Australian Dollar Price Shocks and the Australian Stock Market

This thesis presents three studies on the Australian dollar price shocks and the Australian stock market. In this thesis, we study the volatility of the major currencies, identify the effect of the Australian dollar return and volatility on six sectors of the Australian stock market, evaluate volatility from the Australian dollar to the big four banks’ shares volatility in Australia, and finally identify the risk factors for the real estate market in Australia at the fundamental factors and macroeconomies level. In chapter two, we investigate the influence of volatility of the foreign exchange rate of the US, the UK, Euro-zone, Japan, and Singapore on the volatility of the six Australian sectors within the investigated period, controlling for the time period’s global financial crisis 2007-2008. The volatility in this study was estimated by using GARCH (1,1) models. Daily data was collected for a period of 2002 to 2014. The dataset is divided into three sub-periods: before GFC (July 2002 to July 2007), during GFC (July 2007 to July 2009) and after GFC (July 2009 to July 2014). The estimated results find a strong relationship between exchange rates for the five countries with volatility of the six Australian sectors, except the health care sectors during GFC. The same relationship is evident before the GFC, except in the banks sector. The statistically significant impact of these foreign exchanges on the five Australian sectors continues after the GFC, except that the materials sector is weakly viii significant. This result is important for the investors and other market participants to understand the risk factors related to the sectors of the Australian stock market. In chapter three, we examine the volatility of the Australian dollar return and the big four Australian banks, using unique high-frequency-hourly-data from September 2012 to September 2016. This study applies an extended version of the generalised autoregressive conditional heteroskedasticity (GARCH) specifications. The GARCH variants specification includes the basic GARCH (1,1), TGARCH (1,1), EGARCH (1,1) and PARCH (1,1) models. This chapter varies from the previous Australian research studies in that detached hourly returns are used over a four-year sample period. The findings show that the volatility of the Australian dollar positively affects the big four banks in Australia in the four models and the short-term interest rate volatility negatively affects the big four banks’ volatility. The outcomes show that significant ARCH term and GARCH term impacts are present in the data, and that the standard error of PARCH model defines the volatility process better than the other three models for Commonwealth Bank (CBA), Westpac and National Australia Bank (NAB). In addition, the best model to describe the volatility for the Australia and New Zealand Banking Group (ANZ) is the TARCH model. This study is important to the market participants and investors, who want to understand the risk factor of Australian dollar volatility on the big four banks in Australia. Chapter four incorporates two objectives of the Australian real estate market. First, this research investigates the effect of TWI return on the Australian REITs volatility from 2009 to 2016 by using monthly panel data. We use fixed and random effect ix models. In the second objective, we examine the linkage between the fundamental factors and the real estate market for three major states in Australia at unit price and house price. These states are New South Wales (NSW), Victoria (VIC), and Queensland (QLD). This research uses monthly data covering the period from 2009 to 2016 by applying the VAR model. This research is important for investors, investment managers and operational decision makers to get a better understanding of how they can manage their investments more effectively during times of any changing macroeconomics factor. The findings of this research will help the real estate investment and Australians funds to reduce macroeconomics exposure. The panel fixed, and random effect models analysis concludes that there is a positive and significant relationship between the market risk and TWI with the Australian REITs, hence the hypothesis of a positive connection was accepted. The Vector Autoregressive Model (VAR) indicated that there is a positive relationship between the NSW real estate market and rental yield, while it is negative with auction clearance rate at a 5% level of significance. For Victoria, the real estate price has a positive significant effect on the Victorian rental yield and average stock on the market, while the auction clearance rate is negative. In Queensland, there is a negative relationship between the auction clearance rate and the average stock on the market with Queensland’s real-estate price. The results of this chapter help portfolio managers to reduce exposure to interest rate risks inherent in property investments by choosing externally managed REITs with low levels of debt. The topic of this thesis is timely, and the outcomes provide significant information to various groups of market participants, such as portfolio managers, policy makers and x risk managers, and to market participants who wish to understand the volatility of major currencies. Since the exchange rates and the stock market are considered as two important markers of financial markets, the outcomes of this thesis provide guidance on how investors and the market participants construct their portfolios. When the Australian dollar shocks are imminent, investors and market participants can adjust or rebalance their portfolios by looking at the sensitivity of each sector to oil price shocks and adjusting accordingly

Financial crisis and dynamic the dependency between six international currencies volatility with sectors volatility: evidence from six Australian sectors

This paper investigates the influence of volatility of foreign exchange rate of the U.S., the U.K., Netherlands, Japan, China and Singapore to the volatility of the six Australian sectors within the investigated period controlling for the time periods global financial crisis 2007-2008.The volatility in this study was estimated using GARCH(1,1) models. Daily data is collected for a period of 2002 to 2014. The dataset is divided into three sub periods: before GFC (July 2002 to July 2007), during GFC (July 2007 to July 2009) and after GFC (July 2009 to July 2014). The estimated results find strong relationship between exchange rates for the six countries with six Australian sectors volatility, except health care sectors during GFC. The same relationship is evident before GFC, except banks sector. The statistically significant impact of these foreign exchange on the six Australian sectors continues after GFC, except materials sector is weakly significantly. This result is important for the investors and other market participants to understand the risk factors related to the sectors of the Australian stock market

The Determinants of the Variability of Stock Prices - Japanese Evidence

This study examines the ability of fundamental variables and macroeconomics variables data to explain future stock market returns of Japan companies by looking at how price-earnings (P/E) ratio, book-to-market (B/M) ratio, current ratio, return on assets (ROA), inflation, changes in interest rates and changes in exchange rates influence future stock market returns. The sample size for the study is 3808 companies, listed on Tokyo stock exchange as of 2010. Data is collected during a period of eleven years, from 2000 to 2010. To test the hypotheses between dependent and independent variables, regression and correlation results are derived through the EVIEWS. Findings show strong and significant correlations between stock return and B/M ratio, P/E ratio, ROA, inflation, changes in interest rates and changes in exchange rates

The effect of economic and fundamental factors on the Australian property performance

This study examines the effect of the trade weighted index (TWI) return on the volatility of Australian Real Estate Investment Trusts (REITs). This study also contributes to the existing literature by measuring the impact of the macroeconomics and fundamental factors on the real estate market for three major states in Australia including New South Wales, Victoria, and Queensland. Using monthly house and unit prices and covering the period from 2009 to 2016, this research uses both fixed and random effect panel data models and the vector autoregressive (VAR) model. The findings of the study suggest that the movement of the TWI has positive and statistically significant impact on Australian REITs suggesting the real estate investors expect risk premium on exchange rate factor. The results also purport that both house and unit prices in Australia are exposed to the fluctuations of the fundamental risk factors. Rental yield, a return component, has a positive relationship with the real estate market in New South Wales and Queensland. The findings of the study provide significant insight to the investors in their portfolio formation since they have the understanding of the priced risk factors

The effect of economic and fundamental factors on the Australian property performance

This study examines the effect of the trade weighted index (TWI) return on the volatility of Australian Real Estate Investment Trusts (REITs). This study also contributes to the existing literature by measuring the impact of the macroeconomics and fundamental factors on the real estate market for three major states in Australia including New South Wales, Victoria, and Queensland. Using monthly house and unit prices and covering the period from 2009 to 2016, this research uses both fixed and random effect panel data models and the vector autoregressive (VAR) model. The findings of the study suggest that the movement of the TWI has positive and statistically significant impact on Australian REITs suggesting the real estate investors expect risk premium on exchange rate factor. The results also purport that both house and unit prices in Australia are exposed to the fluctuations of the fundamental risk factors. Rental yield, a return component, has a positive relationship with the real estate market in New South Wales and Queensland. The findings of the study provide significant insight to the investors in their portfolio formation since they have the understanding of the priced risk factors