by Jack Penm and R.D. Terrell College of Business and Economics The Australian National University

1. by Jack Penm and R.D. Terrell College of Business and Economics The Australian National University SHORT SELLING ASSESSMENT WHERE CONSUMER PRICES INVOLVE BOTH CURRENCY TRADES AND WEATHER SHOCKS

2. Ten Reading Questions Can currency short selling decisions diversify a currency investment portfolio? Can an investor buy one short-term bullish currency, and short sell another short-term bearish currency? Can an investor buy one short-term bearish currency, and short sell another short-term bullish currency? Can Granger causal relations exist between an exchange rate and a price index? Can Granger causal relations be valuable in capturing forex trading opportunities?

3. Are error-correction technologies suitable for identifying causal links between prices and exchange rates? Is VECM suitable for identifying cointegrating vectors? Is the forgetting factor technique a data weighting process? Does subset time series modelling include full-order models? Can climate change modelling portray the potential hazards for small developing states such as Taiwan?

4. OUTLINE Research has been conducted in short selling decisions where consumer prices involve both currency trades and weather shocks, using new sparse patterned forgetting factor inclusive time-series approaches. Our results indicate that, in order to maximise forex trading opportunities and diversify a currency investment portfolio, an investor might choose to buy one short-term bullish currency, and short sell another short-term bearish currency.

5. Subset AR Modelling and the Forgetting Factor Subset autoregressive (AR) modelling includes full-order AR models, and researchers use this approach whenever measurements exhibit some periodicity. If the underlying true AR process has a subset structure, the suboptimal full-order specification can give rise to inefficient estimates and inferior projections. The forgetting factor is a data weighting process that gives more weight to recent observations and less weight to earlier data. The forgetting factor has been widely used to capture nonstationarity and improve forecasting performance.

6. VECM Modelling In vector time-series analysis, VECMs have become an important means of detecting Granger causal relations and cointegrating relations. Commonly employed full-order VECM models assume nonzero entries in all their coefficient matrices. However, applications of VECM models to economic and financial time-series data have revealed that zero entries are indeed possible. This VECM, with allowance for possible zero entries in the coefficient matrices, is referred to as a sparse patterned VECM. Sparse patterned VECM models include full-order models.

7. VECM Modelling The existence of zero entries has not been fully explored in causality and cointegration theory. Specifically, if indirect causality or Granger non-causality exists among the variables, the use of ‘overparameterised’ full-order VECM models may weaken the power of statistical inferences. We argue that the sparse patterned VECM is a more straightforward and effective means of testing for both indirect causality and Granger non-causality. The same benefits will be present if the sparse patterned VECM is used to analyse cointegrating relations

8. VECM Modelling Identical Granger causality, Granger non-causality and indirect causality relations among the variables can be detected by sparse patterned VAR models or by equivalent VECM models. Granger non-causality and/or indirect causality in the framework of a sparse patterned VECM, are more likely to be correctly defined by finding zero coefficient entries where the underlying process does indeed include such zero entries.

9. Summary At the end of January 2001, a US dollar was valued at 32.63 Taiwanese dollars, and a Euro was valued at 30.61 Taiwanese dollars. An investor who borrowed a million US dollars to exchange Taiwanese dollars, received 32.63 million Taiwanese dollars, and then exchanged those Taiwanese dollars to receive 1.066 million Euros.

10. At the end of January 2010, a Euro was valued at 45.50 Taiwanese dollars, and a US dollar was valued at 31.87 Taiwanese dollars. This investor sold the 1.066 million Euros in exchange for Taiwanese dollars, received 48.503 million Taiwanese dollars, and then exchanged those Taiwanese dollars to receive 1.5219 million US dollars. After returning the borrowed one million US dollars, this investor gained 0.5219 million US dollars

11. Conclusion The paper presents the causal links between exchange rates and macroeconomic price indices, in complex forex short selling environments. This approach can be used to guide further appropriate use of data in changing currency trade environments. As the development course of climate change is a long-term slowly evolving underlying process in VECM modelling, the effects of climate change on currency trade are portrayed in the cointegrating relations.