This thesis contributes to the empirical literature on how firms meet exceptional financing needs in relation to “investment spikes” or years with unusually large investment activities. In the earlier part of the thesis, I show that the financing of investment during an investment spike is very different from that at other times. I have done this using data for publicly traded US firms over the period 1988 to 2007 and a filtering procedure suggested by Bond et al. (2006). Specifically, external finance, in particular debt finance, is very important in financing investment in years categorized as investment spikes, confirming the findings of Mayer and Sussman (2005). In addition, it has been found that firms with smaller size, lower profitability, more future growth opportunities, fewer tangible assets and more R&D spending tend to use more equity finance in relation to large investment requirements. I also propose the use of the Markov-switching filter to identify investment spikes. In implementing the Markov-switching filter, I apply a first-order two-state Markov-switching mean model to the investment rates de-trended using Hodrick and Prescott's (1997) filter. A Gibbs-sampling procedure is used to produce the marginal posterior distributions of unobserved state variables and model parameters. Among other advantages, this filter allows us to identify multi-year investment spikes. I show that two-year investment spikes identified by the Markov-switching filter are financed quite similarly to single-year investment spikes and that main findings are robust to calendar-time-dependent clustering of investment spikes generated by macroeconomic shocks. In the later part of the thesis, I find there is a positive effect of share liquidity on the propensity to raise debt finance. Using a sample of firm-year observations identified as investment spikes, I find that firms with more liquid shares tend to rely more heavily on debt to finance investment spikes. This result is robust to a control for the effects of firm size and other firm characteristics, the use of various leverage measures, and the use of a whole sample with investment spike characteristics. Another important finding is that firms with more liquid shares tend to have higher target leverage ratios. One interpretation of these results is that information spillovers from the presence of more informative share prices allow firms with more liquid shares to borrow on more favourable terms in normal times, as well as to obtain additional debt finance at lower costs when taking advantage of unusually large investment opportunities.

This research consists of two essays. The first essay entitled” Stock Return Forecasting with Sum-of-the-Parts Methodology: Evidence from Around the World”, examines forecasting ability of stock returns by employing the sum-of-the-parts (SOP) modeling technique introduced by Ferreira and Santa-Clara (2011).This approach decomposes return into three components of growth in price-earnings ratio, earnings growth, and dividend-price ratio. Each component is forecasted separately and fitted values are used in forecast model to predict stock return. We conduct a series of one-step ahead recursive forecasts for a wide range of developed and emerging markets over the period February 1995 through November 2014. Decomposed return components are forecasted separately using a list of financial variables and the fitted values from the best estimators are used according to out-of-sample performance. Our findings show that the SOP method with financial variables outperforms the historical sample mean for the majority of countries. Second essay entitled,” Equity Premium Predictability under Regime Shifts: International Evidence”, utilizes the modified version of the dividend-price ratio that alleviates some econometric concerns in the literature regarding the non-stationary and persistent predictor when forecasting international equity premium across different regimes. We employ Markov switching technique to address the issue of non-linearity between the equity premium and the predictor. The results show different patterns of equity premium predictability over the regimes across countries by the modified ratio as predictor. In addition, transition probability analysis show the adverse effect of financial crisis on regime transition probabilities by increasing the probability of switching between regimes post-crisis 2007 implying higher risk perceived by investors as a result of uncertainty inherent in regime transitions.

In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation. Within a flexible Bayesian non-linear time series framework, our modeling approach assumes that different regimes are characterized by commonly used structural exchange rate models, with their evolution being driven by a Markov process. We assume a time-varying transition probability matrix with transition probabilities depending on a measure of the monetary policy stance of the central bank at the home and foreign country. We apply this model to a set of eight exchange rates against the US dollar. In a forecasting exercise, we show that model evidence varies over time and a model approach that takes this empirical evidence seriously yields improvements in accuracy of density forecasts for most currency pairs considered. / Series: Department of Economics Working Paper Series

Housing prices are of crucial importance in financial stability management. The severe financial crises that originated in the housing market in the US and subsequently spread throughout the world highlighted the crucial role that the housing market plays in preserving financial stability. After the severe housing market crash, many financial institutions in the US suffered from high default rates, severe liquidity shortages, and even bankruptcy. Against this background, researchers have sought to use econometric models to capture and forecast prices of homes. Available empirical research indicates that nonlinear models may be suitable for modelling price cycles. Accordingly, this thesis focuses primarily on using nonlinear models to empirically investigate cyclical patterns in housing prices. More specifically, the content of this thesis can be summarised in three essays which complement the existing literature on price modelling by using nonlinear models. The first essay contributes to the literature by testing the ability of regime switching models to capture and forecast house prices. The second essay examines the impact of banking factors on house price fluctuations. To account for house price characteristics, the regime switching model and generalised autoregressive conditionally heteroscedastic (GARCH) in-mean model have been used. The final essay investigates the effect of structural breaks on the unit root test and shows that a time-varying GARCH in-mean model can be used to estimate the housing price cycle in the UK.

過去對於台灣股市價量非線性關係的研究，都只停留在檢定出兩者之間存在雙向的非線性因果關係，本文則進一步嘗試以Filardo（1994）提出的變動切換機率馬可夫轉換模型（Time-Varying Transition Probability Markov-switching Model，TVTP）配適台灣的單一市場價量以及跨市場價價、量量、價量雙向互動之非線性結構。實證結果顯示，不論是上市或上櫃市場，以同市場成交金額為訊息變數所配適出的指數報酬模型，以及以另一市場的成交金額為訊息變數所配適出的成交金額模型，都具有相對較佳的解釋力。此外，我們也得到台灣股市的價量互動關係普遍存在同市場和跨市場的價領先量情形，也就是台灣投資人有明顯的追漲殺跌現象。

In the present paper we study switching state space models from a Bayesian point of view. For estimation, the model is reformulated as a hierarchical model. We discuss various MCMC methods for Bayesian estimation, among them unconstrained Gibbs sampling, constrained sampling and permutation sampling. We address in detail the problem of unidentifiability, and discuss potential information available from an unidentified model. Furthermore the paper discusses issues in model selection such as selecting the number of states or testing for the presence of Markov switching heterogeneity. The model likelihoods of all possible hypotheses are estimated by using the method of bridge sampling. We conclude the paper with applications to simulated data as well as to modelling the U.S./U.K. real exchange rate. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

<p>This dissertation is a collection of essays with the main objective of estimate and understand macroeconomic behavior of emerging countries by the lenses of modern tools in general equilibrium modeling.</p><p>In the first chapter, I study whether structural parameters of Small Open Economy Real Business Cycle models are constant when applied to Emerging Markets data. Using data from Argentina, I estimate a small open economy model with trend shocks and working capital constraints, augmented with time varying parameters. I find that so called ``structural" parameters suffer substantial changes in the period 1983-2008. Structural instabilities arise from both technological and financial sources. Given these findings, I inquire which are the features of the data that parameter drifts capture. I review emerging markets facts and find parameter instabilities play a key role in addressing for the variability observed in the data.</p><p>In the second chapter, I study policy changes in emerging countries. Motivated by the repeated stabilization programs implemented by emerging economies during the last 30 years, I develop a dynamic stochastic general equilibrium model with Markov-Switching to study fiscal and monetary policies in emerging economies. I estimate the model for Mexico and find strong evidence of policy changes. Two Regimes are identified. The Active Monetary Policy Regime (AMP), in which monetary and fiscal policies respond to inflation and government debt, respectively; and the Active Fiscal Policy Regime (AFP), in which fiscal policy does not respond to government debt and monetary policy does not respond to inflation. AMP holds during short periods of time after macroeconomic crises during the 80s and 90s, and for a long period after 2002. The rest of the periods, AFP is in effect. I find that switches from AFP to AMP have strong stabilization effects at the cost of high output losses. Moreover, credibility in the persistence of the regime change is key to assess the effectiveness of the stabilization program.</p> / Dissertation

<p>This dissertation presents two essays on Markov-Switching dynamic stochastic general equilibrium models.</p><p>The first essay is "Perturbation Methods for Markov-Switching Models," which is co-authored with Juan Rubio-Ramirez, Dan Waggoner, and Tao Zha. This essay develops an perturbation-based approach to solving dynamic stochastic general equilibrium models with Markov-Switching, which implies that parameters governing policies or the environment evolve over time in a discrete manner. Our approach has the advantages that it introduces regime switching from first principles, allows for higher-order approximations, shows non-certainty equivalence of first-order approximations, and allows checking the solution for determinacy. We explain the model setup, introduce an iterative procedure to solve the model, and illustrate it using a real business cycle example.</p><p>The second essay considers a model with financial frictions and studies the role of expectations and unconventional monetary policy during financial crises. During a financial crisis, the financial sector has</p><p>reduced ability to provide credit to productive firms, and the central bank may help lessen the magnitude of the downturn by using unconventional monetary policy to inject liquidity into credit markets. The model allows agents in the economy to expect policy changes by allowing parameters to change according to a Markov process, so agents have expectations about the probability of the central bank intervening during a crisis, and also have expectations about the central bank's exit strategy post-crisis. </p><p>Using this Markov Regime Switching specification, the paper addresses three issues. First, it considers the effects of different exit strategies, and shows that, after a crisis, if the central bank sells off its accumulated assets too quickly, the economy can experience a double-dip recession. Second, it analyzes the effects of expectations of intervention policy on pre-crisis behavior. In particular, if the central bank commits to always intervening during crises, there is a loss of output in pre-crisis times relative to if the central bank commits to never intervening. Finally, it considers the welfare implications of committing to intervening during crises, and shows that committing can raise or lower welfare depending upon the exit strategy used, and that committing before a crisis can be welfare decreasing but then welfare increasing once a crisis occurs.</p> / Dissertation

The Great Moderation means the reduction in the volatility of aggregate economic activity and here we use GDP growth rate to stand for economic activity. In this paper, we apply a Markov switching model to estimate the timing of the Great Moderation in France. Subsequently, by using a Time-varying structural vector autoregression model to determine which are the main variables that cause the reduction of French GDP growth rate and to see the relationship of these variables we choose.

The global foreign exchange market is the largest financial market with turnover in this market often outstripping the GDP of countries in which they are located. The dynamics in the foreign exchange market, especially price dynamics, have huge implications for financial asset values, financial returns and volatility in the international financial system. It is therefore an important area of study. Exchange rates have often departed significantly from the level implied by fundamentals and exhibit excessive volatility. This reality creates a role for central bank intervention in this market to keep the rate in line with economic fundamentals and the overall policy mix, to stabilize market expectations and to calm disorderly markets. Studies that attempt to measure the effectiveness of intervention in the foreign exchange market in terms of exchange rate trends and volatility have had mixed results. This, in many cases, reflects the unavailability of data and the weaknesses in the empirical frameworks used to measure effectiveness. This thesis utilises the most recent data available and some of the latest methodological advances to measure the effectiveness of central bank intervention in the foreign exchange markets of a variety of countries. It therefore makes a contribution in the area of applied empirical methodologies for the measurement of the dynamics of intervention in the foreign exchange market. It demonstrates that by using high frequency data and more robust and appropriate empirical methodologies central bank intervention in the foreign exchange market can be effective. Moreover, a framework that takes account of the interactions between different central bank policy instruments and price dynamics, the reaction function of the central bank, different states of the market, liquidity in the market and the profitability of the central bank can improve the effectiveness of measuring the impact of central bank policy in the foreign exchange market and provide useful information to policy makers.