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Effects of interest rate, exchange rate and their volatilities on stock prices: evidence from banking industry of Pakistan  [PDF]
Syed Tehseen JAWAID,Anwar Ul HAQ
Theoretical and Applied Economics , 2012,
Abstract: This study investigates the effects of exchange rate, interest rates, and their volatilities on stock prices of banking industry of Pakistan. Cointegration results suggests the existance of significant negative long run relationship between exchange rate and short term interest rate with stock prices. On the other hand, positive and significant relationship exists between volatilities of exchange rate and interest rate with stock prices. Causality analysis confirms bidirectional causality between exchange rate and stock prices. Whereas, unidirectional causality runs from short term interest rate to stock prices. Sensitivity analysis confirms that the results are robust. It is suggested that investors should invest in banking sector stocks when exchange rate and interest rates are highly volatile. The result also supports the view that exchange rate and interest rate can be used as an indicator for investment decision making in banking sector stocks.
Interest Rate Risk of Bond Prices on Macedonian Stock Exchange - Empirical Test of the Duration, Modified Duration and Convexity and Bonds Valuation  [PDF]
Zoran Ivanovski,Toni Draganov Stojanovski,Nadica Ivanovska
Quantitative Finance , 2012,
Abstract: This article presents valuation of Treasury Bonds (T-Bonds) on Macedonian Stock Exchange (MSE) and empirical test of duration, modified duration and convexity of the T-bonds at MSE in order to determine sensitivity of bonds prices on interest rate changes. The main goal of this study is to determine how standard valuation models fit in case of T- Bonds that are traded on MSE and to verify whether they offer reliable results compared with average bonds prices on MSE. We test the sensitivity of T- Bonds on MSE on interest rate changes and determine that convexity is more accurate measure as approximation of bond prices changes than duration. Final conclusion is that T-Bonds traded at MSE are not sensitive on interest rate changes due to institutional investors' permanent higher demand and at the same time market limited offer of risk-free instruments.
The Interaction between Exchange Rates and Stock Prices: An Australian Context  [cached]
Noel Dilrukshan Richards,John Simpson,John Evans
International Journal of Economics and Finance , 2009, DOI: 10.5539/ijef.v1n1p3
Abstract: The aim of this paper is to examine the interaction between stock prices and exchange rates in Australia. During the period of the study, the value of the stock market increased by two-thirds and the Australian dollar exchange rate appreciated by almost one-third. The empirical analysis employed provides evidence of a positive co-integrating relationship between these variables, with Granger causality found to run from stock prices to the exchange rate during the sample period. Although commodity prices have not been included, the significance of the results lends support to the notion that these two key financial variables interacted in a manner consistent with the portfolio balance model, that is, stock price movements cause changes in the exchange rate. This challenges the traditional view of the Australian economy as export-dependent, and also suggests that the Australian stock market has the depth and liquidity to adequately compete for both domestic and international capital against other larger markets.
The Long-Run and Short-Run Relationship between the Exchange Rates and Stock Market Prices  [PDF]
Clement Mwaanga, Nsama Njebele
Journal of Financial Risk Management (JFRM) , 2017, DOI: 10.4236/jfrm.2017.64023
Abstract: The aim of this study was to investigate the relationship between the exchange rate and the stock market price in Zambia using the time series monthly data from2004 to 2016. To measure the stock market prices, we used LuSE overall index (LuSE Index) and the exchange rate was measured using the Zambia’s Real Effective Exchange Rate (REER). In order to establish the relationship between the exchange rate and the stock market price, we employed the Vector Autoregression (VAR) based cointegration test methodology and Auto Regression distribution lag (ARDL) bound tests. The Johansson cointegration test results revealed the existence of the cointegration long run. However the Auto Regression distribution lag bound tests show that its impact is statistically insignificant. The Vector Error Correction Model (VECM) revealed that there is no short-run relationship between the exchange rate and stock market prices. The findings of this study have implications for academicians, policy makers and investors.
Biofuel and Food-Commodity Prices  [PDF]
Gal Hochman,Scott Kaplan,Deepak Rajagopal,David Zilberman
Agriculture , 2012, DOI: 10.3390/agriculture2030272
Abstract: The paper summarizes key findings of alternative lines of research on the relationship between food and fuel markets, and identifies gaps between two bodies of literature: one that investigates the relationship between food and fuel prices, and another that investigates the impact of the introduction of biofuels on commodity-food prices. The former body of literature suggests that biofuel prices do not affect food-commodity prices, but the latter suggests it does. We try to explain this gap, and then show that although biofuel was an important contributor to the recent food-price inflation of 2001–2008, its effect on food-commodity prices declined after the recession of 2008/09. We also show that the introduction of cross-price elasticity is important when explaining soybean price, but less so when explaining corn prices.
Commodity Food Prices: Review and Empirics  [PDF]
Anthony N. Rezitis,Maria Sassi
Economics Research International , 2013, DOI: 10.1155/2013/694507
Abstract: The present paper provides a literature review of studies examining the potential causes and consequences of recent surges in food and agricultural commodity prices. Furthermore, this paper uses the structural trend methodology proposed by Koopman et al. (2009) to analyze movements in the IMF monthly commodity food price index for the period 1992(11)–2012(10) and to provide forecasts for the period 2012(11)–2014(12). The empirical results indicate that commodity food prices present seasonality and cyclicality with the longest periodicity of two years. The empirical findings identify certain structural breaks in commodity food price series as well as outliers. These structural breaks seem to capture the trend component of the price series well, while the outliers take account of temporal effects, that is, short-lived spikes. Finally, the presented forecasts show high and volatile commodity food prices. 1. Introduction Commodity food prices have surged upwards in dramatic fashion in recent years after several decades of relative stability and low levels. In particular, commodity food prices increased dramatically between late 2006 and mid-2008, and by reaching high levels later on (i.e., during 2010, early 2011, and the third quarter of 2012), they caused serious concerns about a repeat of the 2006–2008 food crisis. This phenomenon has motivated several analyses of the factors that have caused commodity food prices to increase in recent years. The purpose of the present paper is twofold. First, it reviews the empirical studies that identify and analyze the possible causes of the recent food and agricultural commodity spikes. Second, it uses a structural time series approach to analyze the behavior of the monthly commodity food price index for the past 20 years. In the empirical part, the present paper departs from previous detrending methods and employs a structural time series approach [1], which provides the possibility of discovering commodity price cycles. Furthermore, this approach permits not only the possibility of stochastic cycles but also the presence of stochastic trends in levels and growth rates and provides efficient forecasts on the commodity food price index. The remainder of this paper is organized as follows. Section 2 presents and discusses the literature on the causes of commodity food price increases in recent years. In Section 2.1, specific discussion is devoted to the possible linkages between fuel and food prices, while in Section 2.2 the possible relation between speculation and food prices is provided. Section 3 presents the
Implied Bond and Derivative Prices Based on Non-Linear Stochastic Interest Rate Models  [PDF]
Ghulam Sorwar, Sharif Mozumder
Applied Mathematics (AM) , 2010, DOI: 10.4236/am.2010.11006
Abstract: In this paper we expand the Box Method of Sorwar et al. (2007) to value both default free bonds and interest rate contingent claims based on one factor non-linear interest rate models. Further we propose a one-factor non-linear interest rate model that incorporates features suggested by recent research. An example shows the extended Box Method works well in practice.
The Gibson Paradox: Real Gold, Interest Rates and Prices  [cached]
Adam Abdullah
International Business Research , 2013, DOI: 10.5539/ibr.v6n4p32
Abstract: This paper aims to provide an analysis and explanation of the curious empirical relationships that exist between the price of gold, the interest rate and commodity prices, operating under the English 19th century fractional reserve gold standard and the modern American fractional reserve fiat paper standard, known as the Gibson Paradox. This paper argues that the value and purchasing power of the British pound and American dollar are managed in relation to their rate of exchange with gold and the real rate of interest, such that, changes in the general level of prices are the effect and not the cause.
Forecasting Energy Commodity Prices Using Neural Networks  [PDF]
Massimo Panella,Francesco Barcellona,Rita L. D'Ecclesia
Advances in Decision Sciences , 2012, DOI: 10.1155/2012/289810
Abstract: A new machine learning approach for price modeling is proposed. The use of neural networks as an advanced signal processing tool may be successfully used to model and forecast energy commodity prices, such as crude oil, coal, natural gas, and electricity prices. Energy commodities have shown explosive growth in the last decade. They have become a new asset class used also for investment purposes. This creates a huge demand for better modeling as what occurred in the stock markets in the 1970s. Their price behavior presents unique features causing complex dynamics whose prediction is regarded as a challenging task. The use of a Mixture of Gaussian neural network may provide significant improvements with respect to other well-known models. We propose a computationally efficient learning of this neural network using the maximum likelihood estimation approach to calibrate the parameters. The optimal model is identified using a hierarchical constructive procedure that progressively increases the model complexity. Extensive computer simulations validate the proposed approach and provide an accurate description of commodities prices dynamics. 1. Introduction Energy is a principal factor of production in all aspects of every economy. Energy price dynamics are affected by complex risk factors, such as political events, extreme weather conditions, and financial market behavior. Crude oil is a key and highly transportable component for the economic development and growth of industrialized and developing countries, where it is refined into the many petroleum products we consume. Over the last 10 years, the global demand for crude oil and gas has increased largely due to the rapidly increasing demands of non-OECD countries, especially China [1]. Local gas and coal are mainly used in the electricity generation process and recently their supply and demand experienced a profound transformation. The economic exploitation at higher prices of non-conventional forms of oil and gas, such as shale gas and shale oil, is modifying the demand for the three fossil fuels. The production of shale gas in the US will shortly bring the US to be less dependent on imported oil and, in addition, it means a large part of the electricity generation process has been switched from coal to gas. The deregulation of gas and electricity markets makes the prices of these commodities to be formed in competitive markets. Crude oil and natural gas in the last decade have been largely traded on spot, derivative, and forward markets by producers, consumers, and investors. Crude oil and gas are
Commodity Prices Rise Sharply at Turning Points  [PDF]
Bin Li,K. Y. Michael Wong,Amos H. M. Chan,Tsz Yan So,Hermanni Heimonen,David Saad
Quantitative Finance , 2015,
Abstract: Commodity prices depend on supply and demand. With an uneven distribution of resources, prices are high at locations starved of commodity and low where it is abundant. We introduce an agent-based model in which agents set their prices to maximize profit. At steady state, the market self-organizes into three groups: excess producers, consumers, and balanced agents. When resources are scarce, prices rise sharply at a turning point due to the disappearance of excess producers. Market data of commodities provide evidence of turning points for essential commodities, as well as a yield point for non-essential ones.
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