Michael D. McKenzie

The impact of exchange rate volatility on German-US trade flows

Abstract This paper analyses the effect of exchange rate volatility on Germany-US bilateral trade flows for the period 1973:4–1992:9. ARCH models are used to generate a measure of exchange rate volatility and are then tested against Germanys exports to, and imports from, the US. This paper differs from many papers previously published as the effects of volatility are found to be positive and statistically significant for the period under review. The debate over the use of real or nominal exchange rate data in the derivation of volatility estimation is also addressed.

Time‐Varying Beta Risk of Australian Industry Portfolios: A Comparison of Modelling Techniques

This paper investigates three techniques for the estimation of conditional time‐dependent betas: (a) a multivariate generalised ARCH approach; (b) a time‐varying beta market model approach suggested by Schwert and Seguin (1990); and (c) the Kalman filter technique. These approaches are applied to a sample of returns on Australian industry portfolios over the period 1974–1996. The evidence found in this paper, based on in‐sample forecast errors, overwhelmingly supports the Kalman filter approach When out‐of‐sample forecasts are considered the evidence again finds in favour of the Kalman filter approach.

THE IMPACT OF EXCHANGE RATE VOLATILITY ON AUSTRALIAN TRADE FLOWS

Abstract This paper analyses the impact of exchange rate volatility on Australian trade flows. ARCH models are used to generate a measure of exchange rate volatility which is then tested in a model of Australian imports and exports. This paper differs from many of the papers previously published as special attention is given to the export and import trade data sets used. Not only is aggregate trade data tested for the effects of volatility, but disaggregate sectoral trade data is also analysed. Testing sectoral trade data allows us to detect whether the direction or magnitude of the impact of volatility differs depending on the nature of the market in which the goods are traded. If the effect of exchange rate volatility does differ by market, then testing aggregate trade data convolutes the true nature of the relationship and may prevent a significant relationship from being derived. The results obtained in this paper suggest that the impact of exchange rate volatility does differ between traded good sectors although it remains difficult to firmly establish the nature of the relationship.

A multi-country study of power ARCH models and national stock market returns

The use of conditionally heteroscedastic models to model time varying volatility has become commonplace in the empirical finance literature. Ding, Granger and Engle (1993) suggested a model which extends the ARCH class of models to analysing a wider class of power transformations than simply taking the absolute value or squaring the data as in the conventional models. This class of models is called power ARCH (PARCH). This paper analyses the applicability of this model to national stock market returns for ten countries plus a world index. We find the model to be generally applicable once GARCH and leverage effects are taken into consideration. In addition, we also find that the optimal power transformation is remarkably similar across countries.

New Insights into the Impact of the Introduction of Futures Trading on Stock Price Volatility

We examine whether, and to what extent, the introduction of trading in share futures contracts on individual stocks (i.e., individual share futures, or ISFs) has impacted on the systematic risk and volatility of the underlying shares. The use of ISFs allows a unique experimental design that complements existing work on index futures. Our major findings are as follows. First, we found a general reduction in systematic risk on individual stocks after the listing of futures. Second, we found evidence of a decline in unconditional volatility. Third, we found mixed evidence concerning the impact on conditional volatility. Fourth, the introduction of futures was found to impact on the market dynamics, as reflected by a change in the asymmetric volatility response, although the direction of that change is stock‐specific. In general, the results point to a number of features that are case‐specific and provide new insights into the mixed results that are typical of existing studies.

Time varying country risk: an assessment of alternative modelling techniques

Three different techniques for the estimation of a time-varying beta are investigated: a bivariate GARCH model, the Schwert and Seguin approach, and the Kalman filter method. These approaches are applied to a set of monthly Morgan Stanley country index data over the period 1970 to 1995 and their relative performances compared. In-sample forecast tests of the performance of each of these methods for generating conditional beta suggest that the GARCH-based estimates of risk generate the lowest forecast error although these are not necessarily significantly less than those generated by the other techniques considered.

Generalized asymmetric power ARCH modelling of exchange rate volatility

This paper considers the ability of the power ARCH model to capture the stylized features of volatility in 17 heavily traded bilateral exchange rates. This power ARCH model nests a number of models from the ARCH family. The relative merits of these nested ARCH models can be considered using the standard log likelihood ratio test. The results of this paper suggest that in the presence of symmetric responses to innovations in the market, the GARCH(1,1) model is preferred. Where asymmetry is present, than the inclusion of a leverage term is worthwhile as long as a power term is also included.

The Determinants of Conditional Autocorrelation in Stock Returns

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day-of-the-week are potential determinants of conditional autocorrelation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time-varying patterns of return autocorrelation.

Power ARCH modelling of commodity futures data on the London Metal Exchange

A recent addition to the ARCH family of econometric models was introduced by Ding and co-workers wherein the power term by which the data is transformed was estimated within the model rather than being imposed by the researcher. This paper considers the ability of the Power GARCH class of models to capture the stylized features of volatility in a range of commodity futures prices traded on the London Metals Exchange (LME). The results of this procedure suggest that asymmetric effects are not generally present in the LME futures data. Further, unlike stock market data which is well described by the model, futures data is not as well described by the APGARCH model. Nested within the APGARCH model are several other models from the ARCH family. This paper uses the standard log likelihood procedure to conduct pairwise comparisons of the relative merits of each and the results suggest that it is the Taylor GARCH model which performs best.

Chaotic behavior in national stock market indices: New evidence from the close returns test

Abstract Attempts have been made to detect chaotic behaviour in financial markets data using techniques which require large, clean data sets. Although such data are common in the physical sciences where these tests were developed, financial returns data typically do not conform. The close returns test is a recent innovation in the literature and is better suited to testing for chaos in financial markets. This paper tests for the presence of chaos in a wide range of major national stock market indices using the close returns test. The results indicate that the data are not chaotic, although considerable nonlinearities are present. The commonly used BDS test is also applied to the data and, in comparison, the close returns test provides substantially more evidence of nonlinearity compared to the BDS test.