The impact of transaction duration,volume and direction on price dynamics and volatility

We explore the role of trade volume, trade direction, and the duration between trades in explaining price dynamics and volatility using an Asymmetric Autoregressive Conditional Duration model applied to intraday transactions data. Our results suggest that volume, direction and duration are important determinants of price dynamics, while duration is also an important determinant of volatility. However, the impact of volume and direction on volatility is marginal after controlling for duration, and the impact of volume on volatility appears to be confined to periods of infrequent trading.     

The next tick on Nasdaq

The Nasdaq stock market provides information about buying and selling interest in its limit order book. Using a vector autoregressive model of trades and returns, I assess the effect of the entire order book on the next tick. I also determine the influence of individual market makers and electronic networks and find evidence that the identity of market participants can be useful information. Finally, I produce a set of dynamic market price responses to buy and sell orders, and I find that these estimates vary with standard measures of liquidity.     

The macroeconomic content of international equity market factors

Although existing research has examined the association between macroeconomic data and particular equity markets, little is known regarding the economic content of the latent factors common to international equity markets. This paper considers the macroeconomic information incorporated in unobserved common equity market factors, as well as the possibility that the macroeconomic sensitivities of the factors differ across alternative levels of volatility. Several models are estimated for 15 developed equity markets to examine the economic composition of the common factors, thereby providing an alternative perspective on the economic fundamentals underlying equity markets. A formal Bayesian selection process suggests that a common structure incorporating global and European factors is preferred to the baseline case of a single global factor or the extended scenario of dual global factors. The common factors are associated with a small set of macroeconomic variables.     

Nonlinear interdependence of the Chinese stock markets

The methodologies and assumptions in financial integration studies are problematic and may lead to spurious empirical results. Using surrogate data analysis and the mutual prediction method of testing for nonlinear interdependence, it is feasible for an analyst, with a scant knowledge of the underlying dynamics of two dynamical systems, to show whether or not the systems are interdependent. This study applies these techniques in testing for nonlinear interdependence of three Chinese stock markets: Shanghai, Shenzhen, and Hong Kong. The empirical results of the present study indicate that the stock market series are nonlinear and that the Chinese stock exchanges are nonlinearly interdependent. Specifically, the evidence indicates that Shanghai and Shenzhen markets are bi-directionally interdependent, while Shanghai and Hong Kong as well as Shenzhen and Hong Kong markets are unidirectionally interdependent, with the direction of interdependence going from the mainland's markets to the Hong Kong market.     

Market structure, informational efficiency and liquidity: An experimental comparison of auction and dealer markets

We report the results of 18 market experiments that were conducted in order to compare the call market, the continuous auction and the dealer market. Transaction prices in the call and continuous auction markets are much more efficient than prices in the dealer markets. The call market shows a tendency towards underreaction to new information. Execution costs are lowest in the call market and highest in the dealer market. The trading volume and Roll's (Journal of Finance (1984) 1127–1139) serial covariance estimator are inappropriate measures of execution costs in the present context. The relation between private signals, trading decisions and trading profits is analyzed.     

Risk and predictability of Singapore's private residential market

This study explores the short-run predictability of, and the risks facing investors in, Singapore's private housing market. We explicitly model a periodically collapsing rational speculative bubble within the present-value framework, and propose an unconventional approach as a first-step to screen for structural break(s). We found that a rational speculative bubble is an important predictor of the short-run price growth, especially in volatile times. Furthermore, rent is the only fundamental having a non-negligible impact. The study suggests that the major risk facing market participants comes from unpredictable local policy shifts, and/or a potentially predictable systemic risk.     

Change analysis of a dynamic copula for measuring dependence in multivariate financial data

This paper proposes a new approach to measure dependencies in multivariate financial data. Data in finance and insurance often cover a long time period. Therefore, the economic factors may induce some changes within the dependence structure. Recently, two methods have been proposed using copulas to analyse such changes. The first approach investigates changes within the parameters of the copula. The second determines the sequence of copulas using moving windows. In this paper we take into account the non-stationarity of the data and analyse the impact of (1) time-varying parameters for a copula family, and (2) the sequence of copulas, on the computations of the VaR and ES measures. We propose tests based on conditional copulas and the goodness-of-fit to decide the type of change, and further give the corresponding change analysis. We illustrate our approach using the Standard & Poor 500 and Nasdaq indices in order to compute risk measures using the two previous methods.     

Log-normal continuous cascade model of asset returns: aggregation properties and estimation

Multifractal models and random cascades have been successfully used to model asset returns. In particular, the log-normal continuous cascade is a parsimonious model that has proven to reproduce most observed stylized facts. In this paper, several statistical issues related to this model are studied. We first present a quick, but extensive, review of its main properties and show that most of these properties can be studied analytically. We then develop an approximation theory in the limit of small intermittency λ²???1, i.e. when the degree of multifractality is small. This allows us to prove that the probability distributions associated with these processes possess some very simple aggregation properties across time scales. Such a control of the process properties at different time scales allows us to address the problem of parameter estimation. We show that one has to distinguish two different asymptotic regimes: the first, referred to as the ‘low-frequency asymptotics’, corresponds to taking a sample whose overall size increases, whereas the second, referred to as the ‘high-frequency asymptotics’, corresponds to sampling the process at an increasing sampling rate. The first case leads to convergent estimators, whereas in the high-frequency asymptotics, the situation is much more intricate: only the intermittency coefficient λ² can be estimated using a consistent estimator. However, we show that, in practical situations, one can detect the nature of the asymptotic regime (low frequency versus high frequency) and consequently decide whether the estimations of the other parameters are reliable or not. We apply our results to equity market (individual stocks and indices) daily return series and illustrate a possible application to the prediction of volatility and conditional value at risk.