Stock index reaction to large price changes:evidence frommajor Asian stock indexes

We examine the short-term price behavior of ten Asian stock market indexes following large price changes or “shocks”. Under the standard OLS regression, there is stronger support for return continuations particularly following positive and negative price shocks of less than 10% in absolute size. The results under the GJR-GARCH method provide stronger support for market efficiency, especially for large price shocks. For example, for the Hong Kong stock index, negative shocks of less than -5% but more than -10% generate a significant one day cumulative abnormal return (CAR) of-0.754% under the OLS method, but an insignificant CAR of 0.022% under the GJR-GARCH. We find no support for the uncertainty information hypothesis. Furthermore, the CARs following the period after the Asian financial crisis adjust more quickly to price shocks.

Techniques to improve forecasting models: applications to energy demand and price

This thesis is a study of three techniques to improve performance of some standard fore-casting models, application to the energy demand and prices. We focus on forecasting demand and price one-day ahead. First, the wavelet transform was used as a pre-processing procedure with two approaches: multicomponent-forecasts and direct-forecasts. We have empirically compared these approaches and found that the former consistently outperformed the latter. Second, adaptive models were introduced to continuously update model parameters in the testing period by combining ?lters with standard forecasting methods. Among these adaptive models, the adaptive LR-GARCH model was proposed for the fi?rst time in the thesis. Third, with regard to noise distributions of the dependent variables in the forecasting models, we used either Gaussian or Student-t distributions. This thesis proposed a novel algorithm to infer parameters of Student-t noise models. The method is an extension of earlier work for models that are linear in parameters to the non-linear multilayer perceptron. Therefore, the proposed method broadens the range of models that can use a Student-t noise distribution. Because these techniques cannot stand alone, they must be combined with prediction models to improve their performance. We combined these techniques with some standard forecasting models: multilayer perceptron, radial basis functions, linear regression, and linear regression with GARCH. These techniques and forecasting models were applied to two datasets from the UK energy markets: daily electricity demand (which is stationary) and gas forward prices (non-stationary). The results showed that these techniques provided good improvement to prediction performance.

Interdependence of international equity market volatility

This paper investigates whether equity market volatility in one major market is related to volatility elsewhere. This paper models the daily conditional volatility of equity market wide returns as a GARCH-(1,1) process. Such a model will capture the changing nature of the conditional variance through time. It is found that the correlation between the conditional variances of major equity markets has increased substantially over the last two decades. This supports work which has been undertaken on conditional mean returns which indicates there has been an increase in equity market integration.

Conditional volatility and firm size:an empirical analysis of UK equity portfolios

The techniques and insights from two distinct areas of financial economic modelling are combined to provide evidence of the influence of firm size on the volatility of stock portfolio returns. Portfolio returns are characterized by positive serial correlation induced by the varying levels of non-synchronous trading among the component stocks. This serial correlation is greatest for portfolios of small firms. The conditional volatility of stock returns has been shown to be well represented by the GARCH family of statistical processes. Using a GARCH model of the variance of capitalization-based portfolio returns, conditioned on the autocorrelation structure in the conditional mean, striking differences related to firm size are uncovered.

Exchange controls and the transmission of equity market volatility:the case of the UK

The paper investigates the impact that the relaxation of UK exchange controls in October 1979, had on the transmission of equity market volatility from the UK to other major equity markets. It is suggested that the existence of exchange controls in the UK was an important source of market segmentation which disturbed the transmission of shocks from one country to another, even when shocks contained global information. It is found that when a spillover GARCH(1,1) model is estimated for the five years before and after the removal of exchange controls, volatility shocks spill over from the UK to other markets much more strongly after the removal of exchange controls. This appears to suggest that volatility as well as returns have become more closely related since the UK removed exchange controls.

Novel materials for membrane separation processes

The aim of this work was to synthesise a series of hydrophilic derivatives of cis-1,2-dihydroxy-3,5-cyclohexadiene (cis-DHCD) and copolymerise them with 2-hydroxyethyl methacrylate (HEMA), to produce a completely new range of hydrogel materials. It is theorised that hydrogels incorporating such derivatives of cis-DHCD will exhibit good strength and elasticity in addition to good water binding ability. The synthesis of derivatives was attempted by both enzymatic and chemical methods. Enzyme synthesis involved the transesterification of cis-DHCD with a number of trichloro and trifluoroethyl esters using the enzyme lipase porcine pancreas to catalyse the reaction in organic solvent. Cyclohexanol was used in initial studies to assess the viability of enzyme catalysed reactions. Chemical synthesis involved the epoxidation of a number of unsaturated carboxylic acids and the subsequent reaction of these epoxy acids with cis-DHCD in DCC/DMAP catalysed esterifications. The silylation of cis-DHCD using TBDCS and BSA was also studied. The rate of aromatisation of cis-DHCD at room temperature was studied in order to assess its stability and 1H NMR studies were also undertaken to determine the conformations adopted by derivatives of cis-DHCD. The copolymerisation of diepoxybutanoate, diepoxyundecanoate, dibutenoate and silyl protected derivatives of cis-DHCD with HEMA, to produce a new group of hydrogels was investigated. The EWC and mechanical properties of these hydrogels were measured and DSC was used to determine the amount of freezing and non-freezing water in the membranes. The effect on EWC of opening the epoxide rings of the comonomers was also investigated

The microstructure of the foreign exchange market:the determinants of bid-ask spreads in the foreign exchange market

The purpose of this thesis is to shed more light in the FX market microstructure by examining the determinants of bid-ask spread for three currencies pairs, the US dollar/Japanese yen, the British pound/US dollar and the Euro/US dollar in different time zones. I examine the commonality in liquidity with the elaboration of FX market microstructure variables in financial centres across the world (New York, London, Tokyo) based on the quotes of three exchange rate currency pairs over a ten-year period. I use GARCH (1,1) specifications, ICSS algorithm, and vector autoregression analysis to examine the effect of trading activity, exchange rate volatility and inventory holding costs on both quoted and relative spreads. ICSS algorithm results show that intraday spread series are much less volatile compared to the intraday exchange rate series as the number of change points obtained from ICSS algorithm is considerably lower. GARCH (1,1) estimation results of daily and intraday bid-ask spreads, show that the explanatory variables work better when I use higher frequency data (intraday results) however, their explanatory power is significantly lower compared to the results based on the daily sample. This suggests that although daily spreads and intraday spreads have some common determinants there are other factors that determine the behaviour of spreads at high frequencies. VAR results show that there are some differences in the behaviour of the variables at high frequencies compared to the results from the daily sample. A shock in the number of quote revisions has more effect on the spread when short term trading intervals are considered (intra-day) compared to its own shocks. When longer trading intervals are considered (daily) then the shocks in the spread have more effect on the future spread. In other words, trading activity is more informative about the future spread when intra-day trading is considered while past spread is more informative about the future spread when daily trading is considered

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

The separation of secondary oil-water dispersions in particulate beds

The literature relating to haze formation, methods of separation, coalescence mechanisms, and models by which droplets <100 μm are collected, coalesced and transferred, have been reviewed with particular reference to particulate bed coalescers. The separation of secondary oil-water dispersions was studied experimentally using packed beds of monosized glass ballotini particles. The variables investigated were superficial velocity, bed depth, particle size, and the phase ratio and drop size distribution of inlet secondary dispersion. A modified pump loop was used to generate secondary dispersions of toluene or Clairsol 350 in water with phase ratios between 0.5-6.0 v/v%.Inlet drop size distributions were determined using a Malvern Particle Size Analyser;effluent, coalesced droplets were sized by photography. Single phase flow pressure drop data were correlated by means of a Carman-Kozeny type equation. Correlations were obtained relating single and two phase pressure drops, as (ΔP2/μc)/ΔP1/μd) = kp Ua Lb dcc dpd Cine A flow equation was derived to correlate the two phase pressure drop data as, ΔP2/(ρcU2) = 8.64*107 [dc/D]-0.27 [L/D]0.71 [dp/D]-0.17 [NRe]1.5 [e1]-0.14 [Cin]0.26  In a comparison between functions to characterise the inlet drop size distributions a modification of the Weibull function provided the best fit of experimental data. The general mean drop diameter was correlated by: q_p q_p p_q /β      Γ ((q-3/β) +1) d qp = d fr  .α        Γ ((P-3/β +1 The measured and predicted mean inlet drop diameters agreed within ±15%. Secondary dispersion separation depends largely upon drop capture within a bed. A theoretical analysis of drop capture mechanisms in this work indicated that indirect interception and London-van der Waal's mechanisms predominate. Mathematical models of dispersed phase concentration m the bed were developed by considering drop motion to be analogous to molecular diffusion.The number of possible channels in a bed was predicted from a model in which the pores comprised randomly-interconnected passage-ways between adjacent packing elements and axial flow occured in cylinders on an equilateral triangular pitch. An expression was derived for length of service channels in a queuing system leading to the prediction of filter coefficients. The insight provided into the mechanisms of drop collection and travel, and the correlations of operating parameters, should assist design of industrial particulate bed coalescers.

Corporate restructuring: empirical evidence from the UK

Corporate restructuring is perceived as a challenge to research. Prior studies do not provide conclusive evidence regarding the effects of restructuring. Since there are discernible findings, this research attempts to examine the effects of restructuring events amongst the UK listed firms. The sample firms are listed in the LSE and London AIM stock exchange. Only completed restructuring transactions are included in the study. The time horizon extends from year 1999 to 2003. A three-year floating window is assigned to examine the sample firms. The key enquiry is to scrutinise the ex post effects of restructuring on performance and value measures of firms with contrast to a matched criteria non-restructured sample. A cross sectional study employing logit estimate is undertaken to examine firm characteristics of restructuring samples. Further, additional parameters, i.e. Conditional Volatility and Asymmetry are generated under the GJR-GARCH estimate and reiterated in logit models to capture time-varying heteroscedasticity of the samples. This research incorporates most forms of restructurings, while prior studies have examined certain forms of restructuring. Particularly, these studies have made limited attempts to examine different restructuring events simultaneously. In addition to logit analysis, an event study is adopted to evaluate the announcement effect of restructuring under both the OLS and GJR-GARCH estimate supplementing our prior results. By engaging a composite empirical framework, our estimation method validates a full appreciation of restructuring effect. The study provides evidence that restructurings indicate non-trivial significant positive effect. There are some evidences that the response differs because of the types of restructuring, particularly while event study is applied. The results establish that performance measures, i.e. Operating Profit Margin, Return on Equity, Return on Assets, Growth, Size, Profit Margin and Shareholders' Ownership indicate consistent and significant increase. However, Leverage and Asset Turn Over suggest reasonable influence on restructuring across the sample period. Similarly, value measures, i.e. Abnormal Returns, Return on Equity and Cash Flow Margin suggest sizeable improvement. A notable characteristic seen coherently throughout the analysis is the decreasing proportion of Systematic Risk. Consistent with these findings, Conditional Volatility and Asymmetry exhibit similar trend. The event study analysis suggests that on an average market perceives restructuring favourably and shareholders experience significant and systematic positive gain.

Wealth effects of mergers and acquisitions for US firms: using alternative pricing models

This empirical study employs a different methodology to examine the change in wealth associated with mergers and acquisitions (M&As) for US firms. Specifically, we employ the standard CAPM, the Fama-French three-factor model and the Carhart four-factor models within the OLS and GJR-GARCH estimation methods to test the behaviour of the cumulative abnormal returns (CARs). Whilst the standard CAPM captures the variability of stock returns with the overall market, the Fama-French factors capture the risk factors that are important to investors. Additionally, augmenting the Fama-French three-factor model with the Carhart momentum factor to generate the four-factor captures additional pricing elements that may affect stock returns. Traditionally, estimates of abnormal returns (ARs) in M&As situations rely on the standard OLS estimation method. However, the standard OLS will provide inefficient estimates of the ARs if the data contain ARCH and asymmetric effects. To minimise this problem of estimation efficiency we re-estimated the ARs using GJR-GARCH estimation method. We find that there is variation in the results both as regards the choice models and estimation methods. Besides these variations in the estimated models and the choice of estimation methods, we also tested whether the ARs are affected by the degree of liquidity of the stocks and the size of the firm. We document significant positive post-announcement cumulative ARs (CARs) for target firm shareholders under both the OLS and GJR-GARCH methods across all three methodologies. However, post-event CARs for acquiring firm shareholders were insignificant for both sets of estimation methods under the three methodologies. The GJR-GARCH method seems to generate larger CARs than those of the OLS method. Using both market capitalization and trading volume as a measure of liquidity and the size of the firm, we observed strong return continuations in the medium firms relative to small and large firms for target shareholders. We consistently observed market efficiency in small and large firm. This implies that target firms for small and large firms overreact to new information resulting in a more efficient market. For acquirer firms, our measure of liquidity captures strong return continuations for small firms under the OLS estimates for both CAPM and Fama-French three-factor models, whilst under the GJR-GARCH estimates only for Carhart model. Post-announcement bootstrapping simulated CARs confirmed our earlier results.

Risk and structural instability in US house prices

This paper employs a Component GARCH in Mean model to show that house prices across a number of major US cities between 1987 and 2009 have displayed asset market properties in terms of both risk-return relationships and asymmetric adjustment to shocks. In addition, tests for structural breaks in the mean and variance indicate structural instability across the data range. Multiple breaks are identified across all cities, particularly for the early 1990s and during the post-2007 financial crisis as housing has become an increasingly risky asset. Estimating the models over the individual sub-samples suggests that over the last 20 years the financial sector has increasingly failed to account for the levels of risk associated with real estate markets. This result has possible implications for the way in which financial institutions should be regulated in the future.

Breaking down the non-normality of daily 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.

Econometrics of yield spreads in the money market:a note

The literature on bond markets and interest rates has focused largely on the term structure of interest rates, specifically, on the so-called expectations hypothesis. At the same time, little is known about the nature of the spread of the interest rates in the money market beyond the fact that such spreads are generally unstable. However, with the evolution of complex financial instruments, it has become imperative to identify the time series process that can help one accurately forecast such spreads into the future. This article explores the nature of the time series process underlying the spread between three-month and one-year US rates, and concludes that the movements in this spread over time is best captured by a GARCH(1,1) process. It also suggests the use of a relatively long term measure of interest rate volatility as an explanatory variable. This exercise has gained added importance in view of the revelation that GARCH based estimates of option prices consistently outperform the corresponding estimates based on the stylized Black-Scholes algorithm.

Selectivity and timing performance of UK investment trusts

This study examines the selectivity and timing performance of 218 UK investment trusts over the period July 1981 to June 2009. We estimate the Treynor and Mazuy (1966) and Henriksson and Merton (1981) models augmented with the size, value, and momentum factors, either under the OLS method adjusted with the Newey-West procedure or under the GARCH(1,1)-in-mean method following the specification of Glosten et al. (1993; hereafter GJR-GARCH-M). We find that the OLS method provides little evidence in favour of the selectivity and timing ability, consistent with previous studies. Interestingly, the GJR-GARCH-M method reverses this result, showing some relatively strong evidence on favourable selectivity ability, particularly for international funds, as well as favourable timing ability, particularly for domestic funds. We conclude that the GJR-GARCH-M method performs better in evaluating fund performance compared with the OLS method and the non-parametric approach, as it essentially accounts for the time-varying characteristics of factor loadings and hence obtains more reliable results, in particular, when the high frequency data, such as the daily returns, are used in the analysis. Our results are robust to various in-sample and out-of-sample tests and have valuable implications for practitioners in making their asset allocation decisions across different fund styles. © 2012 Elsevier B.V.