Högskolan i Skövde

This simulation study investigates the forecasting performance of a new information criterion suggested by Hatemi-J (2003) to pick the optimal lag length in the stable and unstable vector autregression (VAR) models. The conducted Monte Carlo experiments reveal that this information criterion is successful in selecting the optimal lag order in the VAR model when the main aim is to draw ex-ante (forecasting) inference regardless if the VAR model is stable or not. In addition, the simulations indicate that this information criterion is robust to autoregressive conditional heteroskedasticity effects.

The performance of different information criteria - namely Akaike, corrected Akaike (AICC), Schwarz-Bayesian (SBC), and Hannan-Quinn - is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens.

It is widely agreed in empirical studies that allowing for potential structural change in economic processes is an important issue. In existing literature, tests for cointegration between time series data allow for one regime shift. This paper extends three residual-based test statistics for cointegration to the cases that take into account two possible regime shifts. The timing of each shift is unknown a priori and it is determined endogenously. The distributions of the tests are non-standard. We generate new critical values via simulation methods. The size and power properties of these test statistics are evaluated through Monte Carlo simulations, which show the tests have small size distortions and very good power properties. The test methods introduced in this paper are applied to determine whether the financial markets in the US and the UK are integrated.

This article uses quarterly data on short-run nominal interest rates and inflation rates over the last four or three decades collected from Australia, Japan, Malaysia and Singapore to test whether the Fisher relation has empirical support. Since meaningful Fisher effect tests critically depend on the integration and cointegration properties of the variables, we present some empirical evidence on these issues and we also apply the Kalman filter to estimate the time-varying parameters. The results show that the data are generally rejecting a full Fisher effect. This implies that nominal interest rates do not respond point-for-point to changes in the expected inflation rates. The possible reasons for the inability to detect a full Fisher effect are also discussed.

This article investigates the degree of capital mobility in Sweden during 1993 to 2004 using quarterly data. A time varying parameter model is estimated by the Kalman filter, and it shows that the relationship between investment as share in gross domestic product (GDP) and saving as share in GDP is much less than one (within the interval of 0.25-0.35), indicating substantial capital mobility. However, since the coefficient in each period is statistically different from zero, capital is still not perfectly mobile. Nevertheless, capital mobility seems to have increased until 1995 when Sweden became a member of EU and after membership there seems to be no significant increase in capital mobility.

Due to increasing globalization and its potential benefits, many emerging markets have introduced capital liberalization policies to attract much needed foreign direct investment. The objective of this article is to empirically investigate whether the conducted deregulation policies resulted in greater integration of emerging financial markets with the world market. For this purpose, a novel method introduced by Hatemi-J and Hacker (2005) is utilized to calculate the parameters as well as to test the significance of these parameters. This method is shown to be robust to nonnormality and time-varying volatility that usually characterize financial data and therefore it can provide more accurate inference compared to other methods. We find that only four of 17 emerging markets have become more integrated with the world market after implementing the liberalization policy.

Previous studies of the causal relationship between money supply and real output are based on asymptotic distributions. If the assumption of normality is not fulfilled and if ARCH effects are present, asymptotic distributions perform inaccurately. In this paper, we reinvestigate the potential causal relationship between money and output by applying an alternative methodology based on the leveraged bootstrapped simulation techniques using data from Denmark, Japan, Sweden, and the US. We find unidirectional causality from money to output for the sample countries except for Sweden for which causality is bi-directional. This finding of unidirectional causality between money and output supports monetary business-cycle models and reveals one important policy implication—that is, in looking for the sources of output fluctuations, money might be a major factor.

This article investigates the issue of international portfolio diversification with respect to the three largest financial markets in the world-namely the US, Japan and the UK. In addition to making use of traditional portfolio analysis, we also suggest a procedure to calculate bootstrap correlation coefficients that can take into account the dynamic structure between the markets as measured by bootstrapped causality tests. Weekly data is used. The results from the first approach are supporting international diversification. The bootstrapped causality tests provide additional empirical support for this conclusion since the size of the causal effects is negligible and the bootstrap correlations are similar as the standard ones. (c) 2006 Elsevier B.V. All rights reserved.

A crucial input in the hedging of risk is the optimal hedge ratio - defined by the relationship between the price of the spot instrument and that of the hedging instrument. Since it has been shown that the expected relationship between economic or financial variables may be better captured by a time varying parameter model rather than a fixed coefficient model, the optimal hedge ratio, therefore, can be one that is time varying rather than constant. This study suggests and demonstrates the use of the Kalman Filter approach for estimating time varying hedge ratio - a procedure that is statistically more efficient and with better forecasting properties.