This paper investigates the dynamic causal linkages amongst nine major international stock price indexes. In order to gauge the causal transmission patterns we employ very recent methods of: (i) vector error-correction modeling and (ii) level VAR modeling with possibly integrated and cointegrated processes, advocated by: (i) Toda and Phillips (Econometrica, 61 (1993) 1367) and (ii) Toda and Yamamoto (J. Econometrics, 66 (1995) 225), respectively. The paper illustrates how such methods may be appropriately augmented in a compatible fashion to unearth previously unfounded linkage properties inherent amongst a system of stock price indexes. In particular, we demonstrate that previous research, by using ordinary difference VARs, ignored an important component of linkages displayed purely over the long run. This untapped evidence essentially provides robust and very useful information to international financial analysts and investors. At a substantive level, results of this study tend to support the contention offered by several studies in the literature of significant interdependencies between the established OECD and the Asian markets, and also the leadership of the US and UK markets over the short and long run. The levels VAR, however, illustrate the Japanese market's influence as an additional long run leader. Findings seem to be plausible given that these three markets (US, UK and Japan) have consistently contributed over 75% of global stock market capitalization over the major part of the sample under consideration. At a methodological level, this analysis also provides a primer for the wealth of applied financial econometric research focusing on dynamic causal inference which involve systems containing possibly integrated and cointegrated processes.

Long and short term dynamic causal transmission amongst international stock markets

Drawing on new data and advances in exchange rate regimes' classification, we find that countries appear to benefit by having increasingly flexible exchange rate systems as they become richer and more financially developed. For developing countries with little exposure to international capital markets, pegs are notable for their durability and relatively low inflation. In contrast, for advanced economies, floats are distinctly more durable and also appear to be associated with higher growth. For emerging markets, our results parallel the Baxter and Stockman classic exchange regime neutrality result, though pegs are the least durable and expose countries to higher risk of crisis.

Exchange Rate Regime Durability and Performance in Developing Countries Versus Advanced Economies

The relationship between oil prices and exchange rates: Revisiting theory and evidence

It is often believed that the consumer sentiment index has predictive power for future consumption levels. While Granger causality tests have already been used to test for this, no attempt has been made yet to quantify the predictive power of the consumer sentiment index over different time horizons. In this paper, we decompose the Granger causality at different time lags, by looking at a sequence of nested prediction models. Since the consumer sentiment index turns out to be cointegrated with real consumption, we resort to Error Correcting Models. Four consumption series are studied, namely total real consumption, real consumption of durables, nondurables and services. Among other findings, we show that the consumer sentiment index Granger causes future consumption with an average time lag of four to five months. Furthermore, t is found that the consumer sentiment index has more incremental predictive power for consumption of services than for consumption of durables or nondurables, and that the index is not only useful as a predictor at the very short term, but keeps predictive power at larger time horizons.

Consumer sentiment and consumer spending: Decomposing the granger causal relationship in the time domain

Stock forecasting is a very complex non-stationary, nonlinear time series forecasting, and is often affected by many factors, making it difficult to predict it with a simple model. Support vector machine (SVM) is one of the common data mining methods in the field of machine learning. It has outstanding advantages compared with other methods and it is widely used in various fields. However, there are still many problems in the practical application of the method, and the model itself has many fields that need to be improved. The purpose of this paper is to accurately predict the trend of stock prices, providing a reference model for the trend of stock market and the tracking method of stock price prediction, and provide value reference for research on the forecasting model of stock market and investor’s investment decision. Research using a combined model to predict stock market trends whether will have a significant improvement compared to using a single model to forecast that. The method of this paper is to analyze the shortcomings of current stock market forecasting methods and standard support vector machines firstly, at the same time, based on this, a cumulative auto-regressive moving average is proposed, which combines the least squares support vector machine synthesis model (ARI-MA-LS-SVM) to make basic predictions for the stock market. Secondly, process the data first for the predictive indicators by using cumulative auto-regressive moving average. Then, use the least squares support vector machine of simple indicator system to predict stock price fluctuations. Therefore, it can be concluded that the combined model based on ARI-MA-LS-SVM is more suitable for stock price forecasting than the single forecasting model, and the actual performance is better. At the same time, a large number of simulation experiments show that the algorithm of multiple model’s fusion can achieve the expected effect, which indicate that the model has universal applicability, market applicability and stability feasibility. This model can bring some guidance and reference value for many investors and market regulators.

Stock price forecast based on combined model of ARI-MA-LS-SVM

The global financial crisis, the EMU sovereign debt crisis and international financial regulation: lessons from a systematic literature review

The low oil price recently has caused fears about the sustainability of public finances in some oil producing countries. We examine the relationship between oil prices and sovereign credit risk examining the CDS market. Analysing data from nine countries (Brazil, Malaysia, Norway, Qatar, Russia, Saudi Arabia, the United Kingdom, the United States of America and Venezuela) we have estimated bivariate VAR-GARCH-in-mean models. The results of our empirical investigations generally speaking do suggest that positive oil price shocks lead to lower sovereign CDS spreads. Thus, our findings support the hypothesis that higher oil prices improve the fiscal stability of oil producing countries.

Oil prices and sovereign credit risk of oil producing countries: an empirical investigation

This paper tries to link the uncovered interest rate parity condition to the discussion about interest rate convergence in currency unions. Techniques of fractional cointegration analysis are used to examine the relationship between German and Spanish government bond yields with maturities of two, five, seven and ten years in the period 05 January 2001 to 29 December 2006. Back then (in the good times of the currency union) financial markets did not have to fear exchange rate risk and sovereign credit risk. Thus, the risk premia to be observed were small and driven by liquidity risk. Economic theory suggests that a cointegration vector of between the interest rates can only exist when markets do not expect exchange rate movements and the risk premium is not interest rate sensitive (or practically speaking the sensitivity is low). Given the data set examined here, it is probably no surprise that the interest rates of the two countries are cointegrated and that the cointegration vector of German and Spanish...

Government bond yields in Germany and Spain—empirical evidence from better days

In recent years, early warning indicators and real estate—as an increasingly relevant asset class—have received more and more attention in German insurers’ investment strategies. Therefore, this paper examines the relationship of real estate sentiment data as leading indicators for housing activity and house price indices. However, we focus on empirical evidence from the US due to the quite limited data availability in Germany. The National Association of Home Builders (NAHB) housing market index is used as an indicator for US real estate prices and other variables related to housing activity and the S&P/Case-Shiller 20 city home price index for house prices in the US. In order to test for Granger causality among US house prices and the NAHB sentiment indictor we employ a modified Wald test based on Toda and Yamamoto (1995) examining an augmented vector autoregressive (VAR) model in levels. The results of our empirical investigations do show that there are clear signs for unidirectional Granger causality running from the NAHB housing market index to the S&P/Case-Shiller index. Therefore, the NAHB data seem to be quite helpful predicting US house prices. This empirical finding is of high relevance with regard to the construction of early warning indicator systems for real estate prices.

Early warning indicator systems for real estate investments: Empirical evidence and some thoughts from the perspective of financial risk management

By linking measures of forecast accuracy as well as testing procedures with regard to forecast rationality this paper investigates aggregated survey forecasts with forecast horizons of 3, 12, and 24 months for the exchange rates of the Chinese yuan, the Hong Kong dollar, the Japanese yen, and the Singapore dollar vis‐a‐vis the US dollar and, hence, for four different currency regimes. The rationality of the exchange rate predictions is initially assessed utilizing tests for unbiasedness and efficiency which indicate that the investigated forecasts are irrational in the sense that the predictions are biased. As one major contribution of this paper, it is subsequently shown that these results are not consistent with an alternative, less restrictive, measure of rationality. Investigating the order of integration of the time series as well as cointegrating relationships, this empirical evidence supports the conclusion that the majority of forecasts are in fact rational. Regarding forerunning properties of the predictions, the results are rather mediocre, with shorter term forecasts for the tightly managed USD/CNY FX regime being one exception. As one additional important and novel evaluation result, it can be concluded, that the currency regime matters for the quality of exchange rate forecasts.

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Frederik Kunze

Papers

The global financial crisis, the EMU sovereign debt crisis and international financial regulation: lessons from a systematic literature review

Oil prices and sovereign credit risk of oil producing countries: an empirical investigation

Government bond yields in Germany and Spain—empirical evidence from better days

Early warning indicator systems for real estate investments: Empirical evidence and some thoughts from the perspective of financial risk management

Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts