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In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structure. This framework allows to capture possible changes in the underlying structure of the data in a more flexible and robust manner. Specifically, there is neither a need to arbitrarily set the rolling-window size nor a loss of observations in the calculation of the dynamic measures of connectedness, as no rolling-window analysis is involved. Given that the proposed framework rests on multivariate Kalman filters, it is less sensitive to outliers. Furthermore, we emphasise the merits of this approach by conducting Monte Carlo simulations. We put our framework into practice by investigating dynamic connectedness measures of the four most traded foreign exchange rates, comparing the TVP-VAR results to those obtained from three different rolling-window settings. Finally, we propose uncertainty measures for both TVP-VAR-based and rolling-window VAR-based dynamic connectedness measures.

Tourism development is recognised as an essential tool in promoting economic growth; however, it may also contribute to environmental degradation. Increased pressure for reducing CO 2 emissions poses new challenges to policy-makers who try to promote economic growth and environmental protection in tandem. Since 2010, 19 scenic areas in China have been declared as low-carbon tourism demonstration zones. It follows that investigating whether CO 2 emissions originating in the tourism industry could, in fact, compromise sustainable development in China becomes an essential area of study. Whilst this is a key concern to society, there are only a limited number of studies that analyse the environmental impact of tourism and the validity of the tourism-led-growth hypothesis. This study considers both domestic and international tourism and explicitly tests the relationship among tourism development, economic growth and CO 2 emissions in China, employing a panel vector autoregressive model and utilising regional data between 2006 and 2017. Results show that the development of either international or domestic tourism contributes to economic growth, however, at the expense of the environment.

In this study, we examine the network topology of UK regional property returns over the period 1973Q4–2014Q4 using a dynamic measure of connectedness developed by Diebold and Yilmaz (2014). Overall, our findings indicate that the transmission of inter-regional property returns shocks is an important source of regional property return fluctuations. What is more, this is a dynamic, event-dependent process which implies that, over time, any UK region can be both a net transmitter and a net receiver of shocks. This in turn is conducive to evidence that the ripple effect is not the only driving force propelling changes in the UK housing market. In fact, we find that the regions of the South West, Outer South East, East Midlands and Northern Ireland seem to be dominant transmitters of property returns shocks throughout the sample period. We further suggest that additional evidence regarding weak segmentation in the UK may stem from the fact that there is constant interaction across all regions over time. Most interestingly, we show that London may also act as a net-recipient of shocks. Findings are important for policy makers purporting to alleviate regional imbalances and achieve balanced growth, as well as investors who formulate portfolio diversification strategies. Our results exhibit robustness to a series of tests. 在本研宄中，我们采用 Diebold 和 Yilmaz (2014) 开发的关于连通性的一项动态衡量指标，考察了 1973 年第 4 季度至 2014 年第 4 季度英国地区房地产收益的网络拓扑结构。总的来说，我们的研宄结果表明，房地产收益冲击在地区间的传导是地区房地产收益波动的重要来源。而且， 这是一个动态的、依赖于事件的过程，这意味着随着时间的推移，英国任何一个地区都可能既是冲击的净发射源，也是冲击的净接收者。这反过来有利于证明连锁反应不是推动英国住房市场变化的唯一动力。事实上，我们发现在整个抽样期间，西南、东南郊区、中东部和北爱尔兰这些地区似乎主要是房地产收益冲击的发射源。我们进一步指出，关于英国弱分化的其他证据可能源于这样一个事实：随着时间的推移，所有地区之间都存在不断的相互作用。最有趣的是我们显示伦敦也可能是冲击的净接收者。这些调查结果对于旨在缓解地区失衡、实现均衡增长的决策者以及制定投资组合多元化策略的投资者而言非常重要。我们的研宄结果经受住了一系列测试的检验。

In this study, we investigate the financial and monetary policy responses to oil price shocks using a Structural VAR framework. We distinguish between net oil-importing and net oil-exporting countries. Since the 80s, a significant number of empirical studies have been published investigating the effect of oil prices on macroeconomic and financial variables. Most of these studies though, do not make a distinction between oil-importing and oil-exporting economies. Overall, our results indicate that the level of inflation in both net oil-exporting and net oil-importing countries is significantly affected by oil price innovations. Furthermore, we find that the response of interest rates to an oil price shock depends heavily on the monetary policy regime of each country. Finally, stock markets operating in net oil-importing countries exhibit a negative response to increased oil prices. The reverse is true for the stock market of the net oil-exporting countries. We find evidence that the magnitude of stock market responses to oil price shocks is higher for the newly established and/or less liquid stock markets.

In this study, we investigate the lead–lag relationship between housing prices and sales volume across four US regional housing markets, namely Midwest, Northeast, South, and West. To achieve this, we employ a time-varying parameter vector autoregressive framework of analysis that focuses on dynamic connectedness. We not only investigate how either prices or volumes independently co-move across regions but also, we provide evidence on how prices and volumes combined interact with each other across regions over time. In addition, considering the fact that the relevant connectedness index that emerges from our analysis can be used as a measure of risk, we proceed with the development of portfolios aiming to identify opportunities for reducing investment risk in the housing market. Main findings indicate that (i) all four regions can either transmit or receive shocks in the housing market with regard to prices and volume, (ii) during turbulent economic periods, it is sales volume shocks that drive developments in the US housing market, and (iii) there is potential for effective portfolio diversification. Results have policy implications particularly considering the negative outcomes of overheated housing markets and are also relevant to investors and finance professionals for formulating effective portfolio diversification strategies.

We examine the international impact of recent financial crises on contagion dynamics within international equity portfolios. First, we highlight the importance of macroeconomics for portfolio weighting for each region, and then we examine contagion via a structural regime-switching model and a contagion test. We also examine sources of contagion using regime variables, crisis events, and macroeconomic variables. In particular, we study the Argentine debt crisis, the US financial crisis, and the EU sovereign debt crisis. The macroeconomic variables include changes in market capitalization, trade integration, GDP growth, inflation rate, and interest rate. We also employ two classifications, one relating to the portfolio weighting scheme and another one that considers implied global and regional betas. The empirical findings reveal the existence of financial contagion for all the crises that we investigate. Both methods produce similar results. Stronger contagion is evident for global rather than regional betas. Europe is the region with the highest level of contagion and the one mostly affected by the crises. As far as macroeconomic variables are concerned, they are very important in two ways. They statistically significantly explain contagion, while they also reveal contagion under various portfolio weighting schemes. Both methods suggest that the Argentinian crisis mainly contributes to contagion. The research implications suggest that asset allocation and portfolio management should consider both the global and the regional aspects of contagion as differences can occur.

We employ a \\[\\varDelta CoVaR\\] model in order to measure the potential impact of temperature fluctuations on systemic risk, considering all companies from the STOXX Europe 600 Index, which covers a wide range of industries for the period from 1/1/1990 to 29/12/2017. Furthermore, in this study, we decompose temperature into 3 factors; namely (1) trend, (2) seasonality and (3) anomaly. Findings suggest that, temperature has indeed a significant impact on systemic risk. In fact, we provide significant evidence of either positive or nonlinear temperature effects on financial markets, while the nonlinear relationship between temperature and systemic risk follows an inverted U-shaped curve. In addition, hot temperature shocks strongly increase systemic risk, while we do witness the opposite for cold shocks. Additional analysis shows that deviations of temperature by \\[1\\,^{\\circ }\\hbox {C}\\] can increase the daily Value at Risk by up to 0.24 basis points. Overall, higher temperatures are highly detrimental for the financial system. Results remain robust under the different proxies that were employed to capture systemic risk or temperature.

Volatility spillovers between food commodities and oil prices have been identified in the literature, yet, there has been no empirical evidence to suggest that oil price volatility improves real out-of-sample forecasts of food price volatility. In this study we provide new evidence showing that oil price volatility does not improve forecasts of agricultural price volatility. This finding is based on extensive and rigorous testing of five internationally traded agricultural commodities (soybeans, corn, sugar, rough rice and wheat) and two oil benchmarks (Brent and WTI). We employ monthly and daily oil and food price volatility data and two forecasting frameworks, namely, the HAR and MIDAS-HAR, for the period 2nd January 1990 until 31st March 2017. Results indicate that oil volatility-enhanced HAR or MIDAS-HAR models cannot systematically outperform the standard HAR model. Thus, contrary to what has been suggested by the existing literature based on in-sample analysis, we are unable to find any systematic evidence that oil price volatility improves out-of-sample forecasts of food price volatility. The results remain robust to the choice of different out-of-sample forecasting periods and three different volatility measures.

This paper investigates the impact of US partisan conflict index (PCI) on international stock markets. It extracts innovations from a VAR model and estimates regression specifications. The results document that PCI has a substantial explanatory power. This effect is unique given that (i) traditional disagreement measures per se have no explanatory power, and (ii) neither macroeconomic, nor financial uncertainty measures can undermine the power of PCI. The effect appears to be stronger during periods when the Republican Party is in power. Findings further suggest that this linkage could be seen through the prism of both macroeconomic activity and discount rates.

The aim of this study is to present four essays related to the macroeconometric modelling of specific relations within the economy of the United Kingdom for the period 1992-2012. The focal point of these essays is the link between inflation targeting monetary policy decision making and housing or financial prices. In particular, we investigate whether traditional channels of monetary policy are still in effect under the adopted monetary policy regime. At the same time, findings associated with the specific relation between both asset markets or with the various working assumptions which facilitate our investigation are also reported. The specific econometric methods employed include the development of structural vector autoregressive (SVAR), Markov regime-switching, as well as, multivariate generalised autoregressive conditionally heteroskedastic (MGARCH) models. The formulation of these models is predicated upon the selection of appropriate approximations for all financial and macroeconomic indicators of interest. The main findings of the first essay suggest that under the inflation targeting monetary policy regime, innovations in the monetary policy instrument have no direct effect on the stock market as previously suggested by traditional channels of monetary policy. The said innovations though, appear to have a significant negative impact on the housing market. Furthermore, variation in the stock market can be explained by innovations in the housing market. Turning to the second essay, prominent among our results is the fact that innovations in fiscal policy have a significantly negative effect on the stock market (direct impact). In addition, the effects of monetary policy on the stock market also become negative (indirect impact). According to the third essay when both the stock and the housing market are in a highly volatile regime, then contractionary monetary policy pushes both markets to remain at that regime. Finally, the main outcome from the fourth essay is that the time-varying correlation between monetary policy and housing or financial prices becomes stronger during turbulent times. Overall, our findings suggest that within an inflation targeting monetary policy regime the effects of monetary policy decisions on the stock market strongly depend on the broader economic conditions. By contrast, traditional monetary policy channels with respect to the housing market appear to be in effect; however, broader economic conditions have a key role to play in this case as well.