The Impact of the ECB’s Asset Purchase Programmes on Sovereign Bond Spreads in the Euro Area. Bruges European Economic Research Papers 35/2015

This paper estimates the immediate impact of the European Central Bank’s asset purchase programmes on sovereign bond spreads in the euro area between 2008 and 2015 using a country-by-country GARCH model. The baseline estimates are rigorously diagnosed for misspecification and subjected to a wide range of sensitivity tests. Among others, changes in the dependent variable, the independent variables and the number of (G)ARCH terms are tested. Moreover, the model is applied to subsamples and dynamic conditional correlations are analyzed to estimate the effects of the asset purchases on the contagion of spread movements. Generally, it is found that the asset purchase programmes triggered an reduction of sovereign bond spreads. More specifically, the Securities Markets Programme (SMP) had the most significant immediate effects on sovereign bond spreads across the euro area. The announcements related to the Outright Monetary Transactions (OMT) programme also yielded substantial spread compression in the periphery. In contrast to that, the most recent Public Sector Purchase Programme (PSPP) announced in January 2015 and implemented since March 2015 had no significant immediate effects on sovereign bond spreads, except for Irish spreads. Hence, immediate effects seem to be dependent upon the size of the programme, the extent to which it targets distressed sovereigns and the way in which it is communicated.

Conditional risk, return and contagion in the banking sector in Asia

This paper investigates risk and return in the banking sector in three Asian markets of Taiwan, China and Hong Kong. The study focuses on the risk-return relation in a conditional factor GARCH-M framework that controls for time-series effects. The factor approach is adopted to incorporate intra-industry contagion and an analysis of spillovers between large banks and small banks. Finally, the study provides evidence on these relations before and after the Asian financial crisis of 1997. The results are generally consistent across the markets and with expectations.

Combining the wavelet transform and forecasting models to predict gas forward prices

This paper presents a forecasting technique for forward energy prices, one day ahead. This technique combines a wavelet transform and forecasting models such as multi- layer perceptron, linear regression or GARCH. These techniques are applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the wavelet transform. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.

Energy forward price prediction with a hybrid adaptive model

This paper presents a forecasting technique for forward electricity/gas prices, one day ahead. This technique combines a Kalman filter (KF) and a generalised autoregressive conditional heteroschedasticity (GARCH) model (often used in financial forecasting). The GARCH model is used to compute next value of a time series. The KF updates parameters of the GARCH model when the new observation is available. This technique is applied to real data from the UK energy markets to evaluate its performance. The results show that the forecasting accuracy is improved significantly by using this hybrid model. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.

Using monthly returns to model conditional heteroscedasticity

This empirical study examines the extent of non-linearity in a multivariate model of monthly financial series. To capture the conditional heteroscedasticity in the series, both the GARCH(1,1) and GARCH(1,1)-in-mean models are employed. The conditional errors are assumed to follow the normal and Student-t distributions. The non-linearity in the residuals of a standard OLS regression are also assessed. It is found that the OLS residuals as well as conditional errors of the GARCH models exhibit strong non-linearity. Under the Student density, the extent of non-linearity in the GARCH conditional errors was generally similar to those of the standard OLS. The GARCH-in-mean regression generated the worse out-of-sample forecasts.

Stock price reaction following large one-day price changes: UK evidence

We examine the short-term price reaction of 424 UK stocks to large one-day price changes. Using the GJR-GARCH(1,1), we find no statistical difference amongst the cumulative abnormal returns (CARs) of the Single Index, the Fama–French and the Carhart–Fama–French models. Shocks bigger or equal to 5% are followed by a significant one-day CAR of 1% for all the models. Whilst shocks smaller or equal to -5% are followed by a significant one-day CAR of -0.43% for the Single Index, the CARs are around -0.34% for the other two models. Positive shocks of all sizes and negative shocks maller or equal to -5% are followed by return continuations, whilst the market is efficient following larger negative shocks. The price reaction to shocks is unaffected when we estimate the CARs using the conditional covariances of the pricing variables.

The sensitivity of US banks' stock returns to interest and exchange rate changes

Purpose – The purpose of this paper is to investigate the impact of foreign exchange and interest rate changes on US banks’ stock returns. Design/methodology/approach – The approach employs an EGARCH model to account for the ARCH effects in daily returns. Most prior studies have used standard OLS estimation methods with the result that the presence of ARCH effects would have affected estimation efficiency. For comparative purposes, the standard OLS estimation method is also used to measure sensitivity. Findings – The findings are as follows: under the conditional t-distributional assumption, the EGARCH model generated a much better fit to the data although the goodness-of-fit of the model is not entirely satisfactory; the market index return accounts for most of the variation in stock returns at both the individual bank and portfolio levels; and the degree of sensitivity of the stock returns to interest rate and FX rate changes is not very pronounced despite the use of high frequency data. Earlier results had indicated that daily data provided greater evidence of exposure sensitivity. Practical implications – Assuming that banks do not hedge perfectly, these findings have important financial implications as they suggest that the hedging policies of the banks are not reflected in their stock prices. Alternatively, it is possible that different GARCH-type models might be more appropriate when modelling high frequency returns. Originality/value – The paper contributes to existing knowledge in the area by showing that ARCH effects do impact on measures of sensitivity.

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.

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.