Systematic Risk in Energy Businesses: Empirical Evidence for the ASEAN

This paper is conducted to provide an additional empirical evidence in relation to the estimates of equity beta for energy businesses in the ASEAN-5 including Vietnam, Thailand, the Philippines, Malaysia, and Singapore. Listed energy companies for the period from 2005 to 2015 are used. Quantile regression, together with the OLS and LAD, has been used. Findings from this paper indicate that: (i) as long as the OLS and the LAD approaches are adopted, estimates of equity beta are relatively consistent across various research periods; (ii) estimates of equity beta appear to vary substantial across different quantiles; and (iii) estimates of equity beta have appeared to vary across research periods. However, as an overall level across time and methods, a level of risk faced by a company in the energy sector is below the average of the level of risk for the entire market for the above nations. Keywords: Beta, Listed Energy Firms, Quantile regression, ASEAN JEL Classifications: G11; G1

Equity Beta for Regulated Energy Businesses in Australia: A Revisit

This paper aims to estimate the equity beta – a key input of the Capital Asset Pricing Model, for the energy businesses in Australia in the 11-year period from 2005 to 2015. Various methods are used in this paper including Quantile Regression. Listed companies in the energy industry are considered at individual and portfolio levels. Findings from this paper are  both consistent and contrast with prior related studies: (i) energy sector in Australia face a relatively low risk level compared to the market; (ii) OLS results are higher than LAD; and (iii) QR vary across different percentiles. Keywords: Equity Beta, Quantile regression, Australia. JEL Classifications: G11; G1

Modelling the relationship between crude oil and agricultural commodity prices

The food-energy nexus has attracted great attention from policymakers, practitioners and academia since the food price crisis during the 2007-2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets, which is a novel contribution. For the period January 2000 - July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000 - July 2006; (ii) August 2006 - April 2013; and (iii) May 2013 - July 2018. The Structural Vector Autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities

Modelling the relationship between crude oil and agricultural commodity prices

The food-energy nexus has attracted great attention from policymakers, practitioners and academia since the food price crisis during the 2007-2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets, which is a novel contribution. For the period January 2000 - July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000 - July 2006; (ii) August 2006 - April 2013; and (iii) May 2013 - July 2018. The Structural Vector Autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities

SECURITY CAPABILITY ANALYSIS OF COGNITIVE RADIO NETWORK WITH SECONDARY USER CAPABLE OF JAMMING AND SELF-POWERING

This paper investigates a cognitive radio network where a secondary sender assists a primarytransmitter in relaying primary information to a primary receiver and also transmits its own information toa secondary recipient. This sender is capable of jamming to protect secondary and/or primary informationagainst an eavesdropper and self-powering by harvesting radio frequency energy of primary signals.Security capability of both secondary and primary networks are analyzed in terms of secrecy outageprobability. Numerous results corroborate the proposed analysis which serves as a design guidelineto quickly assess and optimize security performance. More importantly, security capability trade-offbetween secondary and primary networks can be totally controlled with appropriate selection of systemparameters

Systematic risk at the industry level: A case study of Australia

The cornerstone of the capital asset pricing model (CAPM) lies with its beta. The question of whether or not beta is dead has attracted great attention from academics and practitioners in the last 50 years or so, and the debate is still ongoing. Many empirical studies have been conducted to test the validity of beta within the framework of CAPM. However, it is a claim of this paper that beta at the industry level has been largely ignored in the current literature. This study is conducted to examine if beta, proxied for a systematic risk, should be considered valid in the application of the CAPM at the industry level for Australia using daily data on 2200 stocks listed on the Australian Securities Exchange from January 2007 to 31 December 2016. Various portfolio formations are utilized in this paper. General economic conditions such as interest rate, inflation, and GDP are examples of systematic risk. Findings from this study indicate that the selection of portfolio construction, estimation technique, and news about economic conditions significantly affects the view whether or not beta should be considered as a valid measure of systematic risk