, a combination of some appropriate long‐range dependence models and regime‐switching specifications is needed.Originality/valueThis paper offers useful insights into the time series behavior of average dynamic conditional correlations in European public property markets.

In this paper, we examine the dynamics of monetary policy in Nigeria with Bayesian approach to a vector autoregression (VAR). We construct and estimate Bayesian Vector Autoregression with Stochastic Volatility (BVAR-SV) model and extract important policy inputs from the model. Nigeria economy is unstable and it is a known fact that changes to monetary policy affects performance of some macroeconomic variables. The BVAR has the ability to capture sudden changes and nonlinearities arising from the interaction among macroeconomic variables and associated shocks. The study uses monthly data during the period 2003M01 till 2023M12 with three macroeconomic variables namely; inflation rate, money supply, and interest rate. A Markov Chain Monte Carlo algorithm that allows for Bayesian estimation and prediction is employed. Results show that there is strong evidence of monetary policy playing a significant role in explaining the dynamics of interest rate while the impulse responses for the variables to a monetary policy shock do change significantly over time. Also, the monetary policy exert less significant influence in terms of money supply and inflation than interest rate in explaining the dynamics in of monetary policy. It is recommended that BVAR should be also be extended to other macroeconomic variables to examine the effects on monetary policy dynamics.

This paper investigates which US stock price index is strongly influenced by the Japanese stock markets. Our empirical tests as to the time-varying correlations derived from a multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model reveal the following evidence. First, (1) we clarify that all US stock returns of the Dow Jones, S&P 500, and NASDAQ Composite indices are statistically significantly positively influenced by the returns of TOPIX and the Nikkei 225. Moreover, we also find that (2) the changes of the NASDAQ-S&P 500 (NS) ratio are statistically significantly positively influenced by the returns of TOPIX and the Nikkei 225 while the changes of the Dow Jones-S&P 500 (DS) ratio and the Dow Jones-NASDAQ (DN) ratio are statistically significantly negatively influenced by the returns of TOPIX and the Nikkei 225. These relations between the returns of TOPIX and the Nikkei 225 and the US stock price index ratios are clearly shown in all three sample periods employed in this paper. Hence our results suggest that, in our analyzing periods, (3) the NASDAQ index is most strongly positively connected with the Japanese stock markets.

This paper studies the volatility spillover and dynamic correlation between EU emission allowance (EUA) prices and energy prices by considering three energy commodities, including oil, gas, and coal. The asymmetric BEKK model is employed for multi-phase analysis of EU ETS, yet only a little empirical evidence backing up the existence of volatility spillover between EU ETS and energy markets, i.e., the establishments of the EU ETS may not effectively limitation and influence energy markets. The time-varying conditional correlation between EUA and each of energy prices is analyzed. The dynamic correlation shows there is a relatively stable, positive correlation between the EUA and Brent oil, natural gas. However, modeling the dynamics correlation also suggests that the correlation between the EUA and the natural gas, coal became weaker and more volatile since second and third phases, especially after the Global Financial Crisis in 2008, which may indicate that the demand reduction in emission allowances caused by the economic slowdown far exceeds the reduction in the annual restraint of EU ETS.