Essays on Time-Varying Risk and Investor Sentiment: Evidence from the U.S. And G-7 Countries Using Multivariate GARCH Modeling

This dissertation investigates the effects of investor sentiment on asset prices in both the U.S. equity market (chapter III) and international market (chapter IV). It employs a conditional version of the CAPM using a parsimonious generalized autoregressive conditional heteroskedasticity (GARCH) model in which the risk premia, betas, and correlations are time-varying. Investor sentiment is presented from two direct measures (surveys) and one indirect measure as conditional information variables; whereas, previous studies used macroeconomic fundamentals. Furthermore, investor sentiment is not assumed to be fully irrational. It is decomposed into its rational and irrational components. Both rational and irrational components are tested as conditioning information variables in several models. Results are compared with the macroeconomic fundamentals model. Chapter III provides evidence U.S. investor sentiment contains information is priced in the U.S. equity market. In chapter IV, we find no evidence U.S. investor sentiment, either total or irrational, is related to the world market price of risk. These findings are important because it provides evidence U.S. investor sentiment does not significantly affect international asset pricing. This implies there are generally no transmission effects of U.S. sentiment across international markets

Non Linear Modelling of Financial Data Using Topologically Evolved Neural Network Committees

Most of artificial neural network modelling methods are difficult to use as maximising or minimising an objective function in a non-linear context involves complex optimisation algorithms. Problems related to the efficiency of these algorithms are often mixed with the difficulty of the a priori estimation of a network's fixed topology for a specific problem making it even harder to appreciate the real power of neural networks. In this thesis, we propose a method that overcomes these issues by using genetic algorithms to optimise a network's weights and topology, simultaneously. The proposed method searches for virtually any kind of network whether it is a simple feed forward, recurrent, or even an adaptive network. When the data is high dimensional, modelling its often sophisticated behaviour is a very complex task that requires the optimisation of thousands of parameters. To enable optimisation techniques to overpass their limitations or failure, practitioners use methods to reduce the dimensionality of the data space. However, some of these methods are forced to make unrealistic assumptions when applied to non-linear data while others are very complex and require a priori knowledge of the intrinsic dimension of the system which is usually unknown and very difficult to estimate. The proposed method is non-linear and reduces the dimensionality of the input space without any information on the system's intrinsic dimension. This is achieved by first searching in a low dimensional space of simple networks, and gradually making them more complex as the search progresses by elaborating on existing solutions. The high dimensional space of the final solution is only encountered at the very end of the search. This increases the system's efficiency by guaranteeing that the network becomes no more complex than necessary. The modelling performance of the system is further improved by searching not only for one network as the ideal solution to a specific problem, but a combination of networks. These committces of networks are formed by combining a diverse selection of network species from a population of networks derived by the proposed method. This approach automatically exploits the strengths and weaknesses of each member of the committee while avoiding having all members giving the same bad judgements at the same time. In this thesis, the proposed method is used in the context of non-linear modelling of high-dimensional financial data. Experimental results are'encouraging as both robustness and complexity are concerned.Imperial Users onl

Econometric Techniques to Examine Volatility in PEX Bulls and Bears and the Causal Relationship between PEX, ASE and TASE

This study is empirically aimed at conducting three tests; testing volatility persistent in PEX bulls and bears, testing market efficiency for PEX, ASE, and TASE, and testing the causality relationship between the three markets. That is, it attempts to explore whether stock market volatility present a different behavior during PEX bulls and bears phases and explore whether PEX, ASE, and TASE are efficient at weak level. For this purpose, long memory measure is used to indicate volatility persistence and market efficiency. In order to define bull and bear phases, we employed the 200-day moving average, already used by practioners and we found three cycles including 3 bulls and 3 bears. Thus, the study employed Rescaled Range (R/S) to calculate the values of difference parameter d to find evidence of long memory behavior for the daily data observations from August, 1997 to March, 2012. In addition to a long memory measure, the study used nonparametric ADF and PP tests to test market efficiency of PEX, ASE, and TASE at weak level. According to Jarque–Bera test, the closing values of Al-Quds Index of PEX in each bull and bear don’t follow the normal probability distribution. So, the study used nonparametric tests of ADF and PP to determine whether the time series are stationary. The time series are found to be non stationary at level in each phase implying that PEX is efficient at weak level in each phase. Further, according to R/S results, the study found that the estimates of parameter d are above 0 and below 0.5 for bear phases, while the values are above 0.5 for the bull phases implying long memory stationarity for the volatility process. This means that volatility is more persistent in the PEX bears markets than in the PEX bull markets. Further, the PEX bears markets are longer than PEX bulls markets. As a result, volatility persistent in PEX bears and risk associated with it should be considered by investors. Added to this, the overall market-adjusted performance measurement indicates that PEX has average levels of returns and risk more than ASE and TASE. To avoid that, investors and other decision makers should consider both fundamental and technical analysis. For market efficiency test, ADF and PP test are also used to find whether time series data of Al-Quds index, ASE index and TA-100 index are stationarity. In the three cases, means and variances seem to be not constant. This indicates that the three indices are found to be nonstationary at level implying that the three markets are efficient at weak level. For further investigation, R/S statistic is used to calculate the difference parameter to indicate market efficiency. The estimates of d are above 0.5 for the PEX and TASE cases implying that time series data are non-stationary, and there is no evidence of long memory behavior (long range dependence) in the time series data. For ASE, the value of d is above 0 and below 0.5 implying that the time series has long memory behavior. This indicates that ASE isn’t efficient at weak level. So, we accept that PEX, and TASE are efficient at weak level but ASE isn’t. Therefore, regulators and policy makers should support market efficiency.The study further investigates correlation and causality relationship among PEX, ASE and TASE. It analyzes whether there is a long run linkage or interdependency between the three markets. The data sample includes daily observations for the January, 2000-March, 2012 time period. As mentioned before, the data are non-stationary at level, while the data are stationary at first difference and therefore conducting Granger causality tests isn’t restricted. The correlation matrix indicates that the three markets aren’t highly correlated. The correlation is verified for the direction of influence by the Granger causality test between the three markets. However, the study found that there is no significant causal relationship between the three markets except theunilateral causality relationship of ASE over PEX, and the relationship of TASE over ASE, whereas reverse causality doesn’t hold true. In general, the study finds that there is no multilateral causal relationship among the three markets and they are being highly correlated. Therefore, Palestinian investors don’t have to consider changes in TASE index, while changes in ASE index must be considered.This study is empirically aimed at conducting three tests; testing volatility persistent in PEX bulls and bears, testing market efficiency for PEX, ASE, and TASE, and testing the causality relationship between the three markets. That is, it attempts to explore whether stock market volatility present a different behavior during PEX bulls and bears phases and explore whether PEX, ASE, and TASE are efficient at weak level. For this purpose, long memory measure is used to indicate volatility persistence and market efficiency. In order to define bull and bear phases, we employed the 200-day moving average, already used by practioners and we found three cycles including 3 bulls and 3 bears. Thus, the study employed Rescaled Range (R/S) to calculate the values of difference parameter d to find evidence of long memory behavior for the daily data observations from August, 1997 to March, 2012. In addition to a long memory measure, the study used nonparametric ADF and PP tests to test market efficiency of PEX, ASE, and TASE at weak level. According to Jarque–Bera test, the closing values of Al-Quds Index of PEX in each bull and bear don’t follow the normal probability distribution. So, the study used nonparametric tests of ADF and PP to determine whether the time series are stationary. The time series are found to be non stationary at level in each phase implying that PEX is efficient at weak level in each phase. Further, according to R/S results, the study found that the estimates of parameter d are above 0 and below 0.5 for bear phases, while the values are above 0.5 for the bull phases implying long memory stationarity for the volatility process. This means that volatility is more persistent in the PEX bears markets than in the PEX bull markets. Further, the PEX bears markets are longer than PEX bulls markets. As a result, volatility persistent in PEX bears and risk associated with it should be considered by investors. Added to this, the overall market-adjusted performance measurement indicates that PEX has average levels of returns and risk more than ASE and TASE. To avoid that, investors and other decision makers should consider both fundamental and technical analysis. For market efficiency test, ADF and PP test are also used to find whether time series data of Al-Quds index, ASE index and TA-100 index are stationarity. In the three cases, means and variances seem to be not constant. This indicates that the three indices are found to be nonstationary at level implying that the three markets are efficient at weak level. For further investigation, R/S statistic is used to calculate the difference parameter to indicate market efficiency. The estimates of d are above 0.5 for the PEX and TASE cases implying that time series data are non-stationary, and there is no evidence of long memory behavior (long range dependence) in the time series data. For ASE, the value of d is above 0 and below 0.5 implying that the time series has long memory behavior. This indicates that ASE isn’t efficient at weak level. So, we accept that PEX, and TASE are efficient at weak level but ASE isn’t. Therefore, regulators and policy makers should support market efficiency.The study further investigates correlation and causality relationship among PEX, ASE and TASE. It analyzes whether there is a long run linkage or interdependency between the three markets. The data sample includes daily observations for the January, 2000-March, 2012 time period. As mentioned before, the data are non-stationary at level, while the data are stationary at first difference and therefore conducting Granger causality tests isn’t restricted. The correlation matrix indicates that the three markets aren’t highly correlated. The correlation is verified for the direction of influence by the Granger causality test between the three markets. However, the study found that there is no significant causal relationship between the three markets except theunilateral causality relationship of ASE over PEX, and the relationship of TASE over ASE, whereas reverse causality doesn’t hold true. In general, the study finds that there is no multilateral causal relationship among the three markets and they are being highly correlated. Therefore, Palestinian investors don’t have to consider changes in TASE index, while changes in ASE index must be considered

Informe de Política Monetaria - Octubre 2020

Recent data suggest that the technical staff’s appraisals of the condition and development of economic activity, inflation and the labor market have been in line with current trends, marked by a decline in demand and the persistence of ample excess productive capacity. A significant projected fall in output materialized in the second quarter, contributing to a decline in inflation below the 3% target and reflected in a significant deterioration of the labor market. A slow recovery in output and employment is expected to continue for the remainder of 2020 and into next year, alongside growing inflation that should remain below the target. The Colombian economy is likely to undergo a significant recession in 2020 (GDP contraction of 7.6%), though this may be less severe than projected in the previous report (-8.5%). Output is expected to have begun a slow recovery in the second half of this year, though it is not projected to return to pre-pandemic levels in 2021 amid significant global uncertainty. The output decline in the first half of 2020 was less severe than anticipated, thanks to an upward revision in first-quarter GDP and a smaller contraction in the second quarter (-15.5%) than had been projected (-16.5%). Available economic indicators suggest an annual decline in GDP in the third quarter of around 9%. No significant acceleration of COVID-19 cases that would imply a tightening of social distancing measures is presumed for the remainder of this year or in 2021. In that context, a gradual opening of the economy would be expected to continue, with supply in sectors that have been most affected by the pandemic recovering slowly as restrictions on economic activity continue to be relaxed. On the spending side, an improvement in consumer confidence, suppressed demand for goods and services, low interest rates, and higher expected levels of foreign demand should contribute to a recovery in output. A low base of comparison would also help explain the expected increase in GDP in 2021. Based on the conditions laid out above, economic growth in 2020 is expected to be between -9% and -6.5%, with a central value of -7.6%. Growth in 2021 is projected to be between 3% and 7%, with a central value of 4.6% (Graph 1.1). Upward revisions compared to the July report take into account a lower-than-expected fall in first-semester growth and a somewhat faster recovery in the third quarter in some sectors. The forecast intervals for 2020 and 2021 growth tightened somewhat but continue to reflect a high degree of uncertainty over theevolution of the pandemic, the easures required to deal with it, and their effects on global and domestic economic activity.Box 1. Evaluation of the Predictive Capacity of Expected Inflation Measures. Authors: César Anzola-Bravo, Anderson Grajales-Olarte, Alexander Guarín-López, Julián Camilo Mateus-Gamboa, Jonathan Alexander Muñoz-Martínez, Carlos Andrés Quicazán-Moreno, Juan Sebastián Rojas-Moreno, Cristhian Hernando Ruiz-CardozoBox 2. Literature Review: Weighing the Drivers of Portfolio Flows to Emerging Market Economies. Authors: Andrés Sánchez-Jabb

Data Science: Measuring Uncertainties

With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

On the profitability of technical trading

The sole use of price and related summary statistics in a technical trading strategy is an anathema to weak-form market efficiency. In practice, however, traders actively use technical analysis to make investment decisions which makes this an important, but often neglected, area for study. This thesis includes four empirical chapters, which provide important evidence on the profitability of technical trading. The results from the detailed analysis undertaken in this thesis have broad relevance to both academics and those in the investment community. Existing research has been predominantly confined to evaluating basic technical trading rules, such as moving averages. Crucially, this ignores chart patterns. Widely employed by practitioners, such patterns form a vital part of technical analysis. As the most important price pattern, the head and shoulders pattern is subjected to detailed and thorough examination in this thesis. A significant contribution is made by evaluating formations recognised and used by traders, in sharp contrast to limited existing studies. Furthermore, a new method is developed to establish how quickly profits from a head and shoulders strategy decay, which has important implications for traders. Existing research has identified both reversal and relative strength effects in financial asset returns. A key separator between these two findings is the formation and holding time over which portfolios of winners and losers are evaluated. Motivated by this, a very large sample of ultra high-frequency data is used to investigate intraday momentum and reversal effects. As well as being an important contribution to research in this field, the results are, once again, of relevance to practitioners. The need for further research into technical analysis is clearly demonstrated by point and figure charting. Whilst traders have made consistent use of the technique for around a century, the amount of existing research is extremely small. Point and figure has attractive data filtering properties, clear trading rules and is particularly suited to intraday technical analysis. Again, using a very large sample of high-frequency data, a detailed evaluation of the profitability of a point and figure trading strategy is undertaken