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The pricing kernel puzzle of Jackwerth (2000) concerns the fact that the empirical pricing kernel implied in S&P 500 index options and index returns is not monotonically decreasing in wealth as standard economic theory would suggest. Thus, those options are currently priced in a way such that any risk-averse investor would increase his/her utility by trading in them. We provide a representative agent model where volatility is a function of a second momentum state variable. This model is capable of generating the empirical patterns in the pricing kernel, albeit only for parameter constellations that are not typically observed in the real world.

Following Ang and Piazzesi’s (2003) study, the authors use an affine term structure model to study the relevance of macroeconomic (domestic and foreign) factors for Peru’s sovereign yield curve in the period from November 2005 to December 2015. The paper aims to discuss this issue.

Risk premia are modeled as time-varying and depend on both observable and unobservable factors; and the authors estimate a vector autoregressive model considering no-arbitrage assumptions.

The authors find evidence that macro factors help to improve the fit of the model and explain a substantial amount of variation in bond yields. However, their influence is very sensitive to the specification model. Variance decompositions show that macro factors explain a significant share of the movements at the short and middle segments of the yield curve (up to 50 percent), while unobservable factors are the main drivers for most of the movements at the long end of the yield curve (up to 80 percent). Furthermore, the authors find that international markets are relevant for the determination of the risk premium in the short term. Higher uncertainty in international markets increases bond yields, although this effect vanishes quickly. Finally, the authors find that no-arbitrage restrictions with the incorporation of macro factors improve forecasts.

To the authors’ knowledge this is the first application of this type of models using data from an emerging country such as Peru.

The cross-quantilogram analysis is employed. The latter can assess the temporal association between two stationary time series at different parts of their joint distribution. Data are daily prices and trading volumes from the futures markets of five agricultural commodities, namely, corn, hard red wheat, oats, rice and soybeans.

The objective to the present work is to investigate for directional predictability between returns and volume (and vice versa) in the futures markets of agricultural commodities.

The empirical results reveal evidence, weak as well as strong, that extreme low values of returns are likely to lead high levels of volume. There is also weak evidence that extreme low values of volume are likely to precede high values of returns, except for the futures markets of oats where there is very strong evidence that low values of volume are likely to lead high values of returns. For the commodity of soybeans, there is very strong evidence that extreme high levels of volume are likely to lead high values of returns, but they are very short lived.

Agricultural futures have been recently characterized by increased volatility leading hedgers to be looking for diversification. The present findings suggest that when price crashes occur, investors who suffer losses wish to sell, increasing this way the trading activity. Concurrently, the results reveal that extreme low levels of trading volume might signal a possible price turn around for traders.

This is the first study that employs the quantilogram approach in order to investigate for potential predictability from returns to volume and from volume to returns, in the futures markets of agricultural commodities.

This research aims to model the relationship between the credit risk signals in the credit default swap (CDS) market and agency credit ratings, and determines the factors that help explain the variation in such signals.

A comprehensive analysis of the differences in the relative credit risk assessments of CDS-based risk signals and agency ratings is provided. It is shown that the divergence between credit risk signals in the CDS market and agency ratings is explained by factors which the rating agencies may consider differently than credit market participants.

The results suggest that agency credit ratings of relative riskiness of a reference entity do not always correspond with assessments by CDS spreads, as the price of risk is a function of additional macro and micro factors that can be explained using statistical analysis.

This research is unique in modeling the relationship between the credit risk assessments of the CDS market and the agency ratings, which to the best of the authors' knowledge has not been analyzed before in terms of their agreement and the level of discrepancy between them. This model can be used by investors in debt instruments that are not explicitly CDSs or which have illiquid CDS contracts, to replicate market-based, point-in-time credit risk signals. Based on both market-based and firm-specific factors in this model, the results can be used to augment through-the-cycle credit risk assessments, analyze issues surrounding the pricing of CDSs and examine the policies of credit rating agencies.

Stahmann Farms Enterprises is an Australian company focused on farming, processing, and marketing of pecans and the high-end macadamia nuts. While the company owns pecan farms, it depends solely on farmers for the supply of macadamia, which poses a challenge with numerous competitors trying to attract the farmers. As an agro-processing business, it is dependent on the vagaries of nature and profits fluctuate wildly. Competition exists in the form of large processors as well as new entrants. Given the company's target of becoming one of the top five macadamia processing businesses in the world, volume and profitability need to be driven up. There are a number of paths open before the company and the MD and his team need to evaluate these and draw up a strategy to be presented to the board.

This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.

This paper examines the pricing of interest rates derivatives such as caps and swaptions in the pricing kernel framework. The underlying state variable is extended to the general infinitely divisible Levy process. For computational purposes, a simple pricing kernel as in Flesaker and Hughston (1996) and Jin and Glasserman (2001) is used. The main contribution or purpose of this paper is to find several proper positive martingales, which is key role of practical applications of the pricing kernel approach with interest rates guarantee to be positive. Particularly, this paper first finds and applies a quite general type of a positive martingale process to pricing interest rate derivatives such as swaptions and range notes in the incomplete market setting. Such interest rate derivatives are hard to find analytic solutions. Consequently, this paper shows that such a choice of the positive martingale in the kernel framework is a promising approach to price interest rate derivatives

With the presence of pricing errors, the authors consider statistical inference on the variance risk premium (VRP) and the associated implied variance, constructed from the option prices and the historic returns.

The authors propose a nonparametric kernel smoothing approach that removes the adverse effects of pricing errors and leads to consistent estimation for both the implied variance and the VRP. The asymptotic distributions of the proposed VRP estimator are developed under three asymptotic regimes regarding the relative sample sizes between the option data and historic return data.

This study reveals that existing methods for estimating the implied variance are adversely affected by pricing errors in the option prices, which causes the estimators for VRP statistically inconsistent. By analyzing the S&P 500 option and return data, it demonstrates that, compared with other implied variance and VRP estimators, the proposed implied variance and VRP estimators are more significant variables in explaining variations in the excess S&P 500 returns, and the proposed VRP estimates have the smallest out-of-sample forecasting root mean squared error.

This study contributes to the estimation of the implied variance and the VRP and helps in the predictions of future realized variance and equity premium.

This study is the first to propose consistent estimations for the implied variance and the VRP with the presence of option pricing errors.

The purpose of this paper is to capture the presence and impact of optimism in the Indian equity market.

The data set comprises the daily values of the Nifty 50 index, index options and Treasury-bill index for a period of five years (2006-2011). The focus of this paper is two pronged. It first investigates the presence of optimism (pessimism) using the pricing kernel technique suggested by Barone-Adesi et al. (2012). Second, it tries to analyze the relationship of this bias with stock market indicators like risk premium, market return and volatility using time series regression.

The findings indicate that the Indian equity market has been predominantly pessimistic from the period 2006 to 2011. The interaction of this bias with market indicators also unveils some interesting insights. The study shows that high past volatility can lead to pessimism in the Indian equity market and vice versa. It further explores that when the investors are rational, their risk and return relationship is positive while it tends to be negative when they are irrational. The impact of investors’ irrationalities on asset valuation has also been accounted by Brown and Cliff (2005).

The findings of the paper have significant implications for fund managers and asset management companies. It is recommended that they should try to identify behavioral biases in their clients before designing their portfolios.

This study is one of the very few attempts to capture the presence and impact optimism (pessimism) in the Indian equity market.