Essays on Empirical Monetary and Financial Economics

In chapter one, I find that currency carry trade, which is borrowing money from a low interest rate country and lending it into a high interest rate country, can generate high excess profits in both developed and emerging markets. Emerging market (EM) data are more favorable to the UIP hypothesis, but G-10 countries are the opposite. In addition, the higher interest rate differential is usually associated with the exchange rate crash of the high interest rate currency. By decomposition, we find that the profit from G-10 country carry trade is mainly from strong exchange rates, while most of the emerging markets carry trade’s profits are from the huge interest rate differential. By using quantile regression, I also find out that carry trade portfolios are exposed to multiple risk factors. Those factors are more significant at the low tail distribution of returns. Commodities prices and emerging market equities index are positively associated with next month’s carry trade return. Liquidity condition in the U.S. is negatively related to G-10 country carry trade, but not related to emerging markets. Finally, by studying Bloomberg country specific risk data, we find that better financial, economic, and political conditions in each country predict lower carry trade return, but not statistically significant. In chapter two, I study the response of asset prices to the monetary policy shock in Federal Reserve Bank. As the most important monetary policy transmission channel, the financial markets behavior around interest rate decision of the Federal Reserve of U.S. have been widely discussed by people in academia and the industrial world. This paper uses an event study of macroeconomics to examine the casual relationship of the monetary policy shock on asset prices.We find that treasury bills, exchange rates of developed countries are significantly influenced by the unexpected component of the monetary policy in U.S. from 1989 to 2008. In addition, emerging market exchange rates respond weakly to the policy surprise. We also pointed out that international equity markets and commodities prices are not sensitive to the rate decision of the Federal Reserve Bank in one day to very significant in 5 days after rate decision. The pre and post-FOMC meeting day’s Treasury bill yields also respond to the anticipated and unanticipated of the rate decisions

Uncovering hidden information and relations in time series data with wavelet analysis: three case studies in finance

This thesis aims to provide new insights into the importance of decomposing aggregate time series data using the Maximum Overlap Discrete Wavelet Transform. In particular, the analysis throughout this thesis involves decomposing aggregate financial time series data at hand into approximation (low-frequency) and detail (high-frequency) components. Following this, information and hidden relations can be extracted for different investment horizons, as matched with the detail components. The first study examines the ability of different GARCH models to forecast stock return volatility in eight international stock markets. The results demonstrate that de-noising the returns improves the accuracy of volatility forecasts regardless of the statistical test employed. After de-noising, the asymmetric GARCH approach tends to be preferred, although that result is not universal. Furthermore, wavelet de-noising is found to be more important at the key 99% Value-at-Risk level compared to the 95% level. The second study examines the impact of fourteen macroeconomic news announcements on the stock and bond return dynamic correlation in the U.S. from the day of the announcement up to sixteen days afterwards. Results conducted over the full sample offer very little evidence that macroeconomic news announcements affect the stock-bond return dynamic correlation. However, after controlling for the financial crisis of 2007-2008 several announcements become significant both on the announcement day and afterwards. Furthermore, the study observes that news released early in the day, i.e. before 12 pm, and in the first half of the month, exhibit a slower effect on the dynamic correlation than those released later in the month or later in the day. While several announcements exhibit significance in the 2008 crisis period, only CPI and Housing Starts show significant and consistent effects on the correlation outside the 2001, 2008 and 2011 crises periods. The final study investigates whether recent returns and the time-scaled return can predict the subsequent trading in ten stock markets. The study finds little evidence that recent returns do predict the subsequent trading, though this predictability is observed more over the long-run horizon. The study also finds a statistical relation between trading and return over the long-time investment horizons of [8-16] and [16-32] day periods. Yet, this relation is mostly a negative one, only being positive for developing countries. It also tends to be economically stronger during bull-periods

The impact of news narrative on the economy and financial markets

This thesis investigates the impact of news narrative on socio-economic systems across four experiments. Recent years have witnessed a rise in the use of so-called alternative data sources to model and predict dynamics in socio-economic systems. Notably, sources such as newspaper text allow researchers to quantify the elusive concept of narrative, to incorporate text-based features into forecasting frameworks and thus to evaluate the impact of narrative on economic events. The first experiment proposes a new method of incorporating a wide array of sentiment scores from global newspaper articles into macroeconomic forecasts, attempting to forecast industrial production and consumer prices leveraging narrative and sentiment from global newspapers. I model industrial production and consumer prices across a diverse range of economies using an autoregressive framework. The second experiment uses narrative from global newspapers to construct themebased knowledge graphs about world events, demonstrating that features extracted from such graphs improve forecasts of industrial production in three large economies. The third experiment proposes a novel method of including news themes and their associated sentiment into predictions of changes in breakeven inflation rates (BEIR) for eight diverse economies with mature fixed income markets. I utilise five types of machine learning algorithms incorporating narrative-based features for each economy. In the above experiments, models incorporating narrative-based features generally outperform their benchmarks that do not contain such variables, demonstrating the predictive power of features derived from news narrative. The fourth experiment utilises GDELT data and the filtering methodology introduced in the first experiment to create a profitable systematic trading strategy based on the average tone scores for 15 diverse economies

Continuous Market Engineering - Focusing Agent Behavior, Interfaces, and Auxiliary Services

Electronic markets spread out amongst business entities as well as private individuals. Albeit numerous approaches on developing electronic markets exist, a unified approach targeting market development, redesign, and refinement has been lacking. This thesis studies the potential of continuously improving electronic markets. Thereby, the experiments? design focuses on Agent Behavior, Interfaces, and Auxiliary Services and thus unveils the potential of continuously improving electronic markets

An investigation of the market efficiency of the Nairobi Securities Exchange

This study tests for the market efficiency of the Nairobi Securities Exchange (NSE) after the year 2000 to determine the effect of technological advancements on market efficiency. Data that is used is the NSE 20 share index over the period 2001 to 2015; and the NSE All Share Index (NSE ASI) from its initiation during 2008 to 2015. We cannot accept the Efficient Market Hypothesis (EMH) for the NSE using the serial correlation test, the unit root tests and the runs test. However, we can accept the EMH for the more robust variance ratio test. Overall, the results of the market efficiency are mixed. The most significant finding is that the efficiency of the NSE has increased since the year 2000 which suggests that advancements in technology have contributed to the increase in the market efficiency of the NSE.Business ManagementM. Com. (Business Management

Signed path dependence in financial markets: Applications and implications

Despite decades of studies, there is still no consensus on what type of serial dependence, if any, might be present in risky asset returns. The serial dependence structure in asset returns is complex and challenging to study, it varies over time, it varies over observed time resolution, it varies by asset type, it varies with liquidity and exchange and it even varies in statistical structure. The focus of the work in this thesis is to capture a previously unexplored notion of serial dependence that is applicable to any asset class and can be both parameteric or non-parameteric depending on the modelling approach preferred. The aim of this research is to develop new approaches by providing a model-free definition of serial dependence based on how the sign of cumulative innovations for a given lookback horizon correlates with the future cumulative innovations for a given forecast horizon. This concept is then theoretically validated on well-known time series model classes and used to build a predictive econometric model for future market returns, which is applied to empirical forecasting by means of a profit seeking trading strategy. The empirical experiment revealed strong evidence of serial dependence in equity markets, being statistically and economically significant even in the presence of trading costs. Subsequently, this thesis provides an empirical study of the prices of Energy Commodities, Gold and Copper in the futures markets and demonstrates that, for these assets, the level of asymmetry of asset returns varies through time and can be forecast using past returns. A new time series model is proposed based on this phenomenon, also empirically validated. The thesis concludes by embedding into option pricing theory the findings of previous chapters pertaining to signed path dependence structure. This is achieved by devising a model-free empirical risk-neutral distribution based on Polynomial Chaos Expansion and Stochastic Bridge Interpolators that includes information from the entire set of observable European call option prices under all available strikes and maturities for a given underlying asset, whilst the real-world measure includes the effects of serial dependence based on the sign of previous returns. The risk premium behaviour is subsequently inferred from the two distributions using the Radon-Nikodym derivative of the empirical riskneutral distribution with respect to the modelled real-world distribution