The relationship between trading activity and stock market volatility: Does the volume threshold matter?

This paper examines whether trading activity conveys valuable information about changes in market volatility dynamics. We use a modelling framework, in which the market smoothly switches from one state to another, according to the volume level. Results show that large volume drives the high volatility regime for most of the markets, quite consistently with the disagreement-in-beliefs hypothesis. The volume decomposition into normal trading activity and surprising information arrival reveals a reverse threshold linkage for emerging markets. Results support the sequential information arrival hypothesis and highlight the key role of asymmetric information and thin trading in modelling the volume-volatility relationship. The proposed volume-based models provide significant forecast improvements over competing models and offer scope for investors to earn substantial profits.     

Moment conditions for Almost Stochastic Dominance

This study establishes necessary conditions for Almost Stochastic Dominance criteria of various orders. These conditions take the form of restrictions on algebraic combinations of moments of the probability distributions in question. The relevant set of conditions depends on the relevant order of ASD but not on the critical value for the admissible violation area. These conditions can help to reduce the information requirement and computational burden in practical applications. A numerical example and an empirical application to historical stock market data illustrate the moment conditions. The first four moment conditions in particular seem appealing for many applications.     

Modelling the risk–return relation for the S&P 100: The role of VIX

A significantly positive risk–return relation for the S&P 100 market index is detected if the implied volatility index (VIX) is allowed for as an exogenous variable in the conditional variance equation. This result holds for 4 alternative GARCH specifications, irrespective of the conditional distribution, and regardless of whether the conditional mean equation includes a constant term. This finding is robust to sub-samples, and to using VIX innovations to control for dividend yield and trading volume effects. Monte Carlo evidence suggests that if VIX is not included, the risk–return relation is more likely to be negative or weak, in line with several previous studies. If VIX is included, the distribution of the risk–return parameter has more than 99% of its mass in the area of positive values. We conclude that VIX carries important forward-looking information which improves the precision of the conditional variance estimation and, subsequently, reveals a significantly positive relation.     

Stock exchange mergers and return co-movement: A flexible dynamic component correlations model

The creation of a common cross-border stock trading platform is found, by use of a Flexible Dynamic Component Correlations (FDCC) model, to have increased long-run trends in conditional correlations between foreign and domestic stock market returns.     

A COMPARISON OF CONDITIONAL AND UNCONDITIONAL APPROACHES IN VALUE‐AT‐RISK ESTIMATION*

In this paper, we attempt to find the most important factor causing the differences in the performance of Value‐at‐Risk (VaR) estimation by comparing the performances of conditional and unconditional approaches. For each approach, we use various methods and models with different degrees of flexibility in their distributions including S_U‐normal distribution, which is one of the most flexible distribution functions. Our empirical results underscore the importance of the flexibility‐of‐distribution function in VaR estimation models. Even though it seems to be unclear which approach is better between conditional and unconditional approaches, it seems to be clear that the more flexible distribution we use, the better the performance, regardless of which approach we use.     

GARCH with omitted persistent covariate

This paper analyzes the effect of omitting a persistent covariate in the GARCH-X model. In particular, we show that if the relevant persistent covariate is omitted and the usual GARCH(1,1) model is fitted, the model will be estimated approximately as an IGARCH model. This may well explain the ubiquitous evidence of the IGARCH in empirical volatility analysis.     

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.     

Paradoxical price effects on insurance markets

In this paper we analyze a paradox phenomenon in certain insurance markets of some countries: in spite of there being strong competition, increasing prices can be found in some submarkets. As a reason for this, we need to note the special distribution of insurance products, a distribution primarily based on intermediaries — and, as a result, there is an increasing part for ‘intermediation’ costs within insurance products' cost structure due to competition for the intermediaries. We build an oligopolistic model assuming that the market is saturated, which is able to explain the overemployment of intermediaries and the high commissions involved. We analyze possible interventions and laws/regulations and suggest that the problem can be solved only with major regulation. Finally, we have come up with an empirical analysis to test the model.     

Non-linear causality between the stock and real estate markets of Western European countries: Evidence from rank tests

Using the non-parametric rank tests proposed by Breitung (2001), we set out in this study to determine whether any non-linear long-run equilibrium relationship exists between the stock and real estate markets of Western European countries. We go on to adopt the threshold error-correction model (TECM) to determine whether a similar relationship is discernible possibly non-linear functions of the log-price of these two markets. The findings clearly point to the existence of long-run unidirectional and bidirectional causality between the real estate market and the stock market in regions both above and below the threshold level. Finally, we find the existence of both wealth and credit price effects in the real estate markets and stock markets of Western European countries, which thereby offer financial institutions and individual investors in their construction of long-term investment portfolios within these two asset markets.     

Interest-Rate Reforms and Financial Deepening in Botswana: An Empirical Investigation

In this paper, we examine the impact of interest-rate reforms on financial deepening in Botswana, one of Africa's economic development success stories. We employ three proxies of financial deepening against deposit rate, a proxy for interest-rate reforms. The financial deepening in this study is defined as the increase in the relative size and role of the financial system in an economy. The empirical results of our study show that the impact of interest-rate reforms on financial deepening is sensitive to the variable used as a proxy for financial deepening. When the ratio of bank deposits to GDP (BD/GDP) is used as a proxy for financial deepening, the interest-rate reforms impact positively on the level of financial deepening. However, when the monetization variable (M2/GDP) and the ratio of the private sector credit to GDP (DCP/GDP) are used, the coefficient of the deposit rate in the financial deepening model turns out to be statistically insignificant. Overall, our results show that the positive impact of interest-rate reforms on financial deepening in Botswana is minimal. This outcome, though contrary to our expectations, is not surprising given the high level of population dependency in Botswana. Our results show that there is a strong negative relationship between the dependency ratio and financial deepening in Botswana.     

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.