A Time Varying Parameter Model to Test for Predictability and Integration in Stock Markets of Transition Economies

This paper introduces a model, based on the Kalman filter framework, which allows for latent factors, time varying parameters, and a general GARCH structure for the residuals, extending the Bekaert and Harvey (1997) model. With this extension it is possible to test if an emerging stock market becomes more efficient over time and more integrated with other already established markets. We apply this models to the Czech, Polish, Hungarian, and Russian stock markets. We use data at daily frequency running from April 7th 1994 to July 10th 1997. We show that those markets have a rather heterogeneous pattern with regard to seasonalities and exhibit significant asymmetric GARCH effects where bad news generate greater volatility. In Hungary good news, instead, generate greater volatility leads us to formulate a liquidity hypothesis. A latent factor captures macroeconomic expectations. Concerning predictability, measured with time varying autocorrelations, Hungary reached efficiency before 1994. Russia shows signs of ongoing convergence towards efficiency. For Poland and the Czech Republic we find no improvements. With regard to market integration there is evidence that the importance of Germany has changed over time for all markets. Shocks in the UK are positively related to the Czech and Polish market but neither with the Russian nor the Hungarian ones. Shocks in the US have no impact on these markets but Russia. A strong negative correlation between Russia and the US and Germany tends to disappear.Central and Eastern Europe; stock indices; predictability; market integration

Cointegration versus Spurious Regression in Heterogeneous Panels

We consider the issue of cross sectional aggregation in nonstationary, heterogeneous panels where each unit cointegrates. We first derive the asymptotic properties of the aggregate estimate, and a necessary and sufficient condition for cointegration to hold in the aggregate relationship. We also develop an estimation and testing framework to verify whether the condition is met. Secondly, we analyze the case when cointegration doesn't carry through the aggregation process, investigating whether a mild violation can still lead to an aggregate estimator that summarizes the micro relationships reasonably well. We derive the asymptotic measure of the degree of non cointegration of the aggregated estimate and we provide estimation and testing procedures. A Monte Carlo exercise evaluates the small sample properties of the estimator.Aggregation, Cointegration, Heterogeneous Panel, Monte Carlo Simulation.

Stopping Tests in the Sequential Estimation for Multiple Structural Breaks

In this paper, we propose the use of bootstrapping methods to obtain correct critical values for dating breaks. Following the procedure proposed in Banerjee, Lazarova and Urga (1998), we consider the case of estimating a system with two or more marginal processes and a conditional process. First, the location of the breaks in marginal models is estimated. Next, the marginal models are imposed on the conditional model to form a reduced form system. The conditional model with its own breaks is then estimated. The estimation of the break dates is sequential. Break dates are estimated via two alternative procedures: including estimated break dates one by one or splitting the sample. Inclusion of additional breaks or splitting samples are repeated until a criterion for stopping is satisfied. In this paper we propose bootstrap tests as criterion for stopping sequential search. This procedure allows to improve the estimators to avoid excessive bias and prove to be stable in the case of both stationary and non-stationary series. Finally, we illustrate the methods by modelling the money demand in United KingdomStructural Breaks, Sequential Testing, Bootstrap

Explaining the Diversity of Industry Investment Responses to Uncertainty Using Long Run Panel Survey Data

This paper presents an empirical study of the channels of influence from uncertainty to fixed investment suggested by real options theory. Using panel data from the Confederation of British Industry (CBI) Industrial Trends Survey, we report OLS estimates of the impact of uncertainty on investment where the regressors are augmented by cross-sectional averages of the dependent variable and of the individual specific regressors, as recently suggested by Pesaran (2004). The cross-industry pattern of results is checked for consistency with the pattern predicted by real options theory, using a specially constructed data set of industrial characteristics. We find that irreversibility is able to predict the pattern detected, but only when combined with a measure of the information advantage of delay. There is also evidence for expansion options effects; industries with high R&D and advertising intensities tend to have positive uncertainty effects.Investment, Industry, Irreversibility, Real Options, Uncertainty

Profitability, Capacity, and Uncertainty: A Robust Model of UK Manufacturing Investment

This paper uses a model of capital investment that ascribes a theoretical role to profitability and uncertainty in determining the capital-output ratio. Empirical implementation uses quarterly data from UK manufacturing over a thirty-year period, and unique co-integrating relationships are obtained for two asset classes: buildings and plant and machinery. The corresponding dynamic equations are also well specified. Non-nested testing shows that the performance of the estimated investment models ranks similarly to the performance of predictions from direct investment intentions.

Contrasts Between Classes of Assets in Fixed Investment Equations as a Way of Testing Real Option Theory

This paper tests the power of real options theory to explain investment under uncertainty, exploiting differences in the degree of irreversibility between machinery and buildings. It reports estimates of investment equations for each asset class using a large sample of UK manufacturing industries, with results that are consistent with the predictions of real options theory. Additionally, using a specially constructed industryspecific measure of irreversibility for machinery investment, the paper provides further confirmation of the empirical relevance of real options.Investment, Irreversibility, Real Options, Uncertainty, Panel Data

Monetary disorder and financial regimes - The demand for money in Argentina, 1900-2006

Argentina is a unique experience of protracted economic instability and monetary disorder. In the framework of a long-term view, we investigate the demand for narrow money in Argentina from 1900 to 2006, shedding some light on the existence of money demand equilibria in extremely turbulent economies. The paper examines the effect of monetary regime changes by dealing with the presence of structural breaks in long-run equations. We estimate and test for regime changes through a sequential approach and we embed breaks in long-run models. A robust cointegration analysis can be hence performed in a single-equation framework. We find that estimated parameters are in sharp contrast with those reported in the literature for Argentina, but in line with those reported for industrialized countries, while significant structural breaks appear consistent with major policy shocks that took place in Argentina during the 20th century.money demand ; financial regimes ; structural breaks ; single-equation cointegration ; cointegration test ; Argentina monetary history

Measuring Liquidity in Gas Markets: The Case of the UK National Balancing Point. ESRI Research Bulletin 2019/06

Liquidity is the ability to match buyers and sellers at the lowest transaction costs. Therefore, in a liquid market, executing a transaction over a short-time horizon does not imply higher costs than spreading the same transaction over a longer horizon. Policymakers and practitioners traditionally use the churn ratio to measure liquidity in gas markets. The churn ratio is the ratio of traded volume to actual physical delivery. However, this measure does not consider the impact of trading activity on prices. This research focuses on applying different measures of liquidity, which are used in financial markets, to measure and assess the impact of trading activity on prices. The UK National Balancing Point (NBP) is used as a case study, since it is the most mature hub for gas trading in Europe. Therefore, conclusions from this study can be extended to other gas markets. The research shows that a positive correlation exists between trading activity and prices in the market. However, the strength of this correlation changes over time, depending upon market conditions. Specifically, in the presence of oversupply the impact of trading activity on prices is lower, thus implying that trading a high amount of gas is less expensive, and liquidity is high. Consequently, risk management costs are also less expensive