My dissertation investigates the financial linkages and transmission of economic shocks between the US and the smallest emerging markets (frontier markets). The first chapter sets up an empirical model that examines the impact of US market returns and conditional volatility on the returns and conditional volatilities of twenty-one frontier markets. The model is estimated via maximum likelihood; utilizes the GARCH model of errors, and is applied to daily country data from the MSCI Barra. We find limited, but statistically significant exposure of Frontier markets to shocks from the US. Our results suggest that it is not the lagged US market returns that have impact; rather it is the expected US market returns that influence frontier market returns The second chapter sets up an empirical time-varying parameter (TVP) model to explore the time-variation in the impact of mean US returns on mean Frontier market returns. The model utilizes the Kalman filter algorithm as well as the GARCH model of errors and is applied to daily country data from the MSCI Barra. The TVP model detects statistically significant time-variation in the impact of US returns and low, but statistically and quantitatively important impact of US market conditional volatility. The third chapter studies the risk-return relationship in twenty Frontier country stock markets by setting up an international version of the intertemporal capital asset pricing model. The systematic risk in this model comes from covariance of Frontier market stock index returns with world returns. Both the systematic risk and risk premium are time-varying in our model. We also incorporate own country variances as additional determinants of Frontier country returns. Our results suggest statistically significant impact of both world and own country risk in explaining Frontier country returns. Time-variation in the world risk premium is also found to be statistically significant for most Frontier market returns. However, own country risk is found to be quantitatively more important.

In this paper we propose a time-varying parameter VAR model for the housing market in the United States, the United Kingdom, Japan and the Euro Area. For these four economies, we answer the following research questions: (i) How can we evaluate the stance of monetary policy when the policy rate hits the zero lower bound? (ii) Can developments in the housing market still be explained by policy measures adopted by central banks? (iii) Did central banks succeed in mitigating the detrimental impact of the financial crisis on selected housing variables? We analyze the relationship between unconventional monetary policy and the housing markets by using the shadow interest rate estimated by Krippner (2013b). Our findings suggest that the monetary policy transmission mechanism to the housing market has not changed with the implementation of quantitative easing or forward guidance, and central banks can affect the composition of an investors portfolio through investment in housing. A counterfactual exercise provides some evidence that unconventional monetary policy has been particularly successful in dampening the consequences of the financial crisis on housing markets in the United States, while the effects are more muted in the other countries considered in this study. (authors' abstract) / Series: Department of Economics Working Paper Series

A large literature has related the failure of interest rate parity in the foreign exchange market to the existence of a time-varying risk premium. Nevertheless, most modern open economy DSGE models imply a (near) perfect interest rate parity condition. This paper presents a stylized two-country incomplete-markets model in which countries have strong precautionary motives because they face international liquidity constraints, the presence of which successfully generates a time-varying risk premium: the country that has accumulated debt after experiencing relative worse times has stronger precautionary motives and its asset carries a risk premium. (author's abstract) / Series: Department of Economics Working Paper Series

This paper studies the predictive performance and in-sample dynamics of three regime switching models for Swedish macroeconomic time series. The models discussed are threshold autoregressive (TAR), Markov switching autoregressive (MSM-AR), and smooth-transition autoregressive (STAR) regime switching models. We perform recursive out-of-sample forecasting to study the predictive performance of the models. We also assess the in-sample dynamics correspondence to the forecast performance and find that there is not always a relationship. Furthermore, we seek to explore if these unrestricted models yield interpretable results regarding the regimes from an macroeconomic standpoint. We assess GDP-growth, the unemployment rate, and government bond yields and find evidence of Teräsvirta's claims that even when the data has non-linear dynamics, non-linear models might not improve the forecast performance of linear models when the forecast window is linear.

In this study, we examine the dynamic relationship between tourism growth and economic growth, using a newly introduced spillover index approach. Based on monthly data for 10 European countries over the period 1995-2012, our analysis reveals the following empirical regularities. First, the tourism-economic growth relationship is not stable over time in terms of both magnitude and direction, indicating that the tourism-led economic growth (TLEG) and the economic-driven tourism growth (EDTG) hypotheses are time-dependent. Second, the aforementioned relationship is also highly economic event-dependent, as it is influenced by the Great Recession of 2007 and the ongoing Eurozone debt crisis that began in 2010. Finally, the impact of these economic events is more pronounced in Cyprus, Greece, Portugal and Spain, which are the European countries that have witnessed the greatest economic downturn since 2009. Plausible explanations of these results are provided and policy implications are drawn. (authors' abstract)

<p>Travel demand management policies such as congestion charges encourage car-users to change among other things route, mode and departure time. Departure time may be especially affected by time-varying charges, since car-users can avoid high peak hour charges by travelling earlier or later, so called peak spreading effects. Conventional transport models do not include departure time choice as a response. For evaluation of time-varying congestion charges departure time choice is essential.</p><p>In this thesis a transport model called SILVESTER is implemented for Stockholm. It includes departure time, mode and route choice. Morning trips, commuting as well as other trips, are modelled and time is discretized into fifteen-minute time periods. This way peak spreading effects can be analysed. The implementation is made around an existing route choice model called CONTRAM, for which a Stockholm network already exists. The CONTRAM network has been in use for a long time in Stockholm and an origin-destination matrix calibrated against local traffic counts and travel times guarantee local credibility. On the demand side, an earlier developed departure time and mode choice model of mixed logit type is used. It was estimated on CONTRAM travel times to be consistent with the route choice model. The behavioural response under time-varying congestion charges was estimated from a hypothetical study conducted in Stockholm.</p><p>Paper I describes the implementation of SILVESTER. The paper shows model structure, how model run time was reduced and tests of convergence. As regards run time, a 75% cut down was achieved by reducing the number of origin-destination pairs while not changing travel time and distance distributions too much.</p><p>In Paper II car-users underlying preferred departure times are derived using a method called reverse engineering. This method derives preferred departure times that reproduce as well as possible the observed travel pattern of the base year. Reverse engineering has previously only been used on small example road networks. Paper II shows that application of reverse engineering to a real-life road network is possible and gives reasonable results.</p> / Silvester

The intensive care unit treats the most critically ill patients in the hospital, and as such the clinical staff in the intensive care unit have to deal with complex, time-sensitive and life-critical situations. Commonly, patients present with multiple organ dysfunctions, require breathing and cardiovascular support, which make diagnosis and treatment even more challenging. As a result, clinical staff are faced with processing large quantities of often confusing information, and have to rely on experience and trial and error. This occurs despite the wealth of cardiovascular metrics that are available to the clinician. Computer models of the cardiovascular system can help enormously in an intensive care setting, as they can take the monitored data, and aggregate it in such a way as to present a clear and understandable picture of the cardiovascular system. With additional help that such systems can provide, diagnosis can be more accurate and arrived at faster, alone with better optimised treatment that can start sooner, all of which results in decreased mortality, length of stay and cost. This thesis presents a model of the cardiovascular system, which mimics a specific patient’s cardiovascular state, based on only metrics that are commonly measured in an intensive care setting. This intentional limitation gives rise to additional complexities and challenges in identifying the model, but do not stand in the way of achieving a model that can represent and track all the important cardiovascular dynamics of a specific patient. One important complication that comes from limiting the data set is need for an estimation for the ventricular time-varying elastance waveform. This waveform is central to the dynamics of the cardiovascular model and is far too invasive to measure in an intensive care setting. This thesis thus goes on to present a method in which the value-normalised ventricular time-varying elastance is estimated from only metrics which are commonly available in an intensive care setting. Both the left and the right ventricular time-varying elastance are estimated with good accuracy, capturing both the shape and timing through the progress of pulmonary embolism and septic shock. For pulmonary embolism, with the algorithm built from septic shock data, a time-varying elastance waveform with median error of 1.26% and 2.52% results for the left and right ventricles respectively. For septic shock, with the algorithm built from pulmonary embolism data, a time-varying elastance waveform with median error of 2.54% and 2.90% results for the left and right ventricles respectively. These results give confidence that the method will generalise to a wider set of cardiovascular dysfunctions. Furthermore, once the ventricular time-varying elastance is known, or estimated to a adequate degree of accuracy, the time-varying elastance can be used in its own right to access valuable information about the state of the cardiovascular system. Due to the centrality and energetic nature of the time-varying elastance waveform, much of the state of the cardiovascular system can be found within the waveform itself. In this manner this thesis presents three important metrics which can help a clinician distinguish between, and track the progress of, the cardiovascular dysfunctions of pulmonary embolism and septic shock, from estimations based of the monitored pressure waveforms. With these three metrics, a clinician can increase or decrease their probabilistic measure of pulmonary embolism and septic shock.

In this note we develop a Taylor rule based empirical exchange rate model for eleven major currencies that endogenously determines the number of structural breaks in the coefficients. Using a constant parameter specification and a standard time-varying parametermodel as competitors reveals that our flexible modeling framework yields more precise density forecasts for all major currencies under scrutiny over the last 24 years. / Series: Department of Economics Working Paper Series

We analyze the interaction between monetary policy in the US and the global economy proposing a new class of Bayesian global vector autoregressive models that accounts for time-varying parameters and stochastic volatility (TVP-SV-GVAR). Our results suggest that US monetary policy responds to shocks to the global economy, in particular to global aggregate demand and monetary policy shocks. On the other hand, US-based contractionary monetary policy shocks lead to persistent international output contractions and a drop in global inflation rates, coupled with rising interest rates in advanced economies and a real depreciation of currencies with respect to the US dollar. We find considerable evidence for heterogeneity in the spillovers across countries, as well for changes in the transmission of monetary policy shocks over time. (authors' abstract) / Series: Department of Economics Working Paper Series

This thesis consists of three papers that make independendet contributions to the field of financial economics. As such, the papers, Chapter 2, Chapter 3, and Chapter 4, can be read independently of each other. In Chapter 2, we construct a simple measure of the aggregate illiquidity of hedge fund portfolios, and show that it has strong in- and out-of-sample forecasting power for 72 portfolios of international equities, U.S. corporate bonds, and currencies, over the 1994 to 2011 period. The forecasting ability of hedge fund illiquidity for asset returns is, in most cases, greater than, and provides independent information relative to, well-known predictive variables for each of these asset classes. We construct a simple equilibrium model to rationalise our findings and empirically verify auxiliary predictions of the model. In Chapter 3, I analyse the risk-shifting of hedge funds. Since the information on hedge fund holdings is very restricted, researchers have used the variance of returns as a proxy for risk. I propose a new method for measuring the time-varying variance. I use this method to investigate whether equity long-short hedge funds engage in risk-shifting driven by their past performance relative to their peers. I find that hedge funds which have strongly underperformed or outperformed their peers in recent months increase their exposure to the core strategy, i.e. the equity long-short strategy, and to non-core strategies. The risk shifting is mitigated for hedge funds with long redemption periods. Chapter 4 contributes to the equity premium prediction literature. I improve the forecast performance of typical single variable predictive regressions used in the equity premium prediction literature through Bayesian priors derived from consumption-based asset pricing models. To implement these model-based priors, I develop a Bayesian procedure which is rooted in the macroeconometrics literature. I find that the model-based priors can increase the explanatory power, measured by the out-of-sample R², of the single variable predictive regressions by several percentage points.