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Extensive studies have investigated the relation between risk and return in the stock and major asset markets, whereas little studies have been done for housing, particularly the Australian housing market. This study aims to determine the relationship between housing risk and housing return in Australia.

The analysis of this study involves two stages. The first stage is to estimate the presence of volatility clustering effects. Thereafter, the relation between risk and return in the Australian housing market is assessed by using a component generalised autoregressive conditional heteroscedasticity-in-mean (C-CARCH-M) model.

The empirical results show that there is a strong positive risk-return relationship in all Australian housing markets. Specifically, comparable results are also evident in all housing markets in various Australian capital cities, reflecting that Australian home buyers, in general, are risk reverse and require a premium for higher risk level. This could be attributed the unique characteristics of the Australian housing market. In addition, there is evidence to suggest that a stronger volatility clustering effect than previously documented in the daily case.

The findings enable more informed and practical investment decision-making regarding the relation between housing return and housing risk.

This paper is the first study to offer empirical evidence of the risk-return relationship in the Australian housing market. Besides, this is the first housing price volatility study that utilizes daily data.

The purpose of this paper is to examine if the enactment of Sarbanes-Oxley (SOX) resulted in lower risk premium and return volatility in the US stock markets. The paper examines the two components of excess return (total risk premium) separately: the amount of volatility (risk) and the unit price of risk (risk premium).

The authors use a Component Generalized Autoregressive Conditional Heteroskedasticity approach to estimate the permanent and transitory component of share price volatility. The authors then use the predicted volatility to measure the unit price of risk and its changes due to the enactment of the SOX Act.

The results regarding excess returns indicate that the implementation of SOX had a positive effect on the market. A positive effect means a steady decrease in required excess rates of returns due to the implementation of SOX. The years leading up to the implementation of SOX are characterized by significant sources of uncertainty. Around the implementation of SOX, the authors observe a long-term reduction in return volatility (risk), and a temporary reduction in the unit price of risk. Subsequent to the implementation, investors gained confidence in the effectiveness of internal controls over the financial reporting process, which helped in reducing the information risk and, therefore, the risk premium.

The authors find that total risk premium decreased over extended periods. The authors conclude that the enactment of SOX helped in reducing the uncertainty in the US capital market resulting in a reduction of total risk premiums and hence the cost of capital.

The results have implications for policy makers, investors and researchers in general and those in the US markets in particular. The results are important because it allows policy makers and regulators to improve on how they design and implement accounting, market and finance regulations and reforms.

The study shows how financial markets react to regulations and the authors also provide information on investors’ reaction as firms adjust to changing regulations. The results of the study allows regulators to potentially use a more refined or targeted approach when introducing new regulations. It also allows investors to make informed investment decisions as they relate to risk premium requirements, which in turn may allow investors to allocate capital more efficiently.

There are many studies concerning the enactment of SOX but few, if any, existing studies examine the original intent of SOX: to calm the US equity markets and restore market confidence from a return volatility perspective. The results have implications for policy makers, investors and researchers in general and those in the US markets in particular. The results are important because it allows policy makers and regulators to improve on how they design and implement accounting, market and finance regulations and reforms.

This study is the first to examine the role of idiosyncratic risk in the pricing of European real estate equities. The capital asset pricing model predicts that in equilibrium, investors should hold the market portfolio. As a result, investors should only be rewarded for carrying undiversifiable systematic risk and not for diversifiable idiosyncratic risk. The study is adding to the growing body of countering studies by first examining time trends of idiosyncratic risk and subsequently the pricing of idiosyncratic risk in European real estate equities. The paper aims to discuss these issues.

The study analyses 293 real estate equities from 16 European capital markets over the 1991-2011 period. The framework of Fama and MacBeth is employed. Regressions of the cross-section of expected equity excess returns on idiosyncratic risk and other firm characteristics such as beta, size, book-to-market equity (BE/ME), momentum, liquidity and co-skewness are performed. Due to recent evidence on the conditional pricing of European real estate equities, the pricing is also investigated using the conditional framework of Pettengill et al. Either realised or expected idiosyncratic volatility forecasted using a set of exponential generalized autoregressive conditional heteroskedasticity models are employed.

The initial analysis of time trends in idiosyncratic risk reveals that while the early 1990s are characterised by both high total and idiosyncratic volatility, a strong downward trend emerged in 1992 which was only interrupted by the burst of the dotcom bubble and the 9/11 attacks along with the global financial and economic crisis. The largest part of total volatility is idiosyncratic and therefore firm-specific in nature. Simple cross-correlations indicate that high beta, small size, high BE/ME, low momentum, low liquidity and high co-skewness equities have higher idiosyncratic risk. While size and BE/ME are priced unconditionally from 1991 to 2011, both measures of idiosyncratic risk fail to achieve significance at reasonable levels. However, once conditioned on the general equity market or real estate equity market, a strong positive relationship between idiosyncratic risk and expected returns emerges in up-markets, while the opposite relationship exists in down-markets. The relationship is robust to firm-specific factors and a series of robustness checks.

The results show that ignoring the conditional relationship between idiosyncratic risk and returns might result in the false realisation that idiosyncratic risk does not matter in the pricing of risky (real estate) assets.

This study is the first to examine the role of idiosyncratic risk in the pricing of European real estate equities. The study reveals differences in the pricing of European real estate equities and US REITs. The study highlights that ignoring the conditional relationship between idiosyncratic risk and returns might result in the false realisation that idiosyncratic risk does not matter in the pricing of risky assets.

In asset management, what if clients want to purchase protection from risk factors, under the form of variance risk premia. This paper aims to address this topic by developing a portfolio optimization framework based on the criterion of the minimum variance risk premium (VRP) for any investor selecting stocks with an expected target return while minimizing the risk aversion associated to the portfolio according to “good” and “bad” times.

To accomplish this portfolio selection problem, the authors compute variance risk-premium as the difference from high-frequencies' realized volatility and options' implied volatility stemming from 19 stock markets, estimate a 2-state Markov-switching model on the variance risk-premia and optimize variance risk-premia portfolios across non-overlapping regions. The period goes from March 16, 2011, to March 28, 2018.

The authors find that optimized portfolios based on variance-covariance matrices stemming from VRP do not consistently outperform the benchmark based on daily returns. Several robustness checks are investigated by minimizing historical, realized or implicit variances, with/without regime switching. In a boundary case, accounting for the realized variance risk factor in portfolio decisions can be seen as a promising alternative from a portfolio performance perspective.

As a new management “style”, the realized volatility approach can, therefore, bring incremental value to construct the conditional covariance matrix estimates.

The authors assess the portfolio performance determined by the variance-covariance matrices that are derived by four models: “naive” (Markowitz returns benchmark), non-switching VRP, maximum likelihood regime-switching VRP and Bayesian regime switching VRP. The authors examine the best return-risk combination through the calculation of the Sharpe ratio. They also assess another different portfolio strategy: the risk parity approach.

This paper aims to examine the nexus between the pricing of market-wide volatility risk and distress risk in the cross-section of portfolio returns for the 1990-2011 time period. The author expands upon prior research by constructing an ex post factor that mimics aggregate volatility risk based on the new VIX index of the Chicago Board Options Exchange, termed FVIX, as well as focuses on volatility risk in crisis versus non-crisis time periods.

The author investigates the relationship between volatility and distress risk using several techniques in the empirical finance literature. Specifically, the author investigates the behavior of correlations between risk factors as well as the correlations between factor loadings when using the Fama and French research portfolios as our test assets for different time periods. Additionally, the author examines the variation in the volatility factor loadings across the size- and value-sorted portfolios and assesses whether augmenting conventional pricing models with a volatility factor leads to a higher goodness-of-fit in pricing the 25 size- and value-sorted portfolios.

The author’s results suggest that factor volatilities are high during periods of market turmoil. In addition, the author presents evidence indicating that a factor mimicking innovation in volatility (based on the new VIX) is correlated with the market and momentum factors, while exhibiting the uncorrelated behavior with respect to the size, value and liquidity factors when using data from 1990 through 2011. In this paper, the author finds that the aggregate volatility factor’s correlation with the market and momentum factors increases during crisis periods. In periods of relative market tranquility, correlations decrease significantly. In examining multivariate factor loadings for the test assets, the results provide no clear pattern with regard to the variation of the volatility loadings across the book-to-market and size dimensions. Furthermore, the author finds that conventional pricing models are comparable to FVIX-augmented pricing models, in terms of goodness-of-fit, when pricing the 25 Fama-French size- and value-sorted portfolios. Additionally, when using the FVIX volatility factor to proxy for aggregate volatility risk, the coefficients are never significant statistically, thus revealing that innovations in aggregate volatility based on the new VIX index do not constitute a priced risk factor in the cross-section of returns.

The author’ finding indicates an absence of strong variation of the volatility factor loadings across the Fama-French research portfolios. In particular, the asset pricing results cast doubt on whether a factor mimicking innovations in aggregate volatility based on the new VIX index is priced. In agreement with prior research, the author believes that the inseparability of volatility and jump risk in the VIX can be a possible explanation of the current findings in this paper.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

We investigate the link between firm volatility and risk-taking (RT) among 4232 institutions across 11 countries during the period of 2000–2017 and find RT is negatively correlated with volatility measures. Second, a decomposition of the primary risk measure, the Z score and Merton distance-to-default, reveals that high RT contributed to lower stock return volatility mainly through better corporate governance, firm size, higher information efficiency, and strong BOD. Third, Australia firms engage in more RT compared to other countries. Finally, majority of the selected countries show the negative impact of RT in firm volatility in the pre-crises period (2002–2006) and during the crises period (2007–2009) but not in the post-crises period (2010–2014).

This paper aims to analyse the governance structures of the Indonesian chilli value chain, price volatility issues across the chain and to critically explore the value chain actors' perceptions and responses to price volatility.

The authors used semi-structured interviews with 148 primary actors of the Indonesian chilli value chain. In-depth interviews with 22 key stakeholders – from local, provincial and national levels – were conducted in order to obtain additional information about their roles and the current policies and challenges in the chilli industry. The authors also conducted focus group discussions (FGDs) with farmers and support providers and held a national workshop to gather governance and price volatility risk-related information.

The Indonesian chilli value chains are long, complex and involve multiple actors. Most relationships within the value chains are based on market governance in which price regulates transactions. Most value chain actors shared a similar perception of price volatility and its causes. Under different governance structures, the value chain actors identified production, product characteristics and marketing as a major cause of price volatility. Although strategies applied by the value chain actors varied, in the main they are all aimed at minimising the impact of price volatility. Contractual arrangements are viable alternatives to minimising price risk.

This research relies primarily on qualitative data derived from purposive data collection methods, which may reduce the ability to generalise the findings. A quantitative analysis is required to validate the level of price volatility perceived by the stakeholders and to assess the cause and impact of price volatility across the chain. Future research should focus on proposing and assessing potential policy interventions that address price volatility, in order to facilitate the development of the Indonesian chilli industry.

To the best of the author’s knowledge, this study is the first to investigate the governance structures of the Indonesia chilli value chain, the value chain actors' perceptions of price volatility and their responses under the different types of governance in a developing country context.

This paper aims to examine the relationship between the conditional variance of the factors from the Fama–French three‐factor model and macroeconomic risk, where macroeconomic risk is proxied by the conditional variance for a default risk premium and real gross domestic product (GDP) growth.

A generalised autoregressive conditional heteroscedastic model is used to generate the conditional volatilities and bivariate Granger causality tests are used to examine the empirical relationship between the risk measures.

Past values of the conditional variance for a default risk premium have information that is precedent to the conditional volatility for value premium and the small stock risk premium, and the conditional variance for the market risk premium has information about the future volatility of macroeconomic risk, as proxied by the conditional variance for GDP growth.

The implications are that conditional volatility associated with default is related to current and future volatility in value premium; however, volatility associated with the market risk premium appears to be a predictor of future macroeconomic risk. A caveat is that the results are dependent on the proxies used for macroeconomic risk and more refined measures of macroeconomic risk may yield different results.

This paper suggests that examination of the relationship between the volatility of macroeconomic factors and the explanatory factors in asset‐pricing models will help to further understanding of the relationship between risk and expected return.

This paper focuses directly on the links between risk associated with the Fama–French factors and macroeconomic risk. This added knowledge is beneficial to practitioners and academics whose interest lies in asset price modelling.