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The purpose of this paper is to pay more attention to four different research questions at least. One is that this study intends to explore the changes of the risk-return relationship over time, because the institutions and environment have changed a lot and might tend to influence the risk-return regime in the Chinese stock markets. The second question is whether there is any difference for the risk-return relationship between Shanghai and Shenzhen stock markets. The third question is to compare the similarities and dissimilarities of the risk-return tradeoff for different frequency data. The fourth question is to compare the explanation power of different GARCH-M type models which are all widely used in exploring the risk-return tradeoff.

This paper investigates the risk-return tradeoff in the Chinese emerging stock markets with a sample including daily, weekly and monthly market return series. A group of variant specifications of GARCH-M type models are used to test the risk-return tradeoff. Additionally, some diagnostic checks proposed by Engle and Ng (1993) are used in this paper, and this will help to assess the robustness of different models.

The empirical results show that the dynamic risk-return relationship is quite different between Shanghai and Shenzhen stock markets. A positive and statistically significant risk-return relationship is found for the daily returns in Shenzhen Stock Exchange, while the conditional mean of the stock returns is negatively related to the conditional variance in Shanghai Stock Exchange. The risk-return relationship usually becomes much weaker for the lower frequency returns in both markets. A further study with the sub-samples finds a positive and significant risk-return trade-off for both markets in the second stage after July 1, 1999.

This paper extends the existing related researches about the Chinese stock markets in several ways. First, this study uses a longer sample to investigate the relationship between stock returns and volatility. Second, this study estimates the returns and volatility relationship with different frequency sample data together. Third, a group of variant specifications of GARCH-M type models are used to test the risk-return tradeoff. In particular, the author employs the Component GARCH-M model which is relatively new in this line of research. Fourth, this study investigates if there is any structural break affecting the risk-return relationship in the Chinese stock markets over time.

The risk-return relationship is one of the most widely investigated topics in finance, yet this relationship remains one of the most controversial topics. The purpose of this paper is to investigate the asymmetric volatility and the risk-return tradeoff at the sector level in the emerging stock market of Jordan.

Data consist of daily prices for 22 sub-sectors spanning from August 1, 2006, to September 30, 2015, covering the periods of pre, during, and after the global financial crisis. The EGARCH-M model is used to document the patterns of asymmetric volatility of sub-sector returns and the risk-return tradeoff during the non-overlapping three sub-sample periods.

The major findings of this study are as follows. In the pre-crisis period, the results suggest some evidence of a positive relationship between risk and return. The results also reveal that good news has more effect than bad news during the same period. In the crisis period, there is a negative but insignificant risk-return relationship and negative shocks have more impact than positive ones. In the post-crisis period, the authors find positive but insignificant risk-return tradeoff with weak evidence of volatility asymmetry.

The results have major implications for investors willing to engage their investment decisions in the Amman Stock Exchange (ASE) and for policymakers who seek to attract and retain regional and international investors. Since the empirical investigation is conducted at the sector level, the study may aid investors to target specific sub-sectors with positive and significant risk-return tradeoff. In addition, investors need to monitor the asymmetric patterns which make the level of risk-aversion more susceptible to coming news. For policymakers, the latest infrastructure reforms are crucial to achieving the potential for growth but the ASE market authority needs to undergo further reforms and provide various promotional incentives.

Although there are numerous studies on asymmetric volatility and risk-return tradeoff, there is a lack of parallel studies at the sector level for both developed and emerging stock markets. Such assessment at the sector level is crucial for international investors after their choice of countries or markets for better choice of portfolio diversification and allocation of financial resources.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.

The purpose of this paper is to estimate the performance of 188 mutual funds relative to the risk/return frontier accounting for the transaction costs of producing a portfolio of investments.

The directional output distance function is used to estimate mutual fund performance. The method allows the data to define a frontier of return and risk accounting for the transaction costs associated with securities management and production of risky returns. Proxies for the transaction costs of producing a portfolio of securities include the turnover ratio, load, expense ratio, and net asset value. The estimates of mutual fund performance are bootstrapped to account for the unknown data generating process. By comparing each mutual fund’s performance relative to the capital market line the authors determine how the fund should adjust their portfolio in regard to risk and return in order to maximize the inefficiency adjusted Sharpe ratio.

The bootstrapped estimates indicate that the average mutual fund could simultaneously expand return and contract risk by 3.2 percent if it were to operate on the efficient frontier. After projecting each mutual fund’s return and risk to the efficient frontier the authors find that a majority of the mutual funds should reduce risk to be consistent with the capital market line.

Many researchers have used data envelopment analysis to estimate a piecewise linear frontier of risk and return to measure mutual fund performance. To the authors’ knowledge the research is the first to use a twice-differentiable quadratic directional distance function to measure the managerial performance and risk/return tradeoff of mutual funds.

The purpose of this paper is to consider the impact on bank risk of portfolio diversification between traditional margin income and fee‐based income for banks operating in Australia.

Considering several performance variables, this analysis compares the benefits of diversification across different bank types relative to margin income and fee income. Further, regression analysis considers bank risk and revenue concentration.

This paper documents that fee‐based income is riskier than margin income but offers diversification benefits to bank shareholders. While improving bank risk‐return tradeoff, these benefits are of second order importance compared to the large negative impact of poor asset quality on shareholder returns.

These results have implications for all stakeholders in Australian banks. The results suggest that shareholders of banks will benefit from increased bank exposure to non‐interest income via diversification. From a regulatory perspective, diversification reduces the possibility of systemic risk, but caution must be offered with respect to banks pursuing absolute returns rather than monitoring risk‐return trade‐offs, and so exploiting the benefits of the implied guarantee offered by “too big to fail” However, shareholders should also monitor bank exposure to non interest income to ensure that they do not become over‐exposed to the point where the volatility effect outweighs the diversification benefits.

The results of this study suggest that Australian regulators should consider requiring increased disclosure of the composition of bank non‐interest income. Such disclosure would aid in understanding the changing nature of banking in Australia. Given the recent sub‐prime crisis in the USA and the role played by fee based income sourced from securitization, increased disclosure of the nature of bank non interest income is now of global importance. This disclosure is particularly germane within the context of the implementation of Basle II, with its increased emphasis upon market discipline, given that Stiroh found increased disclosure in this area is accompanied by improved market pricing for risk.

This study investigates the impact of uncertainty on the mean-variance relationship. We find that the stock market's expected excess return is positively related to the market's conditional variances and implied variance during low uncertainty periods but unrelated or negatively related to conditional variances and implied variance during high uncertainty periods. Our empirical evidence is consistent with investors' attitudes toward uncertainty and risk, firms' fundamentals and leverage effects varying with uncertainty. Additionally, we discover that the negative relationship between returns and contemporaneous innovations of conditional variance and the positive relationship between returns and contemporaneous innovations of implied variance are significant during low uncertainty periods. Furthermore, our results are robust to changing the base assets to mimic the uncertainty factor and removing the effect of investor sentiment.

This paper seeks to examine the benefits of further diversifying a global portfolio of financial assets with New Zealand farm real estate (FRE).

The paper compares efficient sets generated with and without FRE using portfolio theory.

The results show that given the predominantly negative correlation between FRE and financial assets, the risk‐return tradeoffs of portfolios of financial assets can be improved significantly. The diversification benefits measured in terms of risk reduction, return enhancement, and improvement in the Sharpe performance ratios are robust under a number of FRE risk‐return scenarios as well as under high and low inflationary periods. Using five and ten‐year rolling periods it also finds that FRE is a consistent part of risk efficient portfolios. Consistent with the results reported in Lee and Stevenson, for the UK real estate the risk reduction benefits of diversifying with FRE are larger than the risk enhancement benefits.

The results suggest that FRE takes on a consistent role of risk‐reducer rather than a return‐enhancer in a globally diversified portfolio. FRE appears to deserve more serious consideration by investment practitioners that it has been accorded in the past.

The study examines the role of direct real estate in a globally diversified portfolio of financial assets.

For years institutional investors have concentrated their holdings of common stock in a relatively small group, the “institutional favorites.” While the market performance and changes in the composition of institutional favorites were reported in financial literature from the 1950s, no recent studies concerning the characteristics of firms classified as institutional favorites have been presented. The purpose of this study is to establish a financial profile for firms currently favored by financial institutions, and to determine whether these firms differ significantly from firms selected at random. It is concerned with those variables that are indicators ofthe firm’s risk‐return trade off character. As in previous studies of this nature, Multiple Discriminant Analysis was used. The results were somewhat surprising in that institutions did not seem to be drawn to what are perceived to be growth firms. As was expected, however, institutional favorites were larger in size than firms selected at random.

This paper aims to test the hedging ability of housing investment against inflation in Japan and the USA during the period 2000–2020.

This study applies the deep learning method and The exponential general autoregressive conditional heteroskedasticity in mean (1, 1) model with breaks.

Within the asymmetric framework, it is found that housing returns (HR) can hedge against inflation in both these markets, which mentions that when investing in the housing market in Japan and the USA, investors are compensated for bearing from inflation. This result is consistent with Fisher’s hypothesis. Especially, the empirical results show that the risk-return tradeoff is available in Japan’s housing market and not available in the US housing market. Any signal of a high inflation rate – referred to as “bad news” – may cause a drop in HR in Japan and a raise in the USA.

To the best of the author’s knowledge, this is one of the first studies using the deep learning method (long short-term memory model) to estimate the expected/unexpected inflation rates.

Overnight risk is of particular interest for many market participants including traders who provide liquidity to the market, but also to market participants with longer investment horizons who want to determine whether a given risk–return tradeoff can justify possible intermediate portfolio hedging transactions. Overnight risk may in particular play a highly significant role in emerging markets, given that information is incorporated into prices at a slower rate and liquidity may hinder a quick unwinding of portfolio positions.