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Inspired by experimental evidence, we amend the recursive utility model to let risk aversion decrease with the temporal horizon. Our pseudo-recursive preferences remain tractable and retain appealing features of the long-run risk framework, notably its success at explaining asset pricing moments. In addition, our model addresses two challenges to the standard model. Calibrating the agents' preferences to explain the equity premium no longer implies an extreme preference for early resolutions of uncertainty. Horizon-dependent risk aversion helps resolve key puzzles in finance on the valuation of assets across maturities and captures the term structure of equity risk premiums and its dynamics. [ABSTRACT FROM AUTHOR]

Our paper reexamines whether 29 variables from 26 papers published after Goyal and Welch 2008 , as well as the original 17 variables, were useful in predicting the equity premium in-sample and out-of-sample as of the end of 2021. Our samples include the original periods in which these variables were identified, but end later. More than one-third of these new variables no longer have empirical significance even in-sample. Of those that do, half have poor out-of-sample performance. A small number of variables still perform reasonably well both in-sample and out-of-sample. [ABSTRACT FROM AUTHOR]

We present a theory in which the interaction between limited sharing of idiosyncratic labor income risk and labor adjustment costs (that endogenously arise through search frictions) determines interest rate dynamics. In the general equilibrium, the interaction of these two ingredients relates bond risk premiums, cross-sectional skewness of income growth, and labor market tightness. Our model rationalizes an upward-sloping average yield curve and predicts a negative relation between labor market tightness and bond risk premiums. We provide evidence for our theory's mechanism and predictions. [ABSTRACT FROM AUTHOR]

We introduce a new approach to estimating long-term aggregate discount rates using the cross section of earnings and book values to explain current stock prices and extract expected market returns. The proposed discount rate measure is countercyclical. Shocks to it account for nearly half of historical market return variation; in contrast, shocks to other discount rate measures account for no more than 2%. It dominates other measures in explaining time-series variation in returns on duration-sorted portfolios and delivers out-of-sample predictability that exceeds that afforded by other expected return measures and predictive variables. It also performs well in international equity markets. [ABSTRACT FROM AUTHOR]

We study how derivatives (with nonlinear payoffs) affect the underlying asset's liquidity. In a rational expectations equilibrium, informed investors expect low conditional volatility and sell derivatives to the others. These derivative trades affect different investors' utility differently, possibly amplifying liquidity risk. As investors delta hedge their derivative positions, price impact in the underlying drops, suggesting improved liquidity, because informed trading is diluted. In contrast, effects on price reversal are ambiguous, depending on investors' relative delta hedging sensitivity (i.e., the gamma of the derivatives). The model cautions of potential disconnections between illiquidity measures and liquidity risk premium due to derivatives trading. [ABSTRACT FROM AUTHOR]

The opening of equity markets to foreign investment by developing countries appears to generate an enormously large positive growth effect (see Bekaert, Harvey, and Lundblad (2005), Journal of Financial Economics 77, 3–55) in spite of a relatively small role of such markets for financing investment in most economies. We propose a spillover channel from equity market opening to lower costs of bank loans, which helps to explain this puzzle. From analyzing bank loan data associated with China's introduction of the Qualified Foreign Institutional Investors program, we find significant support for this channel. Furthermore, we show that a reduction in the risk premium in loans is an important mechanism. [ABSTRACT FROM AUTHOR]

We study a large currency cross-section using asset pricing methods that account for omitted-variable and measurement-error biases. First, we show that the pricing kernel includes at least three latent factors that resemble (but are not identical to) a strong U.S. "dollar" factor and two weak high Sharpe ratio "carry" and "momentum" slope factors. Evidence for an additional "value" factor is weaker. Second, using this pricing kernel, we find that only a small fraction of the over 100 nontradable candidate factors considered have a statistically significant risk premium, mostly relating to volatility, uncertainty, and liquidity conditions, rather than macro variables. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Consistent with the empirical properties of the consumption data, I develop a model in which consumption and dividend growth follow regime-switching dynamics. I show that regime-shift risk is priced in the model. Regime-shift risk exhibits dominant influence on asset prices: It generates a high equity premium and also induces time-varying risk premiums. The model explains major business cycle-dependent asset market phenomena and, in particular, the stronger predictability of stock returns during recessions. [ABSTRACT FROM AUTHOR]

Under its climate regulation, the EU is expected to become the first continent with a net-zero emissions balance. We study the pricing of climate risks, physical and transition, within European markets. Using text-analysis, we construct two novel (daily) physical and transition risk indicators for the period 2005–2021 and two global climate risk vocabularies. Applying our climate risk indices to an asset pricing test framework, we document the emergence of economically significant transition and physical risk premia post-2015. From a firm-level analysis, using firms' GHG emissions, GHG emissions intensity, environmental, and ESG scores, we find that rises in transition (physical) risk are typically associated with an increase (decrease) in the return of green (brown) stocks. Firm-level information is used by investors to proxy firms' climate-risks exposure, especially for transition risk since 2015, whereas the sectoral classification appears to proxy firms' exposures to physical risk. From a country-level analysis emerges an intensified connection between European stock markets and climate risks post-2015, yet with some heterogeneity. Our results have important economic implications and show that investors demand compensation for their exposure to both climate risk types. Our novel climate risk vocabularies and indicators find several applications in identifying, measuring, and studying climate risks. [ABSTRACT FROM AUTHOR]

We explore the impact of management team ability on firm‐level accounting comparability. Through using a sample of Chinese listed firms from 2009 to 2022, the paper documents that accounting comparability has an inverse U‐shaped association with management team ability where a certain level of managerial ability leads to the highest levels of accounting comparability. Further analysis shows that high‐ability managers have different incentives on accounting comparability when compared to low‐ability managers, especially when they suffer financing constraints, possess more proprietary information, experience weak market environment, bear excessive price risk and risk premium and work in the politically connected firms. [ABSTRACT FROM AUTHOR]

We investigate the impact of macroeconomic variables on bond risk premia prediction via machine learning techniques. On the basis of Chinese treasury bonds from March 2006 to December 2022, we show that adding macroeconomic factors improves bond return forecasts and generates higher economic benefits to investors. This is achieved when the nonlinear relationship between macroeconomic variables and bond returns is modelled via machine learning methods. Furthermore, the importance of macroeconomic determinants changes along the yield curve. Our study sheds new light on the information contained in macroeconomic variables for treasury bond valuation and highlights the importance of utilizing appropriate machine learning methods. [ABSTRACT FROM AUTHOR]

We analyze the role of international reserves and sovereign debt in the determination and international transmission of interest rates. We develop a model that describes the money market equilibrium with capital account openness. It predicts that higher levels of international reserves lead to lower interest rates whereas the effect of higher government debt is ambiguous. We then test empirically these predictions on a panel of 128 countries over 1985–2019. We find that: (i) the transmission of U.S. interest rates to domestic interest rates is stronger in emerging market economies (EMEs) than in advanced economies (AEs) for short‐term interest rates (STIR), and vice versa for long‐term interest rates (LTIR); (ii) international reserves help counteract the transmission of the U.S. policy rate on STIR in EMEs; (iii) a higher sovereign debt level is associated with lower interest rates in EMEs (liquidity effect), but higher interest rates in developing economies (risk premium effect). [ABSTRACT FROM AUTHOR]

Errors-in-variables (EIV) biases plague asset pricing tests. We offer a new perspective on addressing the EIV issue: instead of viewing EIV biases as estimation errors that potentially contaminate next stage risk premium estimates, we consider them to be return innovations that follow a particular correlation structure. We factor this structure into our test design, yielding a new regression model that generates the most accurate risk premium estimates. We demonstrate the theoretical appeal as well as the empirical relevance of our new estimator. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The supplemental appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4966. [ABSTRACT FROM AUTHOR]

This study examines whether the disposition effect can explain time‐varying risk preference and predict the equity risk premium. To do so, we propose an augmented general autoregressive conditional heteroskedasticity (GARCH)‐in‐Mean model unraveling the complex relationship between unrealized gains/losses, realized returns, and the equity risk premium. In our model, the risk aversion coefficient varies with the market state of unrealized gains/losses. Using data from the US stock markets, we show strong evidence that the disposition effect drives time‐varying risk preference: The risk aversion coefficient is significantly positive during periods of unrealized gains, but insignificant during periods of unrealized losses. These findings reconcile the conflicting results of the risk‐return trade‐off in existing literature. Moreover, our model shows significant predictability of the equity risk premium, both in‐sample and out‐of‐sample. Incorporating our model's predictions can yield substantial utility gains for a mean‐variance investor. Our results indicate that the disposition effect leads to time‐varying risk preference and thus induces equity risk premium predictability. [ABSTRACT FROM AUTHOR]

In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio. [ABSTRACT FROM AUTHOR]

Do government policies during the COVID-19 pandemic affect investors' risk aversion, as proxied by the variance premium? To answer this question, this study examines data regarding government responses from thirteen countries. The empirical analysis indicates that government interventions were not able to substantially reduce variance risk premium in international equity markets. The results also show that economic support policies, containment, and closure regulations, and health system interventions all played a significant role in shaping equity variance risk price. [ABSTRACT FROM AUTHOR]

This article estimates the weighted average cost of capital (WACC) for the energy sector in Ecuador, a country with a dollarized economy and illiquid stock markets. Thus, reference companies in the region were taken, and at the same time combined with characteristics of national companies, establishing a useful methodology, which makes sense with the acceptable discount rates in the Ecuadorian economy. For the above, four estimation alternatives were used. In method one, the traditional WACC formula was applied using interest rates and risk premiums from the U.S. market, which resulted in an overestimation due to the double penalty of the country risk and the U.S. market premium. Method two adjusted the market risk premium to consider only the Ecuador-specific risk premium, thus avoiding the double penalty. In method three, the credit default swap (CDS) was used to calculate the country risk premium, and the CDS was excluded from the nominal interest rate, avoiding redundancies. Finally, method four combined the U.S. interest rate with the CDS directly to calculate the market risk premium, more accurately reflecting local economic conditions in a dollarized economy. The WACC results range from 12.63% to 29.70%. In addition, a dummy variable was controlled for during the pandemic period. This article highlights the need for methodologies adapted to emerging markets, since traditional approaches would overestimate the WACC. [ABSTRACT FROM AUTHOR]

This study examines the co-movement between geopolitical risk (GPR), energy price, and bitcoin (BTC) in BRICS countries, namely Brazil, Russia, India, China, and South Africa. Previous studies have focused on the impact of GPR on the volatility and risk premium of BTC investment. However, very limited studies have focused on integrating BTC as an extension of the mix of GPR on the co-movement with energy price. The analysis is based on monthly data of GPR index for BRICS countries, brent oil futures, natural gas futures and BTCs covering the period between March 2012 and Jun 2021. We employ the Bayesian graphical structural vector autoregressive model and time-varying parameter vector autoregressions-based dynamic connectedness to investigate the network-dependence structure. This research project provides useful empirical evidence for assessing the impact of both BTC and GPR on energy prices. Nonetheless, it will also be informative about the likelihood of co-movements occurring at different stages. [ABSTRACT FROM AUTHOR]

Financial economists have long studied factors related to risk premiums, pricing biases, and diversification impediments. This study examines the relationship between a firm's commitment to environmental, social, and governance principles (ESGs) and asset market returns. We incorporate an algorithmic protocol to identify three nonobservable but pervasive E, S, and G time-series factors to meet the study's objectives. The novel factors were tested for information content by constructing a six-factor Fama and French model following the imposition of the isolation and disentanglement algorithm. Realizing that nonlinear relationships characterize models incorporating both observable and nonobservable factors, the Fama and French model statement was estimated using an enhanced shallow-learning neural network. Finally, as a post hoc measure, we integrated explainable AI (XAI) to simplify the machine learning outputs. Our study extends the literature on the disentanglement of investment factors across two dimensions. We first identify new time-series-based E, S, and G factors. Second, we demonstrate how machine learning can be used to model asset returns, considering the complex interconnectedness of sustainability factors. Our approach is further supported by comparing neural-network-estimated E, S, and G weights with London Stock Exchange ESG ratings. [ABSTRACT FROM AUTHOR]

Forecasts of professional forecasters are anomalous: they are biased, and forecast errors are autocorrelated and predictable by forecast revisions. We propose that these anomalies arise because professional forecasters do not know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the world can generate all the prominent aggregate anomalies emphasized in the literature. We show this for professional forecasts of nominal interest rates and Congressional Budget Office forecasts of gross domestic product growth. Our learning model for interest rates can explain observed deviations from the expectations hypothesis of the term structure without relying on time variation in risk premia. [ABSTRACT FROM AUTHOR]

We investigate the impact of audit client political contributions on various audit attributes. We find that, despite having poorer accruals quality, clients with higher political contributions have fewer restatements, receive no more going concern qualified opinions and receive fewer reported material weaknesses. Additionally, we find that auditors earn higher fees from and have longer tenure with their politically connected clients. Our evidence is indirect, but the totality of our results is most consistent with an economic bond between auditors and their politically connected clients, whereby clients are allowed more accounting discretion and receive fewer adverse audit outcomes, and the auditor charges a persistent risk premium. We conclude that client political connections likely influence audit outcomes because of either reduced scrutiny from regulators or direct influence on auditors. We extend a growing body of research on the consequences of corporate political connections. [ABSTRACT FROM AUTHOR]

This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of Merton's conditional Intertemporal Capital Asset Pricing Model (ICAPM) (1973), which predicts an average maximum of 1.83 percent of annual housing risk premium in REIT equity return. Moreover, the housing risk index explains a significant portion of the cross-sectional variation of sectoral REIT returns. In cross-section, the positive risk-return relationship remains significant after controlling for VIX, Fama–French four factors, and a broad set of macroeconomic and financial variables. I also find that the proposed housing beta accurately forecasts U.S. macroeconomic and financial conditions. [ABSTRACT FROM AUTHOR]

We develop a methodology to decompose the conditional market risk premium and risk premia on higher-order moments of excess market returns into risk premia related to contingent claims on down, up, and moderate market returns. The decomposition exploits information about the risk-neutral market return distribution embedded in option prices, but does not depend on assumptions about the functional form of investor preferences or about the market return distribution. The total market risk premium is highly time-varying, as are the contributions from downside, upside, and central risk. Time-series variation in risk premia associated with each region is primarily driven by variation in risk prices associated with the probability of entering each region at short horizons, but it is primarily driven by variation in risk quantities at longer horizons. Analogous decompositions implied by prominent representative agent models generally fail to match the dynamic risk premium behavior implied by the data. Our results provide a set of new empirical facts regarding the drivers of conditional risk premia and identify new challenges for representative agent models. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The internet appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01663. [ABSTRACT FROM AUTHOR]

We introduce a new methodology to conduct simultaneous inference of the non‐parametric component in partially linear time series regression models where the non‐parametric part is a multi‐variate unknown function. In particular, we construct a simultaneous confidence region (SCR) for the multi‐variate function by extending the high‐dimensional Gaussian approximation to dependent processes with continuous index sets. Our results allow for a more general dependence structure compared to previous works and are widely applicable to a variety of linear and non‐linear autoregressive processes. We demonstrate the validity of our proposed methodology by examining the finite‐sample performance in the simulation study. Finally, an application in time series, the forward premium regression, is presented, where we construct the SCR for the foreign exchange risk premium from the exchange rate and macroeconomic data. [ABSTRACT FROM AUTHOR]

South African subsistence livestock farmers rely heavily on traditional mitigation and coping mechanisms to deal with the effects of drought. However, these methods have proven ineffective in managing the full impact of drought. Consequently, policymakers are increasingly interested in promoting Index-based Pasture Insurance (IBPI). This paper aims to assess subsistence livestock farmers' preferences for IBPI. A simple random sampling method was used to select 110 subsistence livestock farmers for data collection. A discrete choice experiment—Conditional Logit and Latent Class models and incentivized lottery games were used to elicit preferences for insurance contracts and loss aversion. The findings indicate that subsistence livestock farmers have a favourable attitude toward IBPI contracts that protect against drought-related pasture degradation. The Conditional Logit model shows that farmers prefer transparent contracts that reimburse with feed and vouchers rather than cash. However, they derive negative marginal utility from basis risk and premium. However, the Latent Class model reveals heterogeneous preferences for IBPI among farmers. Farmers are also loss-averse, but loss aversion did not influence their preference for IBPI. Therefore, the primary recommendation for insurance providers is to consider customizing IBPI attributes to increase adoption among subsistence farmers. [ABSTRACT FROM AUTHOR]

The Journal of Portfolio Management has published a special issue that includes in-depth interviews with four chief investment officers (CIOs) and five theme-oriented articles on critical investment topics. The interviews provide insights into the decision-making processes and strategies of influential asset management companies. The CIOs emphasize the importance of long-term strategies, technology, risk management, client expectations, and leadership styles. The theme-oriented articles cover topics such as fixed-income investing, portfolio diversification in the post-COVID era, leadership in investment management, and the complexities of integrating environmental, social, and governance (ESG) factors into investment portfolios. The articles offer valuable insights into the evolving investment landscape and provide practical knowledge for market professionals. [Extracted from the article]

In the financial system, the customers' willingness to share their data is pivotal, because otherwise, banks and insurance companies are powerless to build on customer data. The key step now is to understand whether there is such willingness and what form it takes. In this study, we investigate how willing customers are to share various kinds of data (on physical health, home, driving style, travel, family, social networks) with their insurance company, in return for different rewards (customised products and services, reduced insurance claims risk and insurance premiums adjusted to personal habits and behaviour). Applying the privacy calculus framework to 1501 responses in a web-based survey, we found that rewards, especially when financial, such as insurance premium benefits, play a pivotal role in driving customer decisions about sharing data. Furthermore, customers associate the data they are asked to share with different levels of privacy, influencing their willingness to share. We also found that, when customers are asked to share various kinds of data in return for different rewards, their own personal innovativeness comes into play. Our findings suggest that, in the data-driven insurance business, different rewards offered in return for specific types of data could help companies minimise the "data acquisition cost" and maximise the data collected. In the era of open data, insurers can explore the many opportunities for segmentation, but new kinds of financial exclusion could emerge, resulting in potential biases and thus misinterpretations should analytics and artificial intelligence models be built upon these premises. [ABSTRACT FROM AUTHOR]

How are contracts structured in the presence of relationship-specific investments when legal enforcement is weak? Using a new audit methodology, we show that simple financial contracts in combination with social norms and reputation concerns can sustain relationship-specific transactions. Wholesalers in the market for pens in India use upfront payments rather than increased risk premiums to mitigate risks arising from relationship-specific investments. Upfront payments for printed pens cover only 40 percent of the production costs, highlighting the importance of upfront payments as a screening device. Ex-post, renegotiation is more likely for printed pens, but in a substantial fraction of cases, renegotiation fails. [ABSTRACT FROM AUTHOR]

Small stocks' time‐varying spreads predict future return gap between small and large stocks. To optimally exploit such predictability, the investor captures current risk premium by purchasing at large spreads with substantially reduced turnover; uses an aim‐in‐front‐of‐the‐target approach to trade‐off between future risk premium and current transaction costs; and meets hedging demand at low costs. Strong interaction between transaction costs and return predictability leads to large losses from myopic trading. Greater variability of the spread is advantageous for investors who trade optimally but detrimental for investors who trade myopically. The spread‐based return predictability significantly increases the investment value of small stocks. [ABSTRACT FROM AUTHOR]

This paper examines the possible nonlinear relationship between public debt and income inequality. The literature extensively examines the non-linear growth effects of public debt, whereas its inequality effects are not investigated sufficiently. Public debt can be used to finance distributive policies, thereby improving income equality. However, high public debt levels can also harm economic growth and decrease the fiscal space due to sustainability concerns, high risk premia, and large debt service burden. Hence, it is possible that public debt can have non-linear effects on inequality. The empirical analysis documents that the relationship between public debt and inequality is positive with a declining marginal effect in the case of advanced countries. However, developing countries display a U-shape relationship, implying that public debt is initially associated with declines in inequality, while higher levels of debt are associated with higher levels of inequality. The analysis also shows that the determinants of inequality can differ greatly between advanced and developing countries. [ABSTRACT FROM AUTHOR]

This study examines how inline XBRL (iXBRL) mandatory adoption impacts audit fees. Although iXBRL can make financial reporting information accessible to a wide range of external stakeholders, the presentation format change should have no impact on audit pricing. However, audit firms note that presenting nonaudited XBRL metadata on top of audited financial statements could lead to an expectation gap where external stakeholders believe that the inline tags have been audited despite no procedures being required. We find that iXBRL adoption is associated with higher audit fees. Upon further examination, we find that the increase in audit fees reflects more of a risk premium being placed on iXBRL clients rather than increased audit effort, as audit quality does not necessarily improve following adoption. We provide timely information on how auditors respond to iXBRL adoption. JEL Classifications: M41; M42; O33. [ABSTRACT FROM AUTHOR]

This paper exploits the concept of expectation dependence to propose an alternative representation of the consumption-based capital asset pricing model (C-CAPM). While the first-degree expectation dependence (FED) drives the C-CAPM's riskiness for a risk-averse investor, the second-degree expectation dependence (SED) is required to account for the downside risk faced by a prudent investor. Theoretical and empirical assessments reveal that the expectation dependence-based C-CAPM can realistically match equity and variance risk premia. The consumption SED risk emerges as a fundamental source of uncertainty driving asset prices. [ABSTRACT FROM AUTHOR]

We examine the commonalities in international equity risk premiums by linking empirical evidence for the ability of U.S. downside and upside variance risk premiums (DVP and UVP, respectively) to predict international stock returns with implications from an empirical model featuring asymmetric economic uncertainty and risk aversion. We find that DVP and UVP predict international stock returns through U.S. bad and good macroeconomic uncertainties, respectively. Sixty percent to 80% of the dynamics of the global equity risk premium for horizons under seven months are driven by economic uncertainty, whereas risk aversion appears more relevant for longer horizons. The predictability patterns of DVP and UVP vary across countries depending on those countries' financial and economic exposure to global shocks. In those with higher economic exposure, investors demand higher compensation for bad macroeconomic uncertainty but lower compensation for good macroeconomic uncertainty, whereas the compensation for bad macroeconomic uncertainty is lower for countries with high financial exposure. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The data and online appendices are available at https://doi.org/10.1287/mnsc.2023.4958. [ABSTRACT FROM AUTHOR]

We empirically study the relationship between currency excess returns and current account uncertainty, measured as forecast dispersion. We find that investment currencies deliver low returns, whereas funding currencies offer a hedge when current account uncertainty is unexpectedly high. Moreover, an increase in current account uncertainty is associated with higher expected future excess returns on investment currencies. This mechanism is consistent with the recent advances in exchange rate theory based on capital flows in imperfect financial markets. This paper was accepted by Gustavo Manso, finance. Funding: A. Krecetovs thanks the Brevan Howard Centre at Imperial College London for financial support. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4949. [ABSTRACT FROM AUTHOR]

This paper documents that the ratio of residential-to-nonresidential investment is a strong in-sample and out-of-sample predictor for the dollar up to 12 quarters. The predictability is robust to a battery of additional checks and holds for other G10 currencies. We explain the predictability in an analytical model with time-varying housing preference, productivity, and volatility. In the model, the U.S. housing investment share is higher during periods with higher growth and lower uncertainty, corresponding to lower future nontradable prices, dollar index, and excess returns. We find strong empirical support for the channel. Alternative explanations, including the business and financial cycle, find less empirical support. This paper was accepted by David Sraer, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4932. [ABSTRACT FROM AUTHOR]

There are some connections between aging notions, stochastic orders, and expected utilities. It is known that the DRHR (decreasing reversed hazard rate) aging notion can be characterized via the comparative statics result of risk aversion, and that the location-independent riskier order preserves monotonicity between risk premium and the Arrow–Pratt measure of risk aversion, and that the dispersive order preserves this monotonicity for the larger class of increasing utilities. Here, the aging notions ILR (increasing likelihood ratio), IFR (increasing failure rate), IGLR (increasing generalized likelihood ratio), and IGFR (increasing generalized failure rate) are characterized in terms of expected utilities. Based on these observations, we recover the closure properties of ILR, IFR, and DRHR under convolution, and of IGLR and IGFR under product, and investigate the closure properties of the dispersive order, location-independent riskier order, excess wealth order, the total time on test transform order under convolution, and the star order under product. We have some new findings. [ABSTRACT FROM AUTHOR]

In this article, a new method is introduced under the Bayesian framework to derive the credibility estimator of risk premiums based on the Esscher premium principle. This new estimator offers desirable statistical properties, making it more useful and practical compared to existing estimators. Additionally, Bayesian models for policy portfolios are established, and empirical Bayes methods are employed to estimate the structural parameters. The empirical Bayesian estimation of risk premiums is also discussed in detail. The convergence rate and goodness of the proposed estimators are verified through simulations. The results demonstrate the effectiveness and accuracy of the new estimator and its superior performance compared to other existing methods. Finally, an empirical analysis is conducted using real insurance data, which further confirms the applicability and reliability of the proposed credibility estimator and its superiority in practical insurance applications. [ABSTRACT FROM AUTHOR]

We synthetically create option contracts on a corporate bond index using CDX swaptions, overcoming the limitations that stem from the lack of traded corporate bond options. Our approach allows us to estimate forward-looking moments concerning the corporate bond market in a model-free manner. By constructing an aggregate volatility measure and the associated variance risk premium, we examine the role of volatility risk in the corporate bond market. We highlight that the ex ante conditional second and higher moments we estimate from synthetic corporate bond options carry important implications for credit risk models, providing an extra basis for testing their validity. [ABSTRACT FROM AUTHOR]

We estimate a model-free term structure of the ex ante dividend risk premium by combining two data sets with different information about future dividends. We aggregate survey forecasts about future dividends for single companies over multiple horizons to construct a term structure of expected S&P 500 dividend growth rates. We use European call and put option prices on the S&P 500 to estimate the term structures of options-implied dividend growth rates and risk-free rates. Applying the method to 2004–2021 data offers a new, ex ante perspective on the conditional time variation of the term structure of the dividend risk premium. [ABSTRACT FROM AUTHOR]

This paper employs insights from earth science on the financial risk of project developments to present an economic theory of critical minerals. Our theory posits that back-ended critical mineral projects that have unaddressed technical and non-technical barriers, such as those involving lithium and cobalt, exhibit an additional risk for investors which we term the "back-ended risk premium". We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector. We posit that the back-ended risk premium may also reduce the gains in productivity expected from artificial intelligence (AI) technologies in the mining sector. Progress in AI may, however, lessen the back-ended risk premium itself by shortening the duration of mining projects and the required rate of investment by reducing the associated risk. We conclude that the best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research. Vespignani and Smyth present an economic theory of risk in critical minerals they term the "back-ended risk premium." They apply AI approaches to reduce this risk premium and lower costs in energy transition. [ABSTRACT FROM AUTHOR]

Insurance is an important element of flood risk management, providing financial compensation after disastrous losses. In a competitive market, insurers need to base their premiums on the most accurate risk estimation. To this end, (recent) historic loss data are used. However, climate variability can substantially affect flood risk, and anticipating such variations could provide a competitive gain. For instance, for a year with higher flood probabilities, the insurer might raise premiums to hedge against the increased risk or communicate the increased risk to policyholders, encouraging risk-reduction measures. In this explorative study, we investigate how seasonal flood forecasts could be used to adapt flood insurance premiums on an annual basis. In an application for Germany, we apply a forecasting method that predicts winter flood probability distributions conditioned on the catchment wetness in the season ahead. The deviation from the long term is used to calculate deviations in expected annual damage, which serve as input into an insurance model to compute deviations in household insurance premiums for the upcoming year. Our study suggests that the temporal variations in flood probabilities are substantial, leading to significant variations in flood risk and premiums. As our models are based on a range of assumptions and as the skill of seasonal flood forecasts is still limited, particularly in central Europe, our study is seen as the first demonstration of how seasonal forecasting could be combined with risk and insurance models to inform the (re-)insurance sector about upcoming changes in risk. [ABSTRACT FROM AUTHOR]

Delta‐hedged option selling strategies typically yield positive returns, owing to the volatility risk premium embedded in the option price. Recent research based on S&P 500 options has found a day–night asymmetry in option returns. We find a similar disparity in the returns for short Nifty option strategies. Positive and significant overnight option returns are accompanied by negative intraday returns. The day–night asymmetry is robust across option categories and subsamples but weaker on days with significant jumps in the underlying. We confirm that the variance risk premium earned by option sellers is mainly a reward for overnight risk. [ABSTRACT FROM AUTHOR]

Purpose: Given the difficulties in finding significant exchange rate exposure in the extant literature, this paper attempts to resolve the so-called "exposure puzzle" by investigating whether currency movements have any significant impact on international industry returns. Design/methodology/approach: This paper utilizes the multivariate Generalized AutoRegressive Conditional Heteroskedasticity (MGARCH) methodology to estimate both symmetric and asymmetric exchange rate exposures for each industry common across 12 countries simultaneously. Findings: The empirical results show that exchange rate exposure is not only statistically significant but also economically important based on the estimation of an asymmetric three-factor exposure model using MGARCH methodology. This is an extremely important finding as it suggests that the "exposure puzzle" may not be a puzzle at all once a better methodology is utilized in the estimation. Research limitations/implications: Because this study tries to resolve the exchange rate exposure puzzle by focusing on whether exchange rate movements affect ex-post returns as opposed to ex ante expected returns and given the significant exposures with respect to different risk factors found in the study, it is interesting to see if any of these risk factors commands a risk premium. In other words, a natural extension of this study is to test whether any of these risk factors is priced in international industry returns. Practical implications: The findings of the study have interesting implications for international investors who would like to diversify their portfolios across different industries and are concerned about whether the unexpected movements in the bilateral exchange rates will affect their portfolio returns in addition to its interest rate and world market risk exposures. Originality/value: The study utilizes the MGARCH methodology, which has not been fully exploited in the exchange rate exposure literature. [ABSTRACT FROM AUTHOR]

We consider the canonical trade model with heterogeneous firms, love for variety and trade costs, and integrate it in the consumption CAPM model. This yields a structural gravity equation that includes an additional factor related to risk premia. Empirical evidence based on firm-level data confirms the importance of cross-sectional heterogeneity in risk and time-varying risk premia to shape bilateral trade flows. The structural gravity model augmented to account for fluctuations in risk premia offers a compelling explanation for trade collapses during abrupt economic downturns. [ABSTRACT FROM AUTHOR]

We focus on analyzing the risk preferences of suppliers regarding two types of financing: partial credit guarantee (PCG) and trade credit financing (TCF). Using the conditional value-at-risk (CVaR) criterion, we study each supply chain member's equilibrium solution and optimal financing strategy under the assumption of demand distributions with an increasing failure rate. Initially, we present an analytical solution of the risk-averse supplier's wholesale price, and prove that the optimal wholesale price decreases in the risk-averse level and initial capital, and increases in the credit guarantee ratio. Furthermore, we derive optimal financing strategies for both the supplier and the retailer under various circumstances. However, it is important to note that the results reveal a potential trade-off associated with PCG. While risk-averse suppliers may be more inclined to provide PCG compared to risk-neutral suppliers, this financing option can negatively impact the supplier's utility and the expected profits of the retailers. Finally, we illustrate how the optimal financing strategies shift in response to changes in the risk-averse level and credit guarantee ratio, and present the win-win situations for the supplier and the retailer. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Disciplines Economics & Administrative Scienves Studies is the property of International Journal of Disciplines in Economics & Administrative Sciences Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of Disciplines Economics & Administrative Scienves Studies is the property of International Journal of Disciplines in Economics & Administrative Sciences Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Climate change and environmental issues have received increasing attention across the world. China's governmental targets for carbon peak and carbon neutralization show the ambition and efforts necessary in challenging these problems. The transportation industry will be crucial in reducing carbon emissions. Based on the green patent application data in China's new energy vehicle (NEV) industry from 2006 to 2021, this article focuses on risk premium of green technology innovation. In particular, the premium effects of the green technology innovation and the cooperative network are empirically examined. Furthermore, two channels that play a role in generating the premium are investigated, i.e., attracting market attention and reducing financing constraints. The empirical results show that the stock returns are positively correlated to the green technology innovation and the company's central position in the cooperative network, i.e., there exist the premium effects of green technology innovation in China's NEV industry. The positional advantage in the cooperative innovation network can further increase analyst following and reduce financing constraints. The research can provide evidence and policy implications for the government, companies and investors. [ABSTRACT FROM AUTHOR]