Impacts of foreign exchange auctions on the informal market rate in Myanmar

Since the abolition of the official peg and the introduction of a managed float in April 2012, the Central Bank of Myanmar has operated the daily two–way auctions of foreign exchange aimed at smoothing exchange rate fluctuations. Despite the reforms to the foreign exchange regime, however, informal trading of foreign exchange remains pervasive. Using the daily informal exchange rate and Central Bank auction data, this study examines the impacts of auctions on the informal market rate. First, a VAR analysis indicates that the official rate did not Granger cause the informal rate. Second, GARCH models indicate that the auctions did not reduce the conditional variance of the informal rate returns. Overall, the auctions have only a quite modest impact on the informal exchange rate.

Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities

In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials’ producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues. Although there are papers focused on modelling fossil fuel prices, CO2 prices and electricity prices, the literature is scarce on attempts to consider all of them together. This paper focuses on both building a multivariate model for the aforementioned prices and comparing its results with those of univariate ones, in terms of prediction accuracy (univariate and multivariate models are compared for a large span of days, all in the first 4 months in 2011) as well as extracting common features in the volatilities of the prices of all these relevant magnitudes. The common features in volatility are extracted by means of a conditionally heteroskedastic dynamic factor model which allows to solve the curse of dimensionality problem that commonly arises when estimating multivariate GARCH models. Additionally, the common volatility factors obtained are useful for improving the forecasting intervals and have a nice economical interpretation. Besides, the results obtained and methodology proposed can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets.

Integração espacial e eficiência do hedge no mercado sul-americano de soja: comparações entre Brasil e Argentina

Este trabalho tem por objetivo analisar o potencial de desenvolvimento do contrato futuro de soja no Brasil, por meio da atração de hedgers brasileiros e argentinos. Para tanto, faz-se necessário conhecer os padrões das conexões dos preços entre as regiões analisadas. Nesse sentido, o Capítulo 2 investigou a integração espacial do mercado físico de soja no Brasil (região de Sorriso, no Mato Grosso) e na Argentina (região de Rosário, na província de Santa Fé) e comparou ao grau de integração com os Estados Unidos. Foram empregados modelos autorregressivos com threshold (TAR e M-TAR) e modelos vetoriais de correção de erros, lineares e com threshold (VECM e TVECM), visando captar os efeitos dos custos de transação sobre a integração espacial entre essas regiões. Os resultados apontaram que o mercado de soja brasileiro, argentino e norte-americano são integrados, mesmo considerando-se os efeitos dos custos de transação sobre as decisões de arbitragem espacial. Consequentemente, os preços da soja no mercado internacional tendem a refletir o comportamento dos principais países produtores. Apesar disso, o tempo de transmissão de choques de preços mostrou-se, em geral, menor entre Brasil e Argentina, refletindo a proximidade geográfica. Apontou-se também o comportamento assimétrico da transmissão desses choques, uma vez que choques positivos sobre a relação de longo prazo tendem a ser mais persistentes que os negativos. Se o contrato futuro reflete o comportamento de preços de um único mercado físico integrado, deve-se então esperar que o risco de base seja menor para este mercado e, portanto, que a eficiência do hedge seja maior. No Capítulo 3, o objetivo se constituiu em verificar se há maior eficiência no hedge realizado com os contratos com vencimento em março na CME em relação à BM&FBOVESPA, considerando-se as relações de longo prazo entre os preços à vista e futuros, bem como a dinâmica na estrutura de covariâncias condicionais, por meio de modelos de correção de erros (VECM) e modelos de heterocedasticidade condicional generalizados com correlação condicional dinâmica (DCC-GARCH). Os resultados mostraram que, em geral, a introdução da dinâmica nos segundos momentos das distribuições dos erros tende a aumentar a eficiência da estratégia de hedge. Além disso, foi observado que os produtores de Sorriso tendem a obter melhores condições de hedge na CME, embora haja redução da variância ao se operar na BM&FBOVESPA. Por outro lado, a eficiência do hedge para os produtores de Rosário foi significativamente maior na BM&FBOVESPA do que na CME, o que indica o mercado potencial de hedgers argentinos para negociar o contrato futuro de soja local no Brasil.

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.