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Numerous studies argue that the market risk premium is associated with expected economic conditions and show that proxies for expected business conditions indeed predict aggregate market returns. By directly estimating short- and long-run expected economic growth, we show that short-run expected economic growth is negatively related to future returns, whereas long-run expected economic growth is positively related to aggregate market returns. In addition, our findings indicate that the risk premium has both high- and low-frequency fluctuations and highlight the importance of distinguishing short- and long-run economic growth in macro-asset pricing models.

Asset prices can be stale. We define price staleness as a lack of price adjustments yielding zero returns (i.e., zeros). The term idleness (respectively, near idleness ) is, instead, used to define staleness when trading activity is absent (respectively, close to absent). Using statistical and pricing metrics, we show that zeros are a genuine economic phenomenon linked to the dynamics of trading volume and, therefore, liquidity. Zeros are, in general, not the result of institutional features, like price discreteness. In essence, spells of idleness or near idleness are stylized facts suggestive of a key, omitted market friction in the modeling of asset prices. We illustrate how accounting for this friction may generate sizable risk compensations in short-dated option returns. This paper was accepted by Kay Giesecke, finance.

We present insights into novel and complex issues regarding cryptocurrency activities, the related investor protection, and blockchain market risks. Crypto digital assets embody global economic ambition with their significant growth and creativity levels. This study employs a novel research approach using multinominal logit (mlogit) classifier modeling techniques to present unique findings regarding crypto-assets. The machine learning model confirmed better accuracy compared to previous research studies. These findings could contribute to a better understanding of the impact of business consumers on cryptocurrencies and blockchain used by business experts and policymakers worldwide. The research results should help future studies develop more machine learning models to ensure more accurate findings and discussions. The mlogit method research presented here confirms that business artificial intelligence methods and human domain knowledge interpretation can help current business leaders to better understand essential business decisions and their significant role in modern business behavioral prescriptive analytics. We derive important perspectives about cryptocurrency and blockchain strategy improvements, which may produce positive policy changes by enhancing the quality of investor protection in blockchain worldwide.

This study addresses how and why the stock return volatility of REITs changes over time and identifies which mechanisms influence it at a firm level. Using U.S. equity REIT data from 1997 to 2018, we provide evidence that systematic risk and the underlying market for a REIT’s properties affect their stock return volatility. The results suggest that both equity and property markets can contribute to an increase in REITs’ stock return volatility. Portfolio diversification can reduce their sensitivity to property market risk. REITs with more asymmetric information and financial constraints are more vulnerable to property market risk.

Prior research suggests that Corporate Environmental Performance (CEP) enables businesses to build strong corporate image and reputation, thus leading to improved firm financial performance. However, studies relating to the relationship between CEP and firm risk are scarce. This research intends to bridge the gap in the literature by examining whether CEP helps firms to reduce their financial risk. Results of the Ordinary Least Squares regression with fixed effects provide strong evidence that environmental performance is negatively associated with firm volatility and firm downside risk. The results are robust after controlling for moderating effects such as financial, institutional and environmental management.

Rather than treat investments as statistical objects to be optimally combined into portfolios, investors are increasingly interested in the environmental, social, and corporate governance (ESG) dimensions of their investments. Analysts traditionally evaluated these dimensions in qualitative ways, but many data providers are attempting to score these dimensions, effectively quantifying what was qualitative. For developed market equities, on the basis of one popular data provider’s ESG assessment, we evaluate the evidence on whether portfolios of highly rated ESG stocks are materially different from their complements (non-ESG stocks) in their investment opportunity sets. It is obvious that ESG stocks differ from non-ESG stocks in their ESG dimensions, but we show that ESG stocks returns are also different. Although the total return-to-total risk of ESG stocks may be lower than that for non-ESG stocks, after factor-adjusting the returns and risks, portfolios of ESG stocks with positive alpha have return-to-risk features comparable to those of portfolios of non-ESG stocks with positive alpha. For portfolios without statistically significant alpha, the portfolios of ESG stocks have lower residual volatility than portfolios of non-ESG stocks. It should be possible, by factor-neutralizing portfolios, to build better beta with comparable alpha portfolios by using ESG factors. TOPICS: ESG investing, equity portfolio management, portfolio management/multi-asset allocation

We optimize a life insurance company's asset allocation in the context of classical portfolio theory when the firm needs to adhere to the market risk capital requirements of Solvency II. The discussion starts with a brief review of the standard formula and the introduction of a parsimonious partial internal model. Subsequently, we estimate empirical risk–return profiles for the main asset classes held by European insurers and run a quadratic optimization program to derive nondominated frontiers with budget, short-sale, and investment constraints. We then compute the capital charges under both solvency models and identify those efficient portfolio compositions that are permitted for an exogenously given amount of equity. Finally, we consider a systematically selected set of inefficient portfolios and check their admissibility, too. Our results show that the standard formula suffers from severe shortcomings that interfere with economically sensible asset management decisions. Therefore, the introduction of Solvency II in its current form is likely to have an adverse impact on certain parts of the European insurance sector.

Having created a monthly dataset of US security prices between 1801 and 1926, we conduct out-of-sample tests of price-return momentum strategies that have been implemented in the post-1925 datasets. The additional time-series data strengthen the evidence that price momentum is dynamically exposed to market risk, conditional on the sign and duration of the trailing market state. On average, in the beginning of positive market states, momentum's equity beta is opposite to the new market direction, which generates a negative contribution to momentum profits around market turning points. A dynamically hedged momentum strategy significantly outperforms the unhedged strategy.

Global economic downturns and multiple extreme events threaten Vietnam's economy, leading to a surge in stock market risk and significant spillovers. This study investigates market risk spillovers and explores the asymmetric effects of macroeconomic indicators on market risk across 24 sectors in Vietnam from 2012 to 2022. We use the value-at-risk (VaR) technique and a vector autoregression (VAR) model to estimate market risks and their spillovers across Vietnamese sectors. We then examine the asymmetric effects of macroeconomic indicators on market risk using a panel nonlinear autoregressive distribution lag (NARDL) model. Our results confirm that Vietnam’s market risk increases rapidly in response to extreme events. Additionally, market risks exhibit substantial inter-connectedness across the Vietnamese sectors. The  Building Materials ,  Technology , and  Securities  sectors are primary risk transmitters, whereas the Minerals ,  Development Investment , and  Education  sectors are major risk absorbers. Our results also confirm that market risk responds asymmetrically to changes in interest rates, exchange rates (USD/VND), trade openness, financial development, and economic growth in the short and long run. Minerals , Oil & Gas , and Rubber are the sectors that are most affected by macroeconomic indicators in the long run. Based on these important findings, implications focused on limiting market risks and their spillovers, along with sustainable investing, have emerged.

We model the influence of sharing large exogeneous losses to the reinsurance market by a bipartite graph. Using Pareto-tailed claims and multivariate regular variation we obtain asymptotic results for the value-at-risk and the conditional tail expectation. We show that the dependence on the network structure plays a fundamental role in their asymptotic behaviour. As is well known in a nonnetwork setting, if the Pareto exponent is larger than 1, then for the individual agent (reinsurance company) diversification is beneficial, whereas when it is less than 1, concentration on a few objects is the better strategy. An additional aspect of this paper is the amount of uninsured losses that are covered by society. In our setting of networks of agents, diversification is never detrimental to the amount of uninsured losses. If the Pareto-tailed claims have finite mean, diversification is never detrimental, to society or individual agents. By contrast, if the Pareto-tailed claims have infinite mean, a conflicting situation may arise between the incentives of individual agents and the interest of some regulator to keep the risk for society small. We explain the influence of the network structure on diversification effects in different network scenarios.