Pär Sjölander

A new test for simultaneous estimation of unit roots and GARCH risk in the presence of stationary conditional heteroscedasticity disturbances

According to previous research, standard unit root tests are considered robust to stationary GARCH distortions. These conclusions are in fact correct when the number of observations is extraordinarily high. However, simulation experiments in this study, using more normal sample sizes, reveal that eight of the most commonly applied unit root tests exhibit considerable bias in the size in the presence of fairly moderate GARCH distortions. As a remedy for the disturbances from GARCH, this article presents size-corrected unbiased critical values for all these examined tests. Nevertheless there is still reduced power in the presence of stationary GARCH distortions. As a solution, a completely new test is formulated which simultaneously models unit roots and the interconnected parameters of GARCH risk. For empirically relevant sample sizes, this new test exhibits superior size and power properties compared with all the traditional unit root tests in the presence of GARCH disturbances.

A New Ridge Regression Causality Test in the Presence of Multicollinearity

The VAR lag structure applied for the traditional Granger causality (GC) test is always severely affected by multicollinearity due to autocorrelation among the lags. Therefore, as a remedy to this problem we introduce a new Ridge Regression Granger Causality (RRGC) test, which is compared to the GC test by means of Monte Carlo simulations. Based on the simulation study we conclude that the traditional OLS version of the GC test over-rejects the true null hypothesis when there are relatively high (but empirically normal) levels of multicollinearity, while the new RRGC test will remedy or substantially decrease this problem.

A Tobit Ridge Regression Estimator

This article analyzes the effects of multicollienarity on the maximum likelihood (ML) estimator for the Tobit regression model. Furthermore, a ridge regression (RR) estimator is proposed since the mean squared error (MSE) of ML becomes inflated when the regressors are collinear. To investigate the performance of the traditional ML and the RR approaches we use Monte Carlo simulations where the MSE is used as performance criteria. The simulated results indicate that the RR approach should always be preferred to the ML estimation method.

Asymmetric Quantile Analysis of the Swedish Mortgage Price Discovery Process

Based on Swedish banking data we discover robust and significantly positive Asymmetric Price Transmission (APT) effects over all analysed regression quantiles of our mortgage interest rates, with even larger positive APT for the higher percentiles. The analysis was enabled through unique access to a Swedish banks (SEB) own records of their true borrowing costs. Our central contribution is that there is a higher propensity for the bank to rapidly increase its mortgage interest rates for customers following an increase in its borrowing costs, compared with the propensity for the bank to decrease its customers’ mortgage rates subsequent to a corresponding borrowing cost decrease.

Are the Basel II requirements justified in the presence of structural breaks

The Basel Accord and the Swedish regulatory authority Finansinspektionen stipulate that banks and securities firms are obliged to estimate their Internal Risk Management Models (IRMMs) based on a minimum time series estimation period length of 1 year back in time. In this article, the Minimum Capital Risk Requirements (MCRRs) are estimated using moving windows of Swedish long and short OMX index futures positions that are bootstrapped (in blocks) by the use of Value-at-Risk Exponential Generalized Autoregressive Conditional Heteroscedasticity (VaR-(E)GARCH) models. In order to detect and adjust for structural changes in the variance, a so-called Iterative Cumulative Sums of Squares (ICSS) algorithm is applied. By the use of the earlier-mentioned approach, it is concluded that out-of-sample risk predictions are more accurate when using estimation periods shorter than 1 year, probably since relevant information are outdated fairly quickly on the markets. Therefore, the Basel Committee can discard the 1-year requirement without increased risk of financial instability.

A New Asymmetric Interaction Ridge (AIR) Regression Method

Despite that interaction terms are standard tools of regression analysis, the side effects of the inclusion of these terms in models estimated by ordinary least squares (OLS) are yet not fully penetrated. The inclusion of interaction effects induces multicollinearity problems since all non zero values are equal between the interaction term and the regressor. In this article, we propose a procedure to remedy this problem by the use of new ridge regression (RR) shrinkage parameters—which we call the asymmetric interaction ridge (AIR) regression method. By means of Monte Carlo simulations we evaluate both OLS and AIR using the mean square error (MSE) performance criterion. The result from the simulation study confirms our hypothesis that AIR always should be preferred to OLS since it has a lower estimated MSE. Moreover, the advantages of our new method are demonstrated in an empirical application where positive asymmetric price transmission effects are exposed for the mortgage interest rates of Handelsbanken Stadshypotek. It is observed that the mortgage interest rates increase more fully and rapidly to an increase in the banks borrowing costs than to a decrease. This asymmetry is defined as positive asymmetric price transmission (APT).

The efficiency of the Scandinavian banking sector – a wavelet quantile regression analysis

In this article, the Scandinavian housing financing market is analysed in order to determine whether the interest rate price-discovery processes of Denmark, Norway and Sweden are efficient. Based on wavelet quantile regression analysis, we find systematic positive asymmetric price transmission (APT) inefficiencies. We conclude that there is a very high propensity for mortgage lenders to directly increase its customers’ mortgage interest rates subsequently to an increase in its borrowing costs. However, after a corresponding borrowing cost decrease, the same mortgage lenders are very slow to decrease its customers’ mortgage rates. These positive coefficients for so-called APT effects are found in all Scandinavian countries, even if the coefficients for Norway were not statistically significant. Wavelet quantile regression analysis, with a focus on the relevant higher percentiles, is easily motivated since the mortgage rates are adjusted very infrequently. Moreover, wavelet decomposition allows a robust analysis at different time frequency scales, while simultaneously controlling for nonstationary trends, autocorrelation and structural breaks. Except for the still positive but yet insignificant and inconclusive coefficients for Norway, the result is very clear-cut. Regardless of which wavelet scaling decomposition or quantile coefficient that is studied – positive APT effects are clearly identified and confirmed on the Scandinavian mortgage market.

Regional economic concentration and growth

The regional relationships between agglomeration and economic growth are expected to be different in different types of regions. In the literature of the new economic geography it is common to stress the importance of access to cities with agglomeration of economic activities in the form of firms and households in order to be able to explain regional growth. However, it is also well known that many rural areas are performing fairly well in terms of employment and economic opportunities.

A stationary unbiased finite sample ARCH-LM test procedure

Engles (1982) Autoregressive Conditional Heteroscedasticity-Lagrange Multiplier (ARCH-LM) test is the undisputed standard test to detect ARCH. In this article, Monte Carlo (MC) simulations are used to demonstrate that the tests statistical size is biased in finite samples. Two complementing remedies to the related problems are proposed. One simple solution is to simulate new unbiased critical values for the ARCH-LM test. A second solution is based on the observation that for econometrics practitioners, detection of ARCH is generally followed by remedial modelling of this time-varying heteroscedasticity by the most general and robust model in the ARCH family; the Generalized ARCH (GARCH(1,1)) model. If the GARCH models stationarity constraints are violated, as in fact is very often the case, obviously, we can conclude that ARCH-LMs detection of conditional heteroscedasticity has no or limited practical value. Therefore, formulated as a function of whether the GARCH models stationarity constraints are satisfied or not, an unbiased and more relevant two-stage ARCH-LM test is specified. If the primary objectives of the study are to detect and remedy the problems of conditional heteroscedasticity, or to interpret GARCH parameters, the use of this articles new two-stage procedure, 2-Stage Unbiased ARCH-LM (2S-UARCH-LM), is strongly recommended.

Market concentration and market power of the Swedish mortgage Sector – a wavelet panel efficiency analysis

Abstract Based on a panel wavelet efficiency analysis, we conclude that there is a systematic pattern of positive asymmetric price transmission inefficiencies in the interest rates of the largest Swedish mortgage lenders. Thus, there seems to be a higher propensity for mortgage lenders to swiftly increase their customers’ mortgage interest rates subsequent to an increase in its borrowing costs, than to decrease their customers’ mortgage rates subsequent to a corresponding decrease in the cost of borrowing. A unique contribution is our proposed wavelet method which enables a robust detection of positive asymmetric price transmission effects at various time-frequency scales, while simultaneously controlling for non-stationary trends, autocorrelation, and structural breaks. Since traditional time-series analysis methods essentially implies that several wavelet time scales are aggregated into one single time series, the blunt traditional error correction analysis totally failed to discover APT effects for this data set. In summary, using the wavelet method we show that even though the customers in the end finally will benefit from decreases in the mortgage lenders’ financing costs, the lenders wait disproportionally long before the customers’ mortgage rates are decreased.