Michail Karoglou

Breaking down the non-normality of stock returns

This paper investigates whether the non-normality typically observed in daily stock-market returns could arise because of the joint existence of breaks and GARCH effects. It proposes a data-driven procedure to credibly identify the number and timing of breaks and applies it on the benchmark stock-market indices of 27 OECD countries. The findings suggest that a substantial element of the observed deviations from normality might indeed be due to the co-existence of breaks and GARCH effects. However, the presence of structural changes is found to be the primary reason for the non-normality and not the GARCH effects. Also, there is still some remaining excess kurtosis that is unlikely to be linked to the specification of the conditional volatility or the presence of breaks. Finally, an interesting sideline result implies that GARCH models have limited capacity in forecasting stock-market volatility.

Forecasting the UK/US Exchange Rate with Divisia Monetary Models and Neural Networks

This paper compares the UK/US exchange rate forecasting performance of linear and nonlinear models based on monetary fundamentals, to a random walk (RW) model. Structural breaks are identified and taken into account. The exchange rate forecasting framework is also used for assessing the relative merits of the official Simple Sum and the weighted Divisia measures of money. Overall, there are four main findings. First, the majority of the models with fundamentals are able to beat the RW model in forecasting the UK/US exchange rate. Second, the most accurate forecasts of the UK/US exchange rate are obtained with a nonlinear model. Third, taking into account structural breaks reveals that the Divisia aggregate performs better than its Simple Sum counterpart. Finally, Divisia-based models provide more accurate forecasts than Simple Sum-based models provided they are constructed within a nonlinear framework.

Stock market efficiency before and after a financial liberalisation reform:do breaks in volatility dynamics matter?

This article focuses on the deviations from normality of stock returns before and after a financial liberalisation reform, and shows the extent to which inference based on statistical measures of stock market efficiency can be affected by not controlling for breaks. Drawing from recent advances in the econometrics of structural change, it compares the distribution of the returns of five East Asian emerging markets when breaks in the mean and variance are either (i) imposed using certain official liberalisation dates or (ii) detected non-parametrically using a data-driven procedure. The results suggest that measuring deviations from normality of stock returns with no provision for potentially existing breaks incorporates substantial bias. This is likely to severely affect any inference based on the corresponding descriptive or test statistics.

Financial liberalization and stock market volatility: the case of Indonesia

This article examines the relationship between financial liberalization and stock market volatility in Indonesia. By looking at the time series properties of the Jakarta Composite Index (JCI) we identify breaks in stock market volatility which coincide with the timing of major policy events. Our main findings are (i) a significant decrease in volatility after the ‘official’ opening of the stock market to foreign participation; (ii) a significant increase in volatility in the year before market opening following reforms that eased entry requirements and the issuance of brokerage licenses and (iii) a significant increase in volatility at the time of the Asian crisis followed by a significant decrease in the second and sixth years after the crisis.

Monetary policy preferences of the EMU and the UK

We estimate the central bank policy preferences for the European Monetary Union and for the UK. In doing so, we extend the theoretical framework suggested by Cecchetti etal. (The Manchester School, Vol. 70 (2002), pp. 596-618), by assuming that policy preferences change across different regimes. Our empirical results suggest that the weight that policy makers put on inflation is typically profound. Furthermore, it appears that volatility shifts of the economic disturbances are the main factor, which generates variation in policy preferences.

Brexit and Foreign Investment in the UK

This paper explores the likely impact on inward FDI in the UK of the prospects of the UK leaving the EU. We determine that a decisive factor is the uncertainty that is created by the prospect of BrExit, and our analysis shows that indeed it amplifies the effect of any shock. We are able to determine the impacts of currency adjustments in a post Brexit world to the likely effect on inward investment flows into the UK. Amongst other things, our results suggest that in the current near-zero inflation, growth and interest rates period, the anticipated sterling depreciation of a BrExit will act to deter FDI from coming to the UK for a prolonged period of time.

Modelling Returns and Volatilities During Financial Crises: A Time Varying Coefficient Approach

We examine how the most prevalent stochastic properties of key financial time series have been affected during the recent financial crises. In particular we focus on changes associated with the remarkable economic events of the last two decades in the mean and volatility dynamics, including the underlying volatility persistence and volatility spillovers structure. Using daily data from several key stock market indices we find that stock market returns exhibit time varying persistence in their corresponding conditional variances. Furthermore, the results of our bivariate GARCH models show the existence of time varying correlations as well as time varying shock and volatility spillovers between the returns of FTSE and DAX, and those of NIKKEI and Hang Seng, which became more prominent during the recent financial crisis. Our theoretical considerations on the time varying model which provides the platform upon which we integrate our multifaceted empirical approaches are also of independent interest. In particular, we provide the general solution for low order time varying specifications, which is a long standing research topic. This enables us to characterize these models by deriving, first, their multistep ahead predictors, second, the first two time varying unconditional moments, and third, their covariance structure.

Breaks in the UK Household Sector Money Demand Function

We use non-parametric procedures to identify breaks in the underlying series of UK household sector money demand functions. Money demand functions are estimated using cointegration techniques and by employing both the Simple Sum and Divisia measures of money. P-star models are also estimated for out-of-sample inflation forecasting. Our findings suggest that the presence of breaks affects both the estimation of cointegrated money demand functions and the inflation forecasts. P-star forecast models based on Divisia measures appear more accurate at longer horizons and the majority of models with fundamentals perform better than a random walk model.