Springer Science and Business Media LLC

While the link between financial market movement and economic policy uncertainty indices is well-established in literature, uncertainty in the form of ‘foreboding’ emanating from catastrophic events has not been explored in literature. This paper explores “foreboding”, which reflects uncertainty at its extreme, following the Covid-19 pandemic. Using Natural Language Processing on minute-by-minute news data, I construct two Foreboding Indices, representing ‘foreboding’ or ‘fearful apprehension’, for 28,622 Covid-related news for the period July 2020–August 2021. The impact of foreboding on financial market volatility is explored using a logistic regression model. Both the indices show a marked increase in June–July, 2020, in January 2021, April, 2021, and July–August, 2021 and have a positive impact on volatility for hourly S&P 500 Index. Understanding of foreboding sentiment is crucial for central banks looking to monitor financial market volatility. Appropriate signaling in accordance to sentiment can help central banks handle detrimental impacts of market volatility. Moreover, FI can be used for market practitioners to gauge the sentiment and take effective trading decisions.

In this paper, we propose a theory for deriving the optimal portfolio that assures the log-utility investors of maximizing their expected utility. Restricting investors' information at defined levels, we propose the sample path-wise optimal portfolio (SPOP), which is consistent with the back-test framework used in actualinvestment. It is proven that, at any finite terminal time, this SPOP is asymptotically optimal among all the portfolios which are predictable under investors' incompleteinformation. The optimality is guaranteed by the continuous Bayesian updating formula. Finally, we discuss an algorithm for searching the SPOP, based on asset prices at discrete time intervals.

This study investigates the impact of index futures trading on the spot market volatility for Ho Chi Minh Stock Exchange (HOSE). The data used in this study are daily VN30-Index, future trading volume and open interests covering the period from March 18th, 2015 to January 2nd, 2020. In order to capture the asymmetric effect, the EGARCH(1,1) model is employed in this study. It is found that the introduction of index future trading leads to the increase the spot market volatility. In addition, our empirical findings reveal that the impact of recent news on spot market volatility in the post-index future period is greater that than the pre-index future period; and the market volatility in the post-futures period is more persistent than in the pre-futures period. Moreover, the level of asymmetric effect on the market volatility in the post-index future period is significantly lower than that for the pre-index futures period. Finally, the results derived from the Granger causality test confirm that the bi-directional causality relation between the spot market volatility and the future trading activity exists in HOSE.

This study attempts to examine the adaptive market hypothesis and evolving predictability of stock returns using four decades of daily data from the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE) in India. The recent developed automatic portmanteau ratio (AVR) and wild bootstrap automatic variance ratio (WAVR) test are used for analysis. We also estimate both the AVR and WAVR statistics in the rolling window framework to examine evolving predictability. The results revealed that BSE and NSE are informationally inefficient in the weak-form. The results of rolling window analysis suggested that the degree of predictable patterns evolves over the period due to global and regional economic and non-economic events. Further, the study compare which stock market is more efficient and found that NSE is more efficient than BSE. The findings of this study provide essential inputs to investors on trading strategies in dynamic economic situations and policymakers to formulate an appropriate policy that can make the Indian stock markets efficient.

This paper develops a valuation model for fixed-rate mortgages, mortgage pools, and residential mortgage-backed securities (RMBS's) using an intensity-based approach. This model incorporates full prepayment, partial prepayment, and default in valuing a mortgage. Full prepayment is further classified into “refinancing” and “sale of a house” depending on the reason. The time of occurrence of each of these three types of prepayment and default is modeled as the first jump time of a Cox process. Under these conditions, the valuation formula for a mortgage as well as a partial differential equation (PDE) that the mortgage value satisfies is provided. As for implementation of the model, the short-term riskless interest rate and the house price are adopted as state variables. Each intensity process is specified in a manner that allows a jump in intensity depending on the state variables and the borrower's incentive for prepayment or default. Through such specifications, it is shown that our model has characteristics similar to some structural models in previous literature. As for the numerical method for valuation, we propose a simple backward induction technique on a tree instead of the commonly used Monte Carlo method. Additionally, the method for estimating the model is discussed, and the results of numerical simulations are reported.

We analyze a trading dataset from the Korean stock market, a representative and leading emerging equity market, to study the impact of domestic institutional trades on information asymmetry. Using the bid–ask spread as a proxy for the adverse selection cost imposed by information asymmetry, we empirically examine the relationship between domestic institutional trades and their corresponding bid–ask spreads. We find that bid–ask spreads tend to increase when the trading volume of domestic institutional investors is high, suggesting that such investors tend to aggravate information asymmetry as informed traders in the Korean stock market.

This study explores the impact of policy uncertainty, geopolitical risk, and sustainable disclosure (ESG) on corporate performance for the period 2014–21 across 23 countries. Using the System GMM technique, it uncovers a negative link between policy uncertainty, geopolitical risk, and corporate performance. Sustainable disclosure mitigates the influence of economic uncertainty and geopolitical risk on firm performance. The results are robust across the various other econometric methods (i.e. fixed effect, random effect and feasible generalized least squares) and alternative proxy used for sustainability disclosure. These findings have implications for policymakers and managers, highlighting the importance of aligning policies with sustainable disclosure practices. This study contributes to the literature by examining these factors on a cross-country scale, potentially among the first of its kind.