General to Specific Modelling of Exchange Rate Volatility : a Forecast Evaluation

The general-to-specific (GETS) approach to modelling is widely employed in the modelling of economic series, but less so in financial volatility modelling due to computational complexity when many explanatory variables are involved. This study proposes a simple way of avoiding this problem and undertakes an out-of-sample forecast evaluation of the methodology applied to the modelling of weekly exchange rate volatility. Our findings suggest that GETS specifications are especially valuable in conditional forecasting, since the specification that employs actual values on the uncertain information performs particularly well.Exchange Rate Volatility, General to Specific, Forecasting

Modelling exchange rate volatility with random level shifts

Recent literature has shown that the volatility of exchange rate returns displays long memory features. It has also been shown that if a short memory process is contaminated by level shifts, the estimate of the long memory parameter tends to be upward biased. In this article, we directly estimate a random level shift model to the logarithm of the absolute returns of five exchange rates series, in order to assess whether random level shifts (RLSs) can explain this long memory property. Our results show that there are few level shifts for the five series, but once they are taken into account the long memory property of the series disappears. We also provide out-of-sample forecasting comparisons, which show that, in most cases, the RLS model outperforms popular models in forecasting volatility. We further support our results using a variety of robustness checks

Non-Linear Markov Modelling Using Canonical Variate Analysis: Forecasting Exchange Rate Volatility

We report on a novel forecasting method based on nonlinear Markov modelling and canonical variate analysis, and investigate the use of a prediction algorithm to forecast conditional volatility. In particular, we assess the dynamic behaviour of the model by forecasting exchange rate volatility. It is found that the nonlinear Markov model can forecast exchange rate volatility significantly better than the GARCH(1,1) model due to its flexibility in accommodating nonlinear dynamic patterns in volatility, which are not captured by the linear GARCH(1,1) model.

General to specific modelling of exchange rate volatility : a forecast evaluation

The general-to-specific (GETS) methodology is widely employed in the modelling of economic series, but less so in financial volatility modelling due to computational complexity when many explanatory variables are involved. This study proposes a simple way of avoiding this problem when the conditional mean can appropriately be restricted to zero, and undertakes an out-of-sample forecast evaluation of the methodology applied to the modelling of weekly exchange rate volatility. Our findings suggest that GETS specifications perform comparatively well in both ex post and ex ante forecasting as long as sufficient care is taken with respect to functional form and with respect to how the conditioning information is used. Also, our forecast comparison provides an example of a discrete time explanatory model being more accurate than realised volatility ex post in 1 step forecasting.Exchange rate volatility, General to specific, Forecasting

General to specific modelling of exchange rate volatility : a forecast evaluation

The general-to-specific (GETS) methodology is widely employed in the modelling of economic series, but less so in financial volatility modelling due to computational complexity when many explanatory variables are involved. This study proposes a simple way of avoiding this problem when the conditional mean can appropriately be restricted to zero, and undertakes an out-of-sample forecast evaluation of the methodology applied to the modelling of weekly exchange rate volatility. Our findings suggest that GETS specifications perform comparatively well in both ex post and ex ante forecasting as long as sufficient care is taken with respect to functional form and with respect to how the conditioning information is used. Also, our forecast comparison provides an example of a discrete time explanatory model being more accurate than realised volatility ex post in 1 step forecasting

Modelling South African Currency Crises as Structural Changes in the Volatility of the Rand

This study tests the theory that currency crises are associated with sudden large changes in the structure of foreign exchange market volatility. Due to increases in market uncertainty, crisis periods exhibit abnormally high levels of volatility. By studying short-term changes in volatility dynamics, it is possible to identify the start- and end-dates of crisis periods with a high degree of precision. We use the iterative cumulative sum of squares algorithm to detect multiple shifts in the volatility of rand returns between January 1994 and March 2009. Dummy variables controlling for the detected shifts in variance are incorporated in a GARCH modelling framework. The analysis indicates that previously identified crisis periods in the rand coincide with significant structural changes in market volatility.Currency crisis, exchange rate, volatility, ICSS algorithm, GARCH

Modelling and forecasting exchange-rate volatility with ARCH-type models

The statistical analysis of short-run exchange-rate data shows that there is strong heteroskedasticity and serial dependence of volatility. In addition, the empirical distributions are leptokurtic. The model of generalized autoregressive conditional heteroskedasticity (GARCH) seems to be ideally suited to model these empirical regularities because the model incorporates autocorrelated volatility explicity and it also implies a leptokurtic distribution. The GARCH model does indeed achieve a reasonably good fit to the exchange-rate data. However, the GARCH model is not able to outperform the naive forecasts of volatility which use the current estimate of the variance from the past data. --

Modelling the Volatility of GHC_USD Exchange Rate Using Garch Model

Modelling and forecasting the exchange rate volatility is a crucial area, as it has implications for many issues in the arena of finance and economics.  Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models with their modifications, is used in capturing the volatility of the exchange rates. Simple rate of returns is employed to model the currency exchange rate volatility of Ghana Cedi-United States Dollar. The daily closing exchange rates were used as the daily observations.  The parameters of these models are estimated using the maximum likelihood method. The results indicate that the volatility of the GHC_USD exchange rate is persistent. The asymmetry terms for TARCH are not statistically significant. Also in TARCH case, the coefficient estimate is negative, suggesting that positive shocks imply a higher next period conditional variance than negative shocks of the same sign. This is the opposite to what would have been expected in the case of the application of a GARCH model to a set of stock returns. But arguably, neither the leverage effect or volatility feedback explanations for asymmetries in the context of stocks apply here. Keywords: Exchange rate, volatility, GARCH mode

Empirical analysis of the dynamics of the South African rand (Post-1994)

Thesis (Ph.D. (Economics))--University of the Witwatersrand, Faculty of Commerce, Law and Management, School of Economic & Business Sciences, 2016.The objective of this thesis is to investigate the recent historical dynamics of the four major nominal bilateral spot foreign exchange rates and the fifteen currency-basket nominal effective exchange rate of the South African rand (hereafter referred to as the rand). The thesis has been organised as three separate studies that add to the advancement of the knowledge of the characteristics and behaviour (causal effects) of the rand. The common thread that holds the individual chapters together is the study of the dynamics of the rand. In particular, the study establishes whether the apparent nonstationarity of the exchange rate is a product of unit root test misspecification (a failure to account for structural change), considers the connexions between the timing of the identified structural shifts and important economic and noneconomic events, and analyses rand volatility and the temporal effect of monetary policy surprises on both the spot foreign exchange market returns and volatility of the rand. In order to do this, low- and high-frequency data are employed. With regard to exchange rate modelling, the theoretical economic-exchange rate frameworks are approached both from the traditional macro-based view of exchange rate determination and a micro-based perspective. The various methodologies applied here tackle different aspects of the exchange rate dynamics. To preview the results, we find that adjusting for structural shifts in the unit root tests does not render any of the exchange rates stationary. However, the results show a remarkable fall in the estimates of volatility persistence when structural breaks are integrated into the autoregressive conditional heteroskedasticity (ARCH) framework. The empirical results also shed light on the impact of modelling exchange rates as long memory processes, the extent of asymmetric responses to ‘good news’ and ‘bad news’, the consistencies and contrasts in the five exchange rate series’ volatility dynamics, and the timing and likely triggers of volatility regime switching. Additionally, there are convincing links between the timing of structural changes and important economic (and noneconomic) events, and commonality in the structural breaks detected in the levels and volatility of the rand. We also find statistically and economically significant high-frequency exchange rate returns and volatility responses to domestic interest rate surprises. Furthermore, the rapid response of the rand to monetary policy surprises suggests a relatively high degree of market efficiency (from a mechanical perspective) in processing this information. Keywords: Exchange rate, expectations, long memory, monetary policy surprises, repo rate, structural breaks, volatility; unit root. JEL Code: C22, E52, E58, F31, F41, G14 and G1

Modelling Exchange Rate Volatility by Macroeconomic Fundamentals in Pakistan

What drives volatility in foreign exchange market in Pakistan? This paper undertakes an analysis of modelling exchange rate volatility in Pakistan by potential macroeconomic fundamentals well-known in the economic literature. For this monthly data on Pak Rupee exchange rates in the terms of major currencies (US Dollar, British Pound, Canadian Dollar and Japanese Yen) and macroeconomics fundamentals is taken from April, 1982 to November, 2011. The results show that the PKR-USD exchange rate volatility is influenced by real output volatility, foreign exchange reserves volatility, inflation volatility and productivity volatility. The PKR-GBP exchange rate volatility is influenced by foreign exchange reserves volatility and terms of trade volatility. The PKR-CAD exchange rate volatility is influenced by terms of trade volatility. The findings of this paper reveal that exchange rate volatility in Pakistan results from real shocks than nominal shocks