South African stock return predictability in the context data mining: The role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.     

Modified Quasi‐Likelihood Ratio Test for Regime Switching

In this paper we propose a modified quasi‐likelihood ratio test of the null hypothesis of one regime against the alternative of two regimes in Markov regime‐switching models. The asymptotic distribution of the proposed test statistic is a simple function of Gaussian random variables, and the inference is no more complicated than in the standard case. Our simulations show that the proposed test has good finite sample size and power that are comparable to the quasi‐likelihood ratio test of Cho and White. We apply our test to stock returns and Japanese policy functions.     

The dependence structure between the Canadian stock market and the USD/CAD exchange rate: a copula approach

Abstract This paper investigates the dependence structure between the real Canadian stock returns and the real USD/CAD exchange rate returns, using the Symmetrized Joe‐Clayton (SJC) copula function. We estimate the SJC copula with monthly data over the period 1995:1 to 2006:12. Our results show significant asymmetric static and dynamic tail dependence between the real stock returns and the real exchange rate returns, such that the two returns are more dependent in the left than in the right tail of their joint distribution. We explain this asymmetric dependence in terms of an asymmetric interest rate policy by Canadian monetary authorities in response to changes in the real exchange rate during sub‐periods of falling and rising commodity prices.     

Volatility in the knowledge economy

Summary. We seek to explain the economic volatility of the last 6 years, in particular the rapid expansion and contraction of the knowledge sectors. Our hypothesis is that these sectors amplify the business cycle due to their increasing returns to scale, growing faster than others in an upswing and contracting faster in a downswing. To test this hypothesis we postulate a general equilibrium model with two sectors: one with increasing returns that are external to the firm and endogenously determined - the knowledge sector - and the other with constant returns to scale. We introduce a new measure of volatility of output, a real beta, and derive a resolving equation, from which we prove that the increasing return sectors exhibit more volatility then other sectors. We validate the main results on US macro economic data of real GDP by industry (2-3 digits SIC codes) of the 1977-2001 period, and provide policy conclusions.Received: 18 March 2002, Revised: 16 February 2004, JEL Classification Numbers: D5, D58, E10, L50, L52, O38, O51.Correspondence to: Graciela Chichilnisky     

Monetary environments and stock returns revisited: A quantile regression approach

We investigate the impact of monetary conditions on stock market returns at different points on the return distributions. Our results reveal no association between stock returns and monetary environments at the lower quantiles. At the upper quantiles, however, we find that expansive monetary conditions lead to significantly larger stock returns. The relationship between returns and monetary conditions at the upper quantiles is also found to be asymmetric, exhibiting a monotonic increase in responsiveness at successive quantiles.     

Stock exchange mergers and weak form of market efficiency: The case of Euronext Lisbon

This exploratory paper is among the first to examine the impact of stock exchange mergers on informational market efficiency. We focus on the merger of Bolsa de Valores de Lisboa e Porto (Portuguese Stock Exchange) with Euronext in 2002 (that created Euronext Lisbon). To investigate this question we perform numerous statistical tests: serial correlation test (ACF test), runs test, unit root test (Kwiatkowski, Philips, Schmidt, & Shin, 1992), multiple variance ratio test (Chow & Denning, 1993) and ranks and signs test (Wright, 2000).The results indicate that the Portuguese Equity Market is inefficient in weak form during pre-merger period implying that investors possessed an opportunity to earn abnormal returns though small in magnitude. The results, sensitive to the methodology used, indicate a mixed evidence of improvement in market efficiency during the post-merger period. Although the findings are mixed, yet most tests show a tendency of improved efficiency.     

TESTING FOR STATIONARITY WITH A BREAK IN PANELS WHERE THE TIME DIMENSION IS FINITE

In this paper, we consider the case of finite time dimension in the panel stationarity tests with structural breaks. By fixing T, the finite sample properties of the tests for both micro (T small and N large) and macro (both T and N large) panel data are generally greatly improved. More importantly, the derivation of the tests for finite T and , as opposed to joint asymptotic where N and simultaneously, avoids the imposition of the rate condition making the test valid for any (T, N) blend. Four models corresponding to the usual combination of breaks are considered. The asymptotic distributions of the test are derived under the null hypothesis and are shown to be normally distributed. Their moments for T fixed are derived analytically employing Ghazal’s corollary 1. The case with unknown breaks is also considered. The proposed tests have generally empirical sizes that are very close to the nominal size. The Monte Carlo simulations show that the power of the test statistics increases substantially with N and T.     

Public infrastructure,non‐cooperative investments,and endogenous growth

Two countries strategically invest in productive infrastructure within a general equilibrium model with endogenous growth. These public investments generate externalities. Dynamic analysis reveals that: (1) under constant returns, the two countries’ growth rates differ during the transition but are identical on the balanced growth path, (2) a country with decreasing returns can experience sustained growth provided that the other country grows at a positive constant rate, (3) cooperation does not necessarily lead to higher growth for each country, and it can increase or decrease the gap between countries’ growth rates depending on the countries’ consumption preferences regarding domestic and foreign goods.     

Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach

This paper presents a capital asset pricing model‐based threshold quantile regression model with a generalized autoregressive conditional heteroscedastic specification to examine relations between excess stock returns and “abnormal trading volume”. We employ an adaptive Bayesian Markov chain Monte Carlo method with asymmetric Laplace distribution to study six daily Dow Jones Industrial stocks. The proposed model captures asymmetric risk through market beta and volume coefficients, which change discretely between regimes. Moreover, they are driven by market information and various quantile levels. This study finds that abnormal volume has significantly negative effects on excess stock returns under low quantile levels; however, there are significantly positive effects under high quantile levels. The evidence indicates that each market beta varies with different quantile levels, capturing different states of market conditions.     

The nonlinear effects of expected and unexpected components of monetary policy on the dynamics of REIT returns

This paper examines empirically whether the expected and unexpected components of monetary policy have nonlinear impacts on the dynamics of REIT returns. Empirical results find the nonlinear response of REIT returns to expected and unexpected components of monetary policy. The unexpected component of monetary policy plays a more prominent role in influencing REIT returns than does the expected component of monetary policy. Specifically, unexpected contractionary monetary policy has a significantly adverse impact on REIT returns, and the adverse effect in a bust market is stronger than in a boom market. In addition, the unexpected monetary policy will also affect the boom-bust dynamics of REIT returns through its effect on the time-varying transition probability matrix. The tightening of the expected and unexpected components of monetary policy will enhance the probability that the REIT market will stay in the bust regime.     

Nonlinear dynamics in CEE stock markets indices

We investigate the existence of nonlinearities in the dynamics of the returns of stock markets indices from CEE economies. We use several types of nonlinear tests. We discuss the implications of the results with respect to the efficient market hypothesis.     

The folk theorem for irreducible stochastic games with imperfect public monitoring

This paper introduces stochastic games with imperfect public signals. It provides a sufficient condition for the folk theorem when the game is irreducible, thus generalizing the results of Dutta (1995) [5] and Fudenberg, Levine, and Maskin (1994) [9]. To do this, the paper extends the concept of self-generation (Abreu, Pearce, and Stacchetti, 1990 [1]) to “return generation,” which explicitly tracks actions and incentives until the next time the state returns to its current value, and asks that players not wish to deviate given the way their continuation payoffs from the time of this return depend on the public signals that have been observed.     

TRADING VOLUME,VOLATILITY, AND GARCH EFFECTS IN THE SOUTH KOREAN WON/US DOLLAR EXCHANGE MARKET: EVIDENCE FROM CONDITIONAL QUANTILE ESTIMATION*

Under the MDH, this paper investigates the asymmetry in the positive relationship between unexpected volume and volatility, and whether the unexpected volume series as a proxy for the rate of information arrival absorbs the GARCH effects. This is achieved by applying a quantile regression approach to the won/dollar exchange market with reliable data on trading volumes. Interestingly, the results show that in a freely floating exchange rate system, the positive relationship increases as exchange rate returns are higher. Contrary to previous studies, despite a significantly positive relationship, the inclusion of volumes alone does not reduce volatility persistence at medium or high levels of returns. In addition, the reform of the South Korean exchange rate system had an impact on the relationship, which occurred in response to a financial crisis.     

流动性与资产定价:基于我国股市资产换手率与预期收益的实证研究

流动性与资产定价是目前金融研究的热点之一 (O’Hara,2 0 0 3 )。本文通过检验交易频率零假设和交易成本备择假设 ,深入分析我国股市流动性与资产定价的理论与经验关系 ,发现 :我国股市存在显著的流动性溢价 ,换手率低、交易成本高且流动性小的资产具有较高的预期收益 ;产生流动性溢价的原因是交易成本而不是交易频率 ;与国外股市相似 ,小企业收益率高于大企业 ,价值股收益率高于成长股。因此 ,我国股市并非令人无法捉摸 ,流动性、规模和价值效应都是资产定价的因素     

Investment dynamics with common and private values

We study a dynamic investment game with two-dimensional signals, where each firm observes its continuously distributed idiosyncratic cost of investment and a discrete signal correlated with common investment returns. We demonstrate that the one-step property holds and provide an equilibrium existence/characterization result. “Reversals” are possible, where a large number of firms investing in a given round becomes bad news about investment returns. Welfare is compared to static and rigid-timing benchmarks, and computed for large economies.     

Modeling hedge fund exposure to risk factors

This paper examines the adjustment dynamics of hedge fund returns and studies their exposure to risk factors in a nonlinear framework for several types of strategies over the last two decades. Nonlinearity is justified by distortions due to the use of short selling, leverage, derivatives and illiquid assets for hedge fund strategies. Among nonlinear models, switching regime (STR) models are applied to reproduce the dynamics of hedge fund returns. This nonlinear multivariate modeling has the advantage of capturing the time-varying exposure of hedge fund strategies to risk factors, and of specifying the asymmetric relationship between hedge fund returns and risk. The findings are interesting and provide several contributions to the hedge fund literature. First, we show that the dynamics of hedge fund returns exhibit significant asymmetry and nonlinearity, indicating that they evolve and vary asymmetrically in accordance with stages in financial cycles. Second, hedge fund exposure to risk factors also varies over time, depending on the strategy and the regime. Finally, our modeling captures the most important changes in hedge fund exposure to risk factors induced by the recent global financial crisis (2008–2009).     

Can volume predict Bitcoin returns and volatility? A quantiles-based approach

Prior studies on the price formation in the Bitcoin market consider the role of Bitcoin transactions at the conditional mean of the returns distribution. This study employs in contrast a non-parametric causality-in-quantiles test to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the whole of their respective conditional distributions. The nonparametric characteristics of our test control for misspecification due to nonlinearity and structural breaks, two features of our data that cover 19th December 2011 to 25th April 2016. The causality-in-quantiles test reveals that volume can predict returns – except in Bitcoin bear and bull market regimes. This result highlights the importance of modelling nonlinearity and accounting for the tail behaviour when analysing causal relationships between Bitcoin returns and trading volume. We show, however, that volume cannot help predict the volatility of Bitcoin returns at any point of the conditional distribution.     

QUANTILE FORECASTS OF FINANCIAL RETURNS USING REALIZED GARCH MODELS*

This article applies the realized generalized autoregressive conditional heteroskedasticity (GARCH) model, which incorporates the GARCH model with realized volatility, to quantile forecasts of financial returns, such as Value‐at‐Risk and expected shortfall. Student's t‐ and skewed Student's t‐distributions as well as normal distribution are used for the return distribution. The main results for the S&P 500 stock index are: (i) the realized GARCH model with the skewed Student's t‐distribution performs better than that with the normal and Student's t‐distributions and the exponential GARCH model using the daily returns only; and (ii) using the realized kernel to take account of microstructure noise does not improve the performance.     

Dynamic modeling under linear-exponential loss

We develop a methodology of parametric modeling of time series dynamics when the underlying loss function is linear-exponential (Linex). We propose to directly model the dynamics of the conditional expectation that determines the optimal predictor. The procedure hinges on the exponential quasi-maximum likelihood interpretation of the Linex loss and nicely fits the multiplicative error modeling framework. Many conclusions relating to estimation, inference and forecasting follow from results already available in the econometric literature. The methodology is illustrated using data on United States GNP growth and Treasury bill returns.     

Is market liquidity less resilient after the financial crisis? Evidence for US Treasuries

Understanding market liquidity resilience, i.e. the capacity of liquidity to absorb shocks, of United States Treasuries is crucial from a financial stability standpoint. The conventional resilience measure has limitations due to the use of the liquidity level. We propose a new complementary approach to analyze resilience based on liquidity volatility. For this purpose, we focus on the link between returns volatility and liquidity volatility, which is a relatively unexplored field. We fit a bivariate conditional correlation (CC-) GARCH model for the 10-year bond returns and five liquidity indicators from January 2003 to June 2016 to analyze persistence and spillovers between these variables in a parsimonious way. We find that after the crisis, spillovers between liquidity volatility and returns volatility are higher, feedback loops are more likely and volatility persistence is lower, which is consistent with a lower resilience. Our results help to explain recent episodes of high volatility in this market.