Households form beliefs by recalling past experiences, but recall is context dependent. Variation in contexts alone can therefore generate non-fundamental disagreement. A stylized model à la Wachter and Kahana (2024) illustrates this cognitive source of disagreement. When combined with salience-based frictions in memory encoding, context-dependent recall also generates testable predictions for belief skewness. Using weather as a contextual cue that triggers the recall of past shopping experiences, I construct a measure of memory-based household inflation disagreement in the U.S. and show that it explains survey-based disagreement, with stronger effects in unusual contexts, and predicts greater belief skewness. The memory effect is distinct from experience and mood effects and generalizes across countries and outcome domains. Finally, memory-based disagreement weakens monetary policy transmission by attenuating intended consumption responses.

The U.S. Treasury yields reached a 20-year high under acute inflation pressure in the post-pandemic era amid aggravated geopolitical conflicts. To quantify the underlying effects of regional geopolitical risks (GPRs) of key U.S. strategic interests, we employ an extended affine term structure model with unspanned GPRs and conventional macroeconomic drivers. We find that GPR shocks, particularly those manifesting U.S.-China rivalry, contribute more to expectations and variations of inflation and yields than shocks to U.S. macroeconomic variables. The results warn on the adequacy of monetary policy in curbing inflation in a fragmented global order with escalating GPRs.

In the digital era, the information value of online prices, characterized by weak price stickiness and high sensitivity to economic shocks, deserves more attention. This paper integrates the high-frequency online inflation rate into the dynamic Nelson-Siegel (DNS) model to explore its relationship with the term structure of interest rates. The empirical results show that the weekly online inflation can significantly predict the yield curve, particularly the slope factor, while the monthly official inflation is predicted by yield curve factors. The mechanism analyses indicate that, due to low price stickiness, online inflation is more responsive to short-term economic conditions and better reflects money market liquidity, thereby having predictive power for the yield curve. Specifically, online inflation for non-durable goods and on weekdays shows stronger predictive power for the slope factor. The heterogeneity in price stickiness across these categories explains the varying impacts on the yield curve.

Taking the prevalence of the global green development concept and China's green credit development practice as the background, this paper constructs a theoretical model analysis framework with the incentive policy of green credit as the entry point. First, the impact effect of green credit incentive policy is examined using the BVAR model. The results show that the green credit incentive policy suppresses the output level in the short run through the financing constraint channel, but has a positive contribution to output in the long run due to the adjustment of the production structure and the dynamic adjustment of green investment and R&D. Next, the paper constructs a DSGE model embedded with green credit fiscal and tax incentive policies, which explains the impact mechanisms and comparative effects of fiscal and financial policies driving green credit. The model shows that the re-guarantee policy is the most effective and consensual green credit incentive policy. In terms of the policy combination, the combination of the re-guarantee policy and the income tax policy is the current optimal policy pairing, and its policy is able to produce an amplification effect through the balance sheet channels of commercial banks and enterprises at the same time. In addition, a certain intensity of the above policy combination not only can effectively increase the scale of green credit, but also does not produce significant negative shocks to output and inflation. In summary, the findings of this paper provide a useful reference for the formulation and implementation of green credit incentive policies.

As many ESG rating agencies have flourished to meet rising interests in ESG investing, we examine the information provider role of these rating agencies. We hypothesize that new ratings can add information useful to investors about rated firms besides any changes to the average level and dispersion in ratings. We exploited the empirical setting where the introduction of various ESG ratings in China is staggered over time and across firms. We show that an increase in the number of ratings by different agencies for a given firm will induce more mutual funds’ investments towards that firm. This is unexplained by rating inflation or rating shopping channels. We further show that such effect is more pronounced when incumbent and entrant agents provide complementary information. For different types of funds, we find different sensitivities to the arrival of new agents in accordance with their explicit requirements for ESG mandate. And interestingly ESG funds that track ESG indices are not responsive to new ratings as ESG indices are sticky in choosing the reference rating. We also provide evidence that the documented effects are not due to endogenous actions taken by incumbent agencies or the firms. Our paper provides interesting and causal evidence of the incremental information from additional ESG ratings which have important implications for the market competition and regulations of ESG rating agencies.

There is mixed evidence of a positive relationship between the stock market risk and return. We reexamine this critical implication of asset pricing theory using fresh data from China's stock market, which is largely segmented from the rest of the global financial market. Using formal variable selection methods and a comprehensive set of predictor variables, we identify conditional market variance, scaled market prices, and inflation as crucial determinants of equity premiums. The estimated simple risk-return relationship exhibits downward omitted variable bias, which underlines the importance of considering multiple factors to explain the variation in equity premiums. We cannot wholly attribute the three-factor conditional equity premium model to data mining, as Guo, Sanni, and Yu (2022) select the same model for the U.S. stock market. These findings challenge existing asset pricing models and provide valuable guidance for future theoretical research.

The study reveals that the COVID-19 crisis has had a strong but one-off negative impact on the hedge fund industry. It also shows that during the new coronavirus pandemic, the main components of the hedge fund industry achieved only partially their main investment goal, i.e. they as a whole provided a hedge of the investment risk but did not produce higher than the market return in the conditions of a growing capital market. In this situation, due to the relatively stable М&A market, the Event-Driven Risk Arbitrage strategy was undoubtedly most successful, followed by the Emerging Markets, the Global Macro and the Long/Short Equity strategies. The worst performance was reported for the Fixed Income Arbitrage strategy due to the currently overvalued bond markets and to the expectations for higher inflation rates in the countries with developed capital markets.

An optimal tax and borrowing plan determines the marginal cost of servicing government debt, p', and makes the government’s debt risk-free. An option to default restricts debt capacity. Optimal debt-GDP ratio dynamics are driven by 1) a primary deficit, 2) interest payments, 3) GDP growth, and 4) hedging costs. Hedging influences debt capacity and debt transition dynamics. For plausible parameter values, we make comparative dynamic quantitative statements about debt-GDP ratio transition dynamics, debt capacity, and how long it would take our example economy to attain that calibrated equilibrium debt capacity.

This paper creates default-probability-(PD)-implied credit ratings for Chinese firms following the S&P global rating standard. The domestic credit rating agency (DCRA) ratings are higher than the PD-implied ratings by ten notches on average for the identical level of default risk, implying that the domestic ratings are significantly inflated. The PD-implied ratings outperform the DCRA ratings in detecting defaults and complement the latter in predicting yield spreads. The PD-implied ratings draw information from operating efficiency-related variables; in contrast, the DCRA ratings pay attention to scale-based firm characteristics in credit risk assessment.

Based on a DSGE model embedded with a stock market, we inspect interconnection between China's financial markets and macroeconomic cycles. We find consumption, investment and capacity utilization display significant and positive responses to stock market booms triggered by financial and confidence shocks, however, inflation responds insignificantly. We perceive a counteractive and significant reaction of China's monetary policy rule to credit-to-GDP gap at business cycle frequency. We decompose stock price into fundamental value influenced by the financial shock and speculative bubble driven by the confidence shock, and the confidence shock's contribution to stock price fluctuations is estimated to be about 14.8%. Model validation based on the DSGE-VAR framework indicates no serious structural model misspecification.