Economic Modelling and Exchange Risk Exposure Essay

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Econometric Modeling

Financial risk is currently at the center of all economic activity due to the incredibly unstable financial environment of the world economy. As a consequence the search for ways to reduce risk has taken a front seat in the important issues of our day. Several instruments exist in order to increase risk reduction possibilities, these include forward and futures contracts as well as various derivatives. That the most optimal number of risk reduction tools is used is vital. That ratio, the optimal number of risk reduction instruments, is decided by the relationship that exists between the spot instrument and the risk reduction tool. A time varying parameter model has been proven to be more effective in finding the relationship between economic variables and can therefore find an optimal reduction risk ratio that is not constant and can be controlled (Hatemi-J and Roca, 2006). The economic exposure can be controlled or regulated through hedging. In case of an existing futures contract in terms of a foreign currency, the entire hedging aspect turns out to be not so much of a concern. However, if no futures contract capable of being hedged is accessible, then the major issue which emerges is what futures contract to employ in order to hedge that specific currency's risk (Ghosh, 1996).

The minimum variance hedge ratio of Johnson (1960) as well as the portfolio approach has been the ways that the stock index has been extensively studied in order to find risk reduction effectiveness. One way to lower risk is through the process of trading futures. The risk of price motion is lowered through this process. Initially, the stock index futures contracts were introduced as a way for those participating in the market to control their market risk without having to move the composition of their portfolios. Risk reduction, also known as hedging, only becomes truly important when there is a significant change in the value of a certain hedged item. The effectiveness of hedging is judged based on whether or not the hedging derivative offset the actual hedging. That hedging effectiveness as applied to futures contracts is the true way to determine financial futures contracts success was the argument poised by Pennings and Meulenberg (1997) (Kenourgios, Samitas and Drosos, 2005).

Simplified credit risk, high liquidity, as well as low cost is why stock index futures are one of the more successful financial derivatives used in the market. Even more so is the reason for futures offering tempting incentives to investors that allow them to reduce the risk exposure in the spot market, and also to allow them to hedge their portfolios. The way it works is through the compensation of favorable price movements in short future contracts sold compared to the unfavorable fluctuations in the longer units of stock index purchases. So the hedge ratio is the rough estimate comparing the amount of futures contracts that have been sold to the stock index. As for the actual influence of the hedge ratio on the reduction of risk depends on the technique adopted (Hull, 1997; Sutcliffe, 1997).

In giving a basic description, foreign exchange exposure can actually be defined as the level of sensitivity that exists for the real domestic value or worth of the currency of the assets, the liabilities and also the operating income with regards to the changes and fluctuations to the exchange rates that were not expected or anticipated. The risk that is related or linked with foreign exchange is measured by the dissimilarity or the variance that comes about from the domestic value or worth of the currency of the assets, the liabilities and also the operating income with regards to the changes and fluctuations to the exchange rates that were not expected or anticipated. A number of important facts that ought to be taken into consideration include the fact that the fluctuations or variations in the nominal exchange rate is not matched or balanced by the corresponding fluctuations or variations in the prices that are found domestically and also overseas as well (Adler and Dumas, 1984). More so, as will be discussed further in the paper, hedging whether it is operational hedging or financial hedging can result in a company having an increased value and worth bearing in mind the various and dissimilar market imperfections.

The key purpose of this project is an evaluation of whether or not econometric modeling of the hedge ratio produces any dissimilarity or variance with respect to money market effectiveness and cross hedgingof the exposure of Exchange rate risk. In the project, four different econometric models are used with the aim of estimating hedge ratio, namely, first difference model, error correction model, quadratic model and conventional model (levels). These models are derived, in the project, from the exchange rates and interest rates of three nations- Japan, Hong Kong and United Kingdom.

An error correction model is an econometric model that is kind of time series model with multiple variables in a direct form estimating the rate at which Y (the dependent variable) returns to its state of equilibrium when there are changes on X (the independent variable). This sort of model is valuable and advantageous for the short-term as well as long-term effects of a time series on another time series. Quadratic models can be defined as non-linear models, which take the elementary form when the functional element/component is non-linear and the parameters of the model are unknown. The conventional econometrics model used in the project is an OLS (ordinary least square) regression type, with the equation's dependent variable being the unhedged exchange rate and the equation's explanatory variable being the return rate or price of instrument which is being hedged. The instrument that is being hedged may be either futures contract or forward contract. Finally, the FD model takes into consideration the conventional model's first differences.

The paper is categorized into 5 chapters. The first chapter introduces the topic of research, followed subsequently by a review of the literature in the second chapter. Chapter 3 comprises a discussion with regards to the empirical testing methods utilized in the study. Subsequently, chapter 4 contains the discussion and evaluation of the results acquired from empirical testing. The fifth and final chapter of this research project offers the summary and discussion along with key findings of this study.

Chapter 2: Literature Review

There are several categories for which the methods of empirically estimating of the hedge ratio that have been deduced. They fall into these following categories: 1. Error Correction (ECM) Models, 2. Autogressive Conditional Heteroskedasticity (ARCH) - based model, and 3. The Ordinary Least Squares (OLS) model. One of the main critiques for the OLS model is the fact that it does not consider the varying time distributions, heteroskedasticity, cointegration, and serial correlation. Thus, by not properly considering cointegration the model fails to specify correctly and results in a term known as underhedging. The ECM models have therefore been counted as superior since they tend to yield better results (Ghosh and Clayton, 1996; Chou, Dennis and Lee, 1996; Sim and Zurbruegg, 2001). The serial correlation is what the ARCH-based models focus on accounting for. Their varying distributions for time are therefore also better in yielding strong results than the OLS methods. Evidence does exist to support this argument (Baillie and Myers, 1991, Park and Switzer, 1995).

Theoretically it does seem that the results from the ARCH-based and ECM models should prove vastly superior to the OLS ones, the truth is that no method has actually been proven to be the best. An estimation of risk reduction ratios using the Greek stock and futures market was done by Floros and Vougas (2004). In their estimation they used ECM, OLS, BGARCH, and the VECM models. The results they found were that VECM as well as ECM seemed to give better results over the OLDS model. As far as the BGARCH model goes, it provided the best results out of all the different models. Another study was done by Lim (1996) in which the Nikkei 225 futures contracts were considered in regards to hedging performance. This study also yielded in favor of the ECM method. Finally, Rossi and Zucca (2002) argued the vast superiority of GARCH as a hedge ratio model over all other ones in the study they performed with the LIFFE traded futures contracts German Bund Futures, Eurolira, and the governmental bonds from Italy

Greece's stock index futures market was used in Floros and Vougas's study that examined its risk reduction ratios. This research study had the purpose of estimating either constant or time varying hedge ratios through various techniques. An array of econometric models was employed in order to estimate as well as derive the hedge ratios of both stock index futures contracts of the ADEX (Athens Derivatives Exchange). Vector and simple error correction models, multivariate generalized autogressive heteroscedasticity (M-GARCH), and regressions from standard OLS was used in the estimation of the proper corresponding hedge ratios usable. The M-GARCH models won out in superiority of its…

Sources Used in Document:

References

Adler, M., and Dumas, B., (1984) Exposure to currency risk: definition and measurement. Financial Management 13, 41 -- 50.

Baillie, R.T. And R.J. Myers (1991). Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge. Journal of Applied Econometrics, 6, 109-124.

Butterworth, D. And Holmes, P. (2001). The hedging effectiveness of stock index futures: Evidence for the FTSE-100 and FTSE-Mid 250 indexes traded in the U.K., Applied Financial Economics, 11, pp. 57-68.

Bystrom, H.N.E (2003). The Hedging Performance of Electricity Futures on the Nordic Power Exchange. Applied Economics, 1, 1-11.

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