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We analyze connectedness between the real and financial sectors of the U.S. economy. Using the weekly ADS index of the Philadelphia FED (the widely used business conditions indicator) to represent the real side, we find that during times of financial distress and/or business cycle turning points the direction of connectedness runs from the real sector to financial markets. The ADS index is derived from a model containing a measure of term structure along with real variables, therefore, it might not be the best representative of the real activity to be used in the connectedness analysis. As an alternative, we derive a real activity index (RAI) from a dynamic factor model of the real sector variables only. The behavior of RAI over time is quite similar to that of the ADS index. When we include RAI to represent the real side of the economy in the connectedness analysis, the direction of net connectedness reverses: financial markets generate positive net connectedness to the real side of the economy.

WOS:000390952900001 The goal of the this paper is to investigate the shock spillover characteristics of the Russian stock market during different rounds of sanctions imposed as a reaction to Russia's alleged role in the Ukrainian crisis. We consider six stock markets, represented by their respective stock indices, namely the US (DJIA), the UK (FTSE), the euro area (Euro Stoxx 50), Japan (Nikkei 225), China (SSE Composite) and Russia (RTS). Linking these markets together in a network on the basis of vector autoregressive processes, we can measure, among other things: (i) direct daily return and volatility spillovers from RTS to other market indices, (ii) daily propagation values quantifying the relative importance of the Russian stock market as a return or volatility shock propagator, and (iii) the amount of network repercussions after a shock. The last two are methodological innovations in this context. It turns out that distinct spillover patterns exist in different rounds of sanctions. Large-scale sanctions, beginning in July 2014, rendered the consequences of shocks from Russia less predictable. While these sanctions reduced the importance of the Russian stock market as a propagator of return shocks, they also increased its importance as a propagator of volatility shocks, thus making the network more vulnerable with respect to volatility shocks from the Russian stock market. This is a form of backlash that the sanctioning economies have been facing.

Cycles in the behavior of stock markets have been widely documented. There is an increasing body of literature on whether stock markets anticipate business cycles or its turning points. Several recent studies assert that financial integration impacts positively on business cycle comovements of economies. We consider three Western equity markets, represented by their respective stock indices: DJIA (USA), FTSE 100 (UK), and Euro Stoxx 50 (euro area). Connecting these three markets together via vector autoregressive processes in index returns, we construct "propagation values" to measure and trace, on a daily basis, the relative importance of a market as a volatility creator within the network, where volatility is due to a return shock in a market. A cross-wavelet analysis reveals the joint frequency structure of pairs of the propagation value series, in particular whether or not two series tend to move in the same direction at a given frequency. Our main findings are: (i) From 2001 onwards, the daily propagation values of markets have been fluctuating much less than before, and high frequencies have become less pronounced; (ii) the European markets are in phase at business cycle frequency, while the US market is not in phase with either European market; (iii) in 2008, the euro area has taken over the leading role. This approach not only provides new insight into the time-dependent interplay of equity markets, but it can also replicate certain findings of traditional business cycle research, and it has the advantage of using only readily available stock market data.

Bu tezde vektör otoregresif modelden hesaplanan öngörü hatası varyans ayrıştırması ile oluşturulmuş özel bir bağlanmışlık çerçevesi incelenmiştir. İlgili model günlük borsa endeks getirileri kullanılarak regresyon tahmini yöntemi ile hesaplanmış ve taşma tablosu oluşturulmuştur. Bir ağ yapısını gösteren bu tablo, taşma endeksi adı verilen bir ölçü ile özetlenmiştir.Bir dizi taşma tablosu ile yola çıkılarak, bu çerçeve muhtelif doğrultularda geliştirilmiştir. İlk olarak bir pazarın bilgi yayma potansiyeli hesaplanmıştır. İkinci olarak günden güne üretilen bilgi miktarı ölçülmüştür. Bunun ötesinde ağın bilgi işleme hızını ve kararlılığını ölçen bir entropi kavramı da oluşturulmuştur.Yukarıda ölçüler kullanılarak hali hazırda kullanılan model belirleme yöntemlerinin genellikle yanlış tahminlerde bulunduğu gösterilmiştir.Bu tezde uygun bir yapısal vektör otoregresif model belirleme yöntemi seçeneği olarak `ayrı varyans kullanımı ile belirleme` yöntemi önerilmektedir.Bu yöntemde incelenen veri kümesindeki yapısal şokların varyanslarındaki kırılmalar belirleme işleminde kullanılmaktadır. Ampirik bir uygulama olarak beş pazardan (`Sistemik Beşli`) oluşan bir sistem çözümlenmiştir. Bu sistemi Dow Jones Sanayi, FTSE, EuroStoxx-50, Nikkei-225 ve Şangay Bileşik endeksleri oluşturmaktadır. Ayrı varyans kullanımı ile belirleme yöntemi, pazarların müreffeh ve kararlı olduğu dönemlerde doğudaki pazarların batıdaki pazarlara göre bağıl bilgi yayma özelliğinin oldukça arttığını göstermiştir. Benzer şekilde meşakkatli zamanlarda ise bu etkinin tersine döndüğü gözlenmiştir.Ağ kararlılığı bağlamında, sistem kararlılığının taşma endeksi ile doğrusal olarak arttığı gözlemlenmektedir. Daha yüksek taşma endeksi seviyelerinde ise sistem kararlılığı önce belli miktarda azalmakta daha sonra ise tekrar doğrusal olarak artmaktadır.Sonuç olarak; ağ bağlanmışlık çerçevesi kapsamında ayrı varyans kullanımı ile belirleme yönteminin daha geniş aralıktaki ağ hareket kümelerinin incelenebilmesi için uygun bir seçenek olduğu düşünülmektedir. Our focus in this thesis is a particular network connectedness framework developed through decomposing the forecast error variance that results from a vector auto regressive (VAR) model. The model is estimated using daily returns on stock indices, yielding a spillover table which can be interpreted as a network structure and summarized in a measure so-called spillover index.Departing from a sequence of spillover tables, this framework has been enhanced in several directions with concepts to quantify: (i) a market's potential to spread information, (ii) the amount of information gained from day to day and (iii) the speed of information processing and the degree of network stability. We evaluate the performance of the extant identification approaches, and show that they are arbitrarily misestimating with respect to the above-mentioned network measures. As an alternative, we propose the use of identification through heteroscedasticity (ITH) which utilizes heteroscedasticity in the data to identify structural VARs.As an empirical example, we analyze `Systemic Five` system of markets representing stock indices DJIA, FTSE, EuroStoxx-50, Nikkei-225 and SSEC.ITH uncovers that at prosperous and stable times, eastern markets' relative importance with respect to the western ones' increase significantly, and during times of trouble their importance decrease drastically.In terms of network stability, we observe that the system stability increases linearly with increasing spillovers followed by temporary decreases and then linear increases again. We conclude that ITH could be proposed as a viable identification approach candidate in the network connectedness framework to investigate richer sets of network dynamics. 143

The degree of connectedness of equity markets on a given day can be assessed by decomposing the forecast error variance resulting from a vector autoregressive model, applied to daily returns on stock indices. This well-known procedure leads, for each day, to a spillover matrix which can be readily interpreted as a network structure and summarized into the so-called spillover index, to which much recent research work has been devoted. Taking a sequence of spillover matrices as starting point, we show how the scope of this concept can be broadened in several ways. Firstly, we develop a concept to quantify a market's potential to spread information, which is related to the eigenvector structure of spillover matrices. Secondly, a Markov chain approach allows the definition of relative market entropy, quantifying the amount of information gained from day to day. A further entropy concept can be related to the speed of shock digestion and network stability. As an empirical example, we analyze a system of five markets represented by stock indices Dow Jones Industrial Average (USA), FTSE (UK), Euro Stoxx 50 (euro area), Nikkei 225 (Japan), and SSE Composite (China). It is demonstrated that increasing trends in the spillover index as well as in speed of information digestion are an empirical fact but no logical necessity theoretical examples show that there can be opposite trends in these series.

This paper develops a set of leading indicators of industrial production growth and consumer price inflation for the period 2001-2010. The choice of indicators is based on pseudo out-of-sample forecasting exercise implemented by Stock and Watson (2003), amongst others. We find that asset prices that reflect expectational factors or interest rates that capture the costs of borrowing for the Turkish economy tend to have the greatest predictive power for future real activity and inflation. Our findings provide evidence on the factors determining real activity and inflation in a period of disinflation and normalization for the Turkish economy.

Uluslararası Interbank Kredilerinin Krizler Üzerine Etkileri Erhan Uluceviz Bu çalışmada, gelişmekte olan ülkelerde kriz yaşanması olasılığını etkileyen faktör leri araştırıyoruz. Özellikle, uluslararası bankalararası sermaye piyasası aracılığıyla gerçekleşen kısa vadeli sermaye hareketlerinin bu konudaki rolünü inceledik. 1990 yılı sonrasında gelişmekte olan ülkelere bu piyasalardan yönlendirilen sermaye akımlarının bu ülkelerde kriz gerçekleşme olasılığını anlamlı düzeyde arttırdığını bulduk. Bu sonuç, borç verenler arasındaki eşgüdüm sorunlarına da işaret ediyor. Geçmiş literatürü doğrular şekilde, düşük reel GSYİH büyüme hızının, yüksek reel faiz oranının, bütçe açıklarının ve düşük nitelikli kurumların da kriz yaşanması olasılığını anlamlı derecede arttırdığını bulduk. Anahtar Kelimeler: Bankacılık Krizleri, Basel Kriterleri, Bankalararası Borç Verme, Nitel Tepki Modelleme, Eşgüdüm Sorunları. Effects of International Interbank Lendings on Twin Crises by Erhan Uluceviz We study the factors that are likely to affect the probability of crises. More specifi cally, we focus on the role of short term capital flows through international interbank market. We find that international interbank lendings have significantly increased the likelihood of crises in developing countries after 1990, signalling some coordination failures in this market. Moreover, confirming the previous literature, we find that low GDP growth, high real interest rates, budget deficits and low quality of institutions are associated with high probability of crises. Keywords: Banking Crises, Basel Capital Accord, Interbank Lendings, Qualitative Response Modelling, Coordination Failures. IV 42