We develop a model that generates slowly unfolding disasters not only in the macroeconomy but also in financial markets. In our model, investors cannot exactly distinguish whether the economy is ...experiencing a mild/temporary downturn or is on the verge of a severe/prolonged disaster. Due to imperfect information, disaster periods are not fully identified by investors ex ante. Bayesian learning induces equity prices to gradually react to persistent consumption declines, which plays a critical role in explaining the VIX, variance risk premium, and put-protected portfolio returns. We show that our model can rationalize the market patterns of recent major crises, such as the dot-com bubble burst, Great Recession, and COVID-19 crisis, through investors' belief channel.
The ratio of gold to platinum prices (GP) reveals persistent variation in risk and proxies for an important economic state variable. GP predicts future stock returns in the time series, explains ...stock return variation in the cross-section, and is significantly correlated with option-implied tail risk measures. Contrary to conventional wisdom, gold prices fall in recessions, albeit by less than platinum prices. A model featuring recursive preferences, time-varying tail risk, and preference shocks for gold and platinum can account for asset pricing dynamics of equity, gold, and platinum markets, rationalize the return predictability, and explain why gold prices fall in bad times.
We measure “good” and “bad” variance premia that capture risk compensations for the realized variation in positive and negative market returns, respectively. The two variance premium components ...jointly predict excess returns over the next one and two years with statistically significant positive (negative) coefficients on the good (bad) component. The
R
2
s reach about 10% for aggregate equity and portfolio returns and 20% for corporate bond returns. To explain the new empirical evidence, we develop a model that highlights the differential impact of upside and downside risk on equity and variance risk premia.
The online appendix is available at
https://doi.org/10.1287/mnsc.2017.2890
.
This paper was accepted by Neng Wang, finance.
Accurate estimation of particle size distribution across a large area is crucial for proper soil management and conservation, ensuring compatibility with capabilities and enabling better selection ...and adaptation of precision agricultural techniques. The study investigated the performance of tree-based models, ranging from simpler options like CART to sophisticated ones like XGBoost, in predicting soil texture over a wide geographic region. Models were constructed using remotely sensed plant and soil indexes as covariates. Variable selection employed the Boruta approach. Training and testing data for machine learning models consisted of particle size distribution results from 622 surface soil samples collected in southeastern Turkey. The XGBoost
Clay
model emerged as the most accurate predictor, with an
R
2
value of 0.74. Its superiority was further underlined by a 21.36% relative improvement in XGBoost
Clay
RMSE compared to RF
Clay
and 44.5% compared to CART
Clay
. Similarly, the
R
2
values for XGBoost
Silt
and XGBoost
Sand
models reached 0.71 and 0.75 in predicting sand and silt content, respectively. Among the considered covariates, the normalized ratio vegetation index and slope angle had the highest impact on clay content (21%), followed by topographic position index and simple ratio clay index (20%), while terrain ruggedness index had the least impact (18%). These results highlight the effectiveness of Boruta approach in selecting an adequate number of variables for digital mapping, suggesting its potential as a viable option in this field. Furthermore, the findings of this study suggest that remote sensing data can effectively contribute to digital soil mapping, with tree-based model development leading to improved prediction performance.
Investors’ learning can drastically alter the dynamics of the variance risk premium: it no longer increases as economic conditions deteriorate but exhibits a highly nonlinear pattern, occasionally ...even turning negative. We demonstrate this intuition using a model where investors rationally form their belief about the hidden economic state. When the “bad” state becomes probable, investors start liking high future variance because it overwhelmingly correlates with lower marginal utility. This mechanism rationalizes the puzzling observation that risk-neutral volatility falls short of physical volatility at the peak of a severe crisis. Our results shed light on the interpretation of good economic uncertainty.
What is the driving force behind the cyclical behavior of unemployment and vacancies? What is the relation between firms’ job-creation incentives and stock market valuations? We answer these ...questions in a model with time-varying risk, modeled as a small and variable probability of an economic disaster. A high probability implies greater risk and lower future growth, lowering the incentives of firms to invest in hiring. During periods of high risk, stock market valuations are low and unemployment rises. The model thus explains volatility in equity and labor markets, and the relation between the two.
Insensitive Investors CHARLES, CONSTANTIN; FRYDMAN, CARY; KILIC, METE
The Journal of finance (New York),
August 2024, 2024-08-00, 20240801, Letnik:
79, Številka:
4
Journal Article
Recenzirano
ABSTRACT
We experimentally study the transmission of subjective expectations into actions. Subjects in our experiment report valuations that are far too insensitive to their expectations, relative to ...the prediction from a frictionless model. We propose that the insensitivity is driven by a noisy cognitive process that prevents subjects from precisely computing asset valuations. The empirical link between subjective expectations and actions becomes stronger as subjective expectations approach rational expectations. Our results highlight the importance of incorporating weak transmission into belief‐based asset pricing models. Finally, we discuss how cognitive noise can provide a microfoundation for inelastic demand in the stock market.
Rational planning of soil resources based on their capabilities are needed for the sustainable use of agricultural lands. Land suitability classification is an important evaluation tool for the ...management of soil resources. This study aimed to evaluate the land suitability for wheat (Triticum aestivum) cultivation using an approach that integrates multi-criteria decision making (MCDA) analysis and geographic information systems (GIS). The study area cover 21146 ha land and is located within the land consolidation area in the Çumra Plain, located in Central Anatolia of Turkey, The physical, chemical and fertility properties of the soil samples collected from 342 points in the study area were used as parameters in the wheat suitability assessment. The relative weight values of the soil parameters were determined by the Analytical Hierarchy Process (AHP). Literature and expert opinion were used in the creation of the AHP matrices and the determination of the sub-criteria. The criteria with the highest weight values or which have the highest impact on wheat growth were soil texture (0.30) and pH (0.16), while the lowest weight values were given for micro elements (0.02). Land Suitability Assessment was applied to the maps of soil variables using weighted overlay analysis in the GIS environment by using the relative weights. Thus, the suitability of the study area for wheat cultivation was mapped. The results revealed that 74% of the study area was highly suitable (S1) and 24% was moderately suitable (S2) for wheat cultivation. The coefficient of determination (R2) was 0.81, which indicated a successful prediction of the GIS-MCDA hybrid approach for wheat suitability assessment. Integration of land suitability analyzes specific to plant variety in land consolidation projects can provide a more detailed perspective on the land in the design of planning studies.
Soil salinity is a major land degradation process reducing biological productivity in arid and semi-arid regions. Therefore, its effective monitoring and management is inevitable. Recent developments ...in remote sensing technology have made it possible to accurately identify and effectively monitor soil salinity. Hence, this study determined salinity levels of surface soils in 2650 ha agricultural and natural pastureland located in an arid region of central Anatolia, Turkey. The relationship between electrical conductivity (EC) values of 145 soil samples and the dataset created using Landsat 5 TM satellite image was investigated. Remote sensing dataset for 23 variables, including visible, near infrared (NIR) and short-wave infrared (SWIR) spectral ranges, salinity, and vegetation indices were created. The highest correlation between EC values and remote sensing dataset was obtained in SWIR1 band (r = -0.43). Linear regression analysis was used to reveal the relationship between six bands and indices selected from the variables with the highest correlations. Coefficient of determination (R2 = 0.19) results indicated that models obtained using satellite image did not provide reliable results in determining soil salinity. Microtopography is the major factor affecting spatial distribution of soil salinity and caused heterogeneous distribution of salts on surface soils. Differences in salt content of soils caused heterogeneous distribution of halophytes and led to spectral complexity. The dark colored slickpots in small-scale depressions are common features of sodic soils, which are responsible for spectral complexity. In addition, low spatial resolution of Landsat 5 TM images is another reason decreasing the reliability of models in determining soil salinity.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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