A New Variance Ratio Test of Random Walk in Emerging Markets: A Revisit

Using a nonparametric variance ratio (VR) test, we revisit the empirical validity of the random walk hypothesis in eight emerging markets in the Middle East and North Africa (MENA). After correcting for measurement biases caused by thin and infrequent trading prevalent in nascent and small stock markets, we cannot reject the random walk hypothesis for the MENA markets. We conclude that a nonparametric VR test is appropriate for emerging stock markets, and argue that our findings can reconcile previously contradictory results regarding the efficiency of MENA markets.     

Persistence characteristics of the Chinese stock markets

Using advanced signal processing, this paper identifies the lack of ergodicity, stationarity, independence and the degree of persistence of the Shanghai (SHI) stock market and Shenzhen A shares (SZI) and B shares (SZBI), before and after the various deregulations and reregulations. Their lack of stationarity and ergodicity are ascribed to (1) the initial interventions in these stock markets by the Chinese government by imposing various daily price change limits, and (2) the changing trading styles, after the Chinese government left these equity markets to develop by themselves. The SHI, SZI, and SZBI are moderately persistent with Hurst exponents slightly greater than the Fickian 0.5 of the Geometric Brownian Motion. These stock markets were considerably more persistent before the deregulations, but they now behave more like Geometric Brownian Motions, i.e., efficiently. Thus, the Chinese stock markets are gradually and properly being integrated into one Chinese stock market. Our results are consistent with similar empirical findings from Latin American, European, and other Asian emerging financial markets.     

Persistence characteristics of Latin American financial markets

The financial rates of return from Latin American stock and currency markets are found to be non-normal, non-stationary, non-ergodic, and long-term dependent, i.e., they have long memory. The degree of long-term dependence is measured by monofractal (global) Hurst exponents from wavelet multiresolution analysis (MRA). Scalograms and scalegrams provide the respective visualizations of these wavelet coefficients and the power spectrum of the rates of return. The slope of the power spectrum identifies the Hurst exponent and thereby the degree of time-scaling dependence that cannot be determined by Box–Jenkins type, stationarity-based, time series analysis. Our long-term dependence and time–frequency scaling results are consistent with similar empirical findings from American, European, and Asian financial markets. They extend the domain of the empirical investigation of the dynamics and risk characteristics of the global financial markets and refute the hypothesis of perfectly efficient financial markets.     

Why Do Price Limits Exist in Stock Markets? A Manipulation‐Based Explanation

Numerous stock market regulators around the world impose daily price limits on individual stock price movements. We derive a simple model that shows that price limits may deter stock market manipulators. Based on our model's implications, we predict that regulators impose price limit rules for markets where the likelihood of manipulation is high. We present empirical evidence consistent with this hypothesis. Our study is the first to formally propose a manipulation‐based rationale for the existence of price limits in stock markets.     

Wavelet multiresolution analysis of high-frequency Asian FX rates, Summer 1997

Foreign exchange (FX) pricing processes are nonstationary: Their frequency characteristics are time dependent. Most do not conform to Geometric Brownian Motion (GBM), because they exhibit a scaling law with Hurst exponents between zero and 0.5 and fractal dimensions between 1.5 and 2. Wavelet multiresolution analysis (MRA), with Haar wavelets, is used to analyze these time and scale dependencies (self-similarity) of intraday Asian currency spot exchange rates. We use the ask and bid quotes of the currencies of eight Asian countries (Japan, Hong Kong, Indonesia, Malaysia, Philippines, Singapore, Taiwan, and Thailand) and, for comparison, of Germany for the crisis period May 1, 1998-August 31, 1997, provided by Telerate (U.S. dollar is the numéraire). Their time-scale-dependent spectra, which are localized in time, are observed in wavelet scalograms. The FX increments are characterized by the irregularity of their singularities. Their degrees of irregularity are measured by homogeneous Hurst exponents. These critical exponents are used to identify the global fractal dimension, relative stability, and long-term dependence, or long-term memory, of each Asian FX series. The invariance of each identified Hurst exponent is tested by comparing it at varying time and scale (frequency) resolutions. It appears that almost all investigated FX markets show antipersistent pricing behavior. The anchor currencies of the D-mark and Japanese Yen (JPY) are ultraefficient in the sense of being most antipersistent or “fast mean-reversing.” This is a surprising result because most financial analyst either assume neutral or persistent behavior in the financial markets, based on earlier research by Granger in the 1960s. This is a pedagogical paper explaining the most rational methodology for the identification of long-term memory in financial time series.     

Modeling Time-Varying Volatility and Expected Returns: Evidence from the GCC and MENA Regions

The aim of this study is to investigate empirically the underlying nexus of stock market returns and volatility in the Gulf Cooperation Council (GCC) countries and Middle East and North Africa (MENA) region by using the GARCH-M model. We find that volatility is time-varying in all countries, which indicates substantial variation in the degree of risk across time. However, we do not find empirical support that this time-varying volatility significantly explains expected returns, except in the case of Kuwait, United Arab Emirates, and the MENA region portfolio. Our findings show that stock return volatility is negatively correlated with stock returns in these three markets under the assumption of investor risk aversion. This lends some support to the hypothesis of a volatility-driven negative relationship in the literature. The policy implications of our results are discussed.     

Dynamic linkages among equity markets in the Middle East and North African countries

The relaxation of security laws and regulations in emerging markets in the Middle East and North Africa (MENA) provides abundant opportunities for foreign investors. These markets exhibit high-expected returns and substantial volatility. In this paper, we investigate the lead/lag relationship between the MENA countries and regions. We find no market causality or spillover from one country to another in the North Africa region. Our results for the Levant region reveal that there are linkages between stock markets in this region. The results for the Gulf Cooperation Council (GCC) region show that there is more interaction and linkage in the GCC region than in the North Africa and Levant regions. An unexpected result is that UAE's stock market leads all the markets in this region. Finally, we investigate linkages among the three regions. We find that GCC influences the other two regions.     

Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis

We divided the whole series of Shenzhen stock market into two sub-series at the criterion of the date of a reform and their scale behaviors are investigated using multifractal detrended fluctuation analysis (MF-DFA). Employing the method of rolling window, we find that Shenzhen stock market was becoming more and more efficient by analyzing the change of Hurst exponent and a new efficient measure, which is equal to multifractality degree sometimes. We also study the change of Hurst exponent and multifractality degree of volatility series. The results show that the volatility series still have significantly long-range dependence and multifractality indicating that some conventional models such as GARCH and EGARCH cannot be used to forecast the volatilities of Shenzhen stock market. At last, the abnormal phenomenon of multifractality degrees for return series is discussed. The results have very important implications for analyzing the influence of policies, especially under the environment of financial crisis.     

Equity market integration in emerging Balkan markets

This paper examines long-run relationships among five Balkan emerging stock markets (Turkey, Romania, Bulgaria, Croatia, Serbia), the United States and three developed European markets (UK, Germany, Greece), during the period 2000-2009. Conventional, regime-switching cointegration tests and Monte Carlo simulation provide evidence in favour of a long-run cointegrating relationship between the Balkan emerging markets within the region and globally. Moreover, we apply the Asymmetric Generalized Dynamic Conditional Correlation (AG-DCC) multivariate GARCH model of Cappiello et al. (2006), in order to capture the impact of the 2007-2009 financial crisis on the time-varying correlation dynamics among the developed and the Balkan stock markets. Results show that stock market dependence is heightened, supporting the herding behaviour during the 2008 stock market crash period. Our findings have important implications for international portfolio diversification and the effectiveness of domestic policies, as these emerging markets are exposed to external shocks.     

Predicting bear and bull stock markets with dynamic binary time series models

Despite the voluminous empirical research on the potential predictability of stock returns, much less attention has been paid to the predictability of bear and bull stock markets. In this study, the aim is to predict U.S. bear and bull stock markets with dynamic binary time series models. Based on the analysis of the monthly U.S. data set, bear and bull markets are predictable in and out of sample. In particular, substantial additional predictive power can be obtained by allowing for a dynamic structure in the binary response model. Probability forecasts of the state of the stock market can also be utilized to obtain optimal asset allocation decisions between stocks and bonds. It turns out that the dynamic probit models yield much higher portfolio returns than the buy-and-hold trading strategy in a small-scale market timing experiment.     

Does financial market liberalization increase the degree of market efficiency? The case of the Athens stock exchange

In this paper we assess if the financial market liberalization introduced in the beginning of the 1990s in Greece has changed the degree of market development (efficiency) by studying time-varying global Hurst exponents. Our results suggest that changes in financial market liberalization have important positive implications on the degree of development of stock markets. These results have important policy implications for the development of stock markets around the world.     

Commodity and equity markets: Some stylized facts from a copula approach

In this paper, we propose to identify the dependence structure that exists between returns on equity and commodity futures and its development over the past 20 years. The key point is that we do not impose any dependence structure, but let the data select it. To do so, we model the dependence between commodity (metal, agriculture and energy) and stock markets using a flexible approach that allows us to investigate whether the co-movement is: (i) symmetrical and frequent, (ii) (a) symmetrical and mostly present during extreme events and (iii) asymmetrical and mostly present during extreme events. We also allow for this dependence to be time-varying from January 1990 to February 2012. Our analysis uncovers three major stylised facts. First, we find that the dependence between commodity and stock markets is time-varying, symmetrical and occurs most of the time (as opposed to mostly during extreme events). Second, not allowing for time-varying parameters in the dependence distribution generates a bias towards an evidence of tail dependence. Similarly, considering only tail dependence may lead to false evidence of asymmetry. Third, a growing co-movement between industrial metals and equity markets is identified as early as 2003; this co-movement spreads to all commodity classes and becomes unambiguously stronger with the global financial crisis after Fall 2008.     

Analysts and anomalies

Analysts' price targets and recommendations contradict stock return anomaly variables. Using an index based on 125 anomalies, we find that analysts' annual stock return forecasts are 11% higher for anomaly-shorts than for anomaly-longs. Anomaly-shorts’ return forecasts are excessively optimistic, exceeding realized returns by 34%. Recommendations also tend to be more favorable for anomaly-shorts, although this result varies across anomaly types. Consistent with analysts' slowly incorporating anomaly information, anomalies forecast revisions in both price targets and recommendations. Our findings imply that investors who follow analysts' actionable information contribute to mispricing.     

Existence of long memory in crude oil and petroleum products: Generalised Hurst exponent approach

This study examines the presence of long-run dependence in a variety of crude and refined energy spot markets during the 1986–2018 period using the time-varying generalised Hurst exponent. Our results indicate that the weak-form efficiency in energy spot markets is clearly time-varying, with USGC(U.S. Gulf Coast Conventional Gasoline) Diesel Fuel the most efficient and Propane the least. An important finding is that after the subprime crisis, the persistence of energy spot market products has increased. Overall, our finding highlights that the time-varying model is preferable to the time-constant one since the former can capture time-varying efficiency, which heavily depends on a country’s predominant economic and political conditions.     

MENA stock market volatility persistence: Evidence before and after the financial crisis of 2008

The global financial crisis began with a financial meltdown in the United States in early 2008 and then it had spread to the rest of the world. In this paper we test whether the MENA equity market volatility presents a different behavior before and after the financial crisis of 2008. Using long range dependence techniques we test for long memory in the returns, absolute and squared returns of the MENA equity markets. We subject the series to unit root tests that allow for structural breaks and use the Bai and Perron (1998, Econometrica, 66, 47; 2003a, J. Appl. Econometrics, 6, 72; 2003b, Econometrics J., 18, 1) to test for multiple breaks in the mean returns. The results indicate that the volatility measures represented by absolute and squared returns show evidence of long memory for the full and subsample periods, while the returns show a weak evidence of long memory. Considering the shift dates and corresponding to the 2008 financial crisis, the returns and volatility measures display less evidence of long memory in the after crisis period as opposed to the before crisis period. The change in the returns and volatility dynamics of these markets was due to financial and economic conditions that took place in the MENA region after the crisis.     

Multiscale hedge ratio between the Australian stock and futures markets: Evidence from wavelet analysis

This paper examines the relationship between the Australian stock and futures markets over various time horizons. In contrast to methods employed in previous studies, wavelet analysis allows us to decompose data into various time scales. Using this technique and the Hurst exponent, we find that the Australian stock and futures markets are antipersistent. The wavelet correlation between the two markets varies over investment horizons, but remains very high. Furthermore, the magnitude of the correlation increases as the time scale increases, indicating that the stock market and the futures market of the All Ordinaries Index are found to be not fundamentally different. The hedge ratio increases as the wavelet time scale increases. In addition, the effectiveness of hedging strategies initially increases with the hedging horizon.     

The impacts of climate policy uncertainty on stock markets: Comparison between China and the US

The interplay between climate policy uncertainty and stock market performance has emerged as a pressing research question in light of the challenges posed by climate change to financial markets. This paper measures China's daily and monthly climate policy uncertainty (CPU) from Jan 2000 to Mar 2022 based on Chinese news data for the first time. Then, the nonlinear and lag impacts of the US CPU and China's CPU on the return, volatility, correlation and tail dependence of China's and US stock markets are investigated and compared by adopting copula function and the distribution lag nonlinear model (DLNM). The data of stock markets includes the Shanghai Composite Index (SSCI) and NASDAQ from Jan 2000 to Mar 2022 from the Choice database, and the Shenzhen Composite Index (SCI) and S&P 500 are used for the robustness test. The empirical results indicate that (1) the growth trend of China’s CPU index is similar to that of the US. However, there are significant differences between the impacts of these two CPUs on stock markets. (2) For China, high CPU decreases current stock market return and increases volatility but decreases it in the future. It could also increase the upper tail dependence between China’s and the US stock markets’ volatilities in current period. (3) For the US, CPU decreases stock market return in the short term but increases it in the long term. High CPU increases volatility in short term, decreases volatility in 5 months and increases it again after 6 months. Both low and high CPU could increase the correlation between China's and US stock markets' volatilities.     

Nonlinear duration dependence in stock market cycles

We reexamine duration dependence in stock market cycles using a generalized Weibull model. Recent empirical work by Cochran and DeFina [Cochran, S. J., & DeFina, R. H. (1995). Duration dependence in the U.S. stock market cycle: A parametric approach. Applied Financial Economics, 5, 309-318.], who use a chronology of stock market cycles to estimate a Weibull hazard model, shows duration dependence in stock prices. They find evidence of duration dependence in prewar market expansions and postwar market contractions. We update their postwar sample, then use a more flexible model that finds evidence of duration dependence for all prewar and postwar samples. The generalized Weibull model is shown to be statistically superior to the conventional Weibull model for all samples except prewar expansions.     

Time series analysis for financial market meltdowns

There appears to be a consensus that the recent instability in global financial markets may be attributable in part to the failure of financial modeling. More specifically, it is alleged that current risk models have failed to properly assess the risks associated with large adverse stock price behavior. In this paper, we first discuss the limitations of classical time series models for forecasting financial market meltdowns. Then we set forth a framework capable of forecasting both extreme events and highly volatile markets. Based on the empirical evidence presented in this paper, our framework offers an improvement over prevailing models for evaluating stock market risk exposure during distressed market periods.