Pricing Kernels with Stochastic Skewness and Volatility Risk

Management Insights

We present a pricing kernel that summarizes well the main features of the dynamics of interest rates and risk in postwar U.S. data and use it to uncover how the pricing kernel has moved with the short rate in this data. Our findings imply that standard monetary models miss an essential link between the central bank instrument and the economic activity that monetary policy is intended to affect, and thus we call for a new approach to monetary policy analysis. We sketch a new approach using an economic model based on our pricing kernel. The model incorporates the key relationships between policy and risk movements in an unconventional way: the central bank's policy changes are viewed as primarily intended to compensate for exogenous business cycle fluctuations in risk that threaten to push inflation off target. This model, while an improvement on standard models, is considered just a starting point for their revision. It leads to critical questions that researchers need to answer as they continue to revise their approach to monetary policy analysis. Copyright © 2009 by the National Bureau of Economic Research.

‘Risk Aversion, background Risk, and the Pricing Kernel’ looks in more detail at utility functions and their effect on the shape of the pricing kernel. The authors discuss the meaning of risk aversion and, in particular, ‘relative risk aversion’ and show that if relative risk aversion is constant at different levels of wealth, then the pricing kernel exhibits constant elasticity. They then show that the introduction of ‘background risk’, that is, non-hedgeable risks, causes the pricing kernel to exhibit declining elasticity. This effect on the pricing kernel is particularly significant for the pricing of options.

Given a pricing kernel we investigate the class of risks that are not priced by this kernel. Risks are random payoffs written on underlying uncertainties that may themselves either be random variables, processes, events or information filtrations. A risk is said to be not priced by a kernel if all derivatives on this risk always earn a zero excess return, or equivalently the derivatives may be priced without a change of measure. We say that such risks are not kernel priced. It is shown that reliance on direct correlation between the risk and the pricing kernel as an indicator for the kernel pricing of a risk can be misleading. Examples are given of risks that are uncorrelated with the pricing kernel but are kernel priced. These examples lead to new definitions for risks that are not kernel priced in correlation terms. Additionally we show that the pricing kernel itself viewed as a random variable is strongly negatively kernel priced implying in particular that all monotone increasing functions of the kernel receive a negative risk premium. Moreover the equivalence class of the kernel under increasing monotone transformations is unique in possessing this property.

Option prices in the market embed a probability distribution over the option’s payoff, which can be fairly easily extracted. But in this risk-neutral distribution, the market’s true expectations about future returns are distorted by risk preferences, in particular, aversion to volatility risk. To separate the returns forecasts from risk aversion, one needs another way to estimate the market’s probability estimates, which the author calls “actuarial probabilities.” This difficult problem becomes even harder in the swaptions market, because the risk exposure for a swaption depends on two different time horizons. For example, a 2 × 5 swaption has two years (“expiry”) of optionality culminating with the option to enter into a swap with a maturity (“tail”) of five years. The volatility surface is three-dimensional, with two time variables and the strike interest rate (the “volatility cube”), and it is difficult to estimate statistically using available data on long-lived contracts. The author develops a procedure based on extracting a small number of common factors of the volatility process using principal components. Since all swaptions are exposed to these factors, with different loadings, modeling the evolution of the principal components, rather than the specific swaption volatilities, allows a more efficient use of the data. Applying this approach to about 10 years of swaption data, the article reports several interesting results, including the fact that three principal components capture a large proportion of the variation in actuarial volatilities across the volatility cube, while a couple extra PCs are needed to achieve the same level of explanatory power for market implied volatilities. One clear result is that the market price of volatility risk is time-varying, and it moves rather quickly, with rates of mean-reversion that seem much faster than for actuarial volatility, particularly for short expiry–long tail swaptions. Another interesting result is that the volatility risk premium reverses sign, being largely positive (market IV > actuarial IV) for contracts with short expiry, but becoming negative for long expiry contracts.

Pricing kernels implicit in option prices play a key role in assessing the risk aversion over equity returns. We deal with nonparametric estimation of the pricing kernel (Empirical Pricing Kernel) given by the ratio of the risk-neutral density estimator and the subjective density estimator. The former density can be represented as the second derivative w.r.t. the European call option price function, which we estimate by nonparametric regression. The subjective density is estimated nonparametrically too. In this framework, we develop the asymptotic distribution theory of the EPK in the L1 sense. Particularly, to evaluate the overall variation of the pricing kernel, we develop a uniform confidence band of the EPK. Furthermore, as an alternative to the asymptotic approach, we propose a bootstrap confidence band. The developed theory is helpful for testing parametric specifications of pricing kernels and has a direct extension to estimating risk aversion patterns. The established results are assessed and compared in a Monte-Carlo study. As a real application, we test risk aversion over time induced by the EPK.

Behavorial Finance vs. Portfolio Theory: Is the Rational Investor Dead?

The Financial Risk Meter (FRM) employs Quantile-LASSO regression to identify systemic financial risk and dependencies among tail events across financial assets. This paper establishes, both theoretically and empirically, a meaningful economic relationship between the FRM index, derived from the penalization parameter in quantile LASSO regression, and the volatility of assets' pricing kernels, the attainable maximal Sharpe ratio, and market volatility. Despite the rapid growth of the crypto market and its increasing integration with traditional financial markets, there remains a dearth of risk measures in this space. FRM @ Crypto exhibits robust predictive capabilities in anticipating future market risk, potentially filling a critical void in this market.

This study examines the pricing kernel and empirical risk aversion implied by Korea’s equity-linked warrants (ELWs). The estimated pricing kernel is clearly time-varying and exhibits a monotonic decrease with the underlying return state, which is consistent with mainstream economic theories on marginal utility. The movement of empirical risk aversion captures the economic conditions reflecting the recent global and liquidity crises. Particularly, empirical risk aversion has a highly significant relationship with the overall stock market return and credit spread change.

In this article we use a novel clustering approach to study the role of heterogeneity in asset pricing. We present evidence that the equity premium is consistent with a stochastic discount factor (SDF) calculated as the average of the household clusters’ intertemporal marginal rates of substitution in the 1984–2002 period. The result is driven by the skewness of the cluster-based cross-sectional distribution of consumption growth, but cannot be explained by the cross-sectional variance and mean alone. We find that nine clusters are sufficient to explain the equity premium with relative risk aversion coefficient equal to six. The result is robust to various averaging schemes of cluster-based consumption growth used to construct the SDF. Lastly, the analysis reveals that standard approximation schemes of the SDF using individual household data produce unreliable results, implying a negative SDF.

Considering market fluctuations and risk aversion, this paper analyzes the decisions of a dual-channel supply chain consisting of one manufacturer and one retailer and compares the differences in the centralized structure, manufacturer-dominated structure, retailer-dominated structure, and Nash equilibrium structure. This paper also analyzes the impacts of market fluctuations and risk aversion on supply chain profits. The results show that the direct selling price is not influenced by market fluctuations and risk aversion under the retailer’s risk aversion, and the manufacturer-dominated structure and the retail price, direct selling price, and wholesale price in other power structures are negatively correlated with market fluctuations and risk aversion, but the order quantity in the four power structures is positively correlated with market fluctuations and risk aversion, and the profits of supply chain are impacted differently by the market fluctuations and risk aversion.

Though it is an important means for enterprises to increase market demand and boost profits, trade credit can carry risks. Besides, risks also result from uncertain market demand. Decision-makers’ attitude towards risk will influence the decisions of enterprises, so it is meaningful to study the impact of risk preference on supply chain performance. This paper explores the effect of risk-averse preferences of the manufacturer or retailer on their delayed payment periods decision and utility when the dual-channel structure is adopted. Customer demands are uncertain and depend on the delayed payment periods that the manufacturer and the retailer may offer to them. Mean-variance model is used to describe the risks due to uncertain demand, and establishes supply chain utility models under four decision-making situations (both are risk-neutral; only the retailer is risk-averse; only the manufacturer is risk-averse; both are risk-averse). According to our study, supply chain members with higher risk aversion are more inclined to prolong delayed payment period. The retailers risk aversion is adverse for her utility, but beneficial to the manufacturers utility, thus a new coordination mechanism is proposed to achieve coordinate when only the retailer is risk averse. We prove that the contract can effectively improve the utility of the whole supply chain. This paper conduces to enrich the emerging literature on relating risk aversion preferences to trade credit period decision and coordination behavior under dual-channel environment.

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