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.

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.

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.

Pricing-to-market and the volatility of UK export prices

This empirical study examines the Pricing-To-Market (PTM) behaviour of 20 UK export sectors. Using both Exponential General Autoregressive Conditional Heteroscedasticity (EGARCH) and Threshold GARCH (TGARCH) estimation methods, we find evidence of PTM that is accompanied by strong conditional volatility and weak asymmetry effects. The PTM estimates suggest that when the currency of exporters appreciates in the current period, exporters pass-on between 31% and 94% of the Foreign Exchange (FX) rate increase to importers. However, both export price changes and producers' prices are sluggish, perhaps being driven by coordination failure and menu driven costs, amongst others. Furthermore, export prices contain strong time varying effects which impact on PTM strategy. Exporters do not typically appear to put much more weight on negative news of (say) an FX rate appreciation compared to positive news of an FX rate depreciation. Much depends on the export sector. © 2010 Taylor & Francis.

Modelling stock 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 volatility dynamics, including the underlying volatility persistence and volatility spillover structure. Using daily data from several key stock market indices, 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 time varying asymmetric GARCH 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.

Split-ARCH

2000 Mathematics Subject Classification: 62M10

Összetett devizatermékek kockázatai

A forint árfolyamának stabilitása az előző évtized folyamán elsősorban a deviza pozícóval rendelkező vállalatokat ösztönözte kockázati pozícióik fedezésére. A 2008. őszi forintgyengülés hatására a forinterősödés ellen fedezeti termékekkel védekező vállalatok pozícióin hatalmas veszteségek keletkeztek, amelyek sok esetben a vállalat teljes eredményét negatívra változtatták. Bár az árfolyam-elmozdulás – a termék jellegétől függetlenül – az euró rövid pozíciókra negatívan hatott, a figyelem középpontjába leginkább az összetett devizaügyleteken realizált veszteségek kerültek. A cikk a strukturált devizaügyletek kockázatát modellezi historikus szimuláció és GARCH-modell segítségével egy válság előtt megkötött konkrét struktúra példáján. Arra keressük a választ, hogy ezek a módszerek mennyire voltak képesek előre jelezni a terméken ténylegesen realizált veszteségeket, vagyis mennyire volt indokolt döntés az ügyletkötés.

Essays on exchange rate economics

Exchange rate economics has achieved substantial development in the past few decades. Despite extensive research, a large number of unresolved problems remain in the exchange rate debate. This dissertation studied three puzzling issues aiming to improve our understanding of exchange rate behavior. Chapter Two used advanced econometric techniques to model and forecast exchange rate dynamics. Chapter Three and Chapter Four studied issues related to exchange rates using the theory of New Open Economy Macroeconomics. ^ Chapter Two empirically examined the short-run forecastability of nominal exchange rates. It analyzed important empirical regularities in daily exchange rates. Through a series of hypothesis tests, a best-fitting fractionally integrated GARCH model with skewed student-t error distribution was identified. The forecasting performance of the model was compared with that of a random walk model. Results supported the contention that nominal exchange rates seem to be unpredictable over the short run in the sense that the best-fitting model cannot beat the random walk model in forecasting exchange rate movements. ^ Chapter Three assessed the ability of dynamic general-equilibrium sticky-price monetary models to generate volatile foreign exchange risk premia. It developed a tractable two-country model where agents face a cash-in-advance constraint and set prices to the local market; the exogenous money supply process exhibits time-varying volatility. The model yielded approximate closed form solutions for risk premia and real exchange rates. Numerical results provided quantitative evidence that volatile risk premia can endogenously arise in a new open economy macroeconomic model. Thus, the model had potential to rationalize the Uncovered Interest Parity Puzzle. ^ Chapter Four sought to resolve the consumption-real exchange rate anomaly, which refers to the inability of most international macro models to generate negative cross-correlations between real exchange rates and relative consumption across two countries as observed in the data. While maintaining the assumption of complete asset markets, this chapter introduced endogenously segmented asset markets into a dynamic sticky-price monetary model. Simulation results showed that such a model could replicate the stylized fact that real exchange rates tend to move in an opposite direction with respect to relative consumption. ^

A comprehensive portfolio construction under stochastic environment

Prior research has established that idiosyncratic volatility of the securities prices exhibits a positive trend. This trend and other factors have made the merits of investment diversification and portfolio construction more compelling. ^ A new optimization technique, a greedy algorithm, is proposed to optimize the weights of assets in a portfolio. The main benefits of using this algorithm are to: (a) increase the efficiency of the portfolio optimization process, (b) implement large-scale optimizations, and (c) improve the resulting optimal weights. In addition, the technique utilizes a novel approach in the construction of a time-varying covariance matrix. This involves the application of a modified integrated dynamic conditional correlation GARCH (IDCC - GARCH) model to account for the dynamics of the conditional covariance matrices that are employed. ^ The stochastic aspects of the expected return of the securities are integrated into the technique through Monte Carlo simulations. Instead of representing the expected returns as deterministic values, they are assigned simulated values based on their historical measures. The time-series of the securities are fitted into a probability distribution that matches the time-series characteristics using the Anderson-Darling goodness-of-fit criterion. Simulated and actual data sets are used to further generalize the results. Employing the S&P500 securities as the base, 2000 simulated data sets are created using Monte Carlo simulation. In addition, the Russell 1000 securities are used to generate 50 sample data sets. ^ The results indicate an increase in risk-return performance. Choosing the Value-at-Risk (VaR) as the criterion and the Crystal Ball portfolio optimizer, a commercial product currently available on the market, as the comparison for benchmarking, the new greedy technique clearly outperforms others using a sample of the S&P500 and the Russell 1000 securities. The resulting improvements in performance are consistent among five securities selection methods (maximum, minimum, random, absolute minimum, and absolute maximum) and three covariance structures (unconditional, orthogonal GARCH, and integrated dynamic conditional GARCH). ^