Advances in variance analysis permit the splitting of the total quadratic variation of a jump-diffusion process into upside and downside components. Recent studies establish that this decomposition enhances volatility predictions and highlight the upside/downside variance spread as a driver of the asymmetry in stock price distributions. To appraise the economic gain of this decomposition, we design a new and flexible option pricing model in which the underlying asset price exhibits distinct upside and downside semivariance dynamics driven by the model-free proxies of the variances. The new model outperforms common benchmarks, especially the alternative that splits the quadratic variation into diffusive and jump components.

[We provide results for the valuation of European-style contingent claims for a large class of specifications of the underlying asset returns. Our valuation results obtain in a discrete time, infinite state space setup using the no-arbitrage principle and an equivalent martingale measure (EMM). Our approach allows for general forms of heteroskedasticity in returns, and valuation results for homoskedastic processes can be obtained as a special case. It also allows for conditional nonnormal return innovations, which is critically important because heteroskedasticity alone does not suffice to capture the option smirk. We analyze a class of EMMs for which the resulting risk-neutral return dynamics are from the same family of distributions as the physical return dynamics. In this case, our framework nests the valuation results obtained by Duan (1995) and Heston and Nandi (2000) by allowing for a time-varying price of risk and nonnormal innovations. We provide extensions of these results to more general EMMs and to discrete-time stochastic volatility models, and we analyze the relation between our results and those obtained for continuous-time models.]

Standard Gaussian term structure models impose the Markov property: the conditional mean is a function of the risk factors. We relax this assumption and consider models where yields are linear in the conditional mean (but not in the risk factors). To illustrate, yields should span expected inflation but not inflation. Second, expected and surprise yield changes can have opposite contemporaneous effects on expected inflation. Third, the survey forecasts and inflation rate can both be in the state. These three features are inconsistent with the Markov assumption. These effects matter empirically in the USA and in Canada.

We propose a new methodology for modeling and estimating time-varying downside risk and upside uncertainty in equity returns and for assessment of risk–return trade-off in financial markets. Using the salient features of the binormal distribution, we explicitly relate downside risk and upside uncertainty to conditional heteroskedasticity and asymmetry through binormal GARCH (BiN-GARCH) model. Based on S&P 500 and international index returns, we find strong empirical support for existence of significant relative downside risk, and robust positive relationship between relative downside risk and conditional mode.

We build a model of bond yields in an economy with secular changes to inflation, real rate, and output growth. Long-run restrictions identify nominal shocks that do not influence the long-run real rate and output growth. Before the anchoring of inflation around the mid-1990s, nominal shocks lifted the output gap and inflation. This led to a higher and steeper yield curve because the short rate was expected to peak after several quarters, following declines in the responses of growth and inflation. With inflation anchored, nominal shocks have small impacts on inflation, output, and bond yields, mostly via the term premium.

Under very general conditions, the total quadratic variation of a jump-diffusion process can be decomposed into diffusive volatility and squared jump variation. We use this result to develop a new option valuation model in which the underlying asset price exhibits volatility and jump intensity dynamics. The volatility and jump intensity dynamics in the model are directly driven by model-free empirical measures of diffusive volatility and jump variation. Because the empirical measures are observed in discrete intervals, our option valuation model is cast in discrete time, allowing for straightforward filtering and estimation of the model. Our model belongs to the affine class enabling us to derive the conditional characteristic function so that option values can be computed rapidly without simulation. When estimated on S&P500 index options and returns the new model performs well compared with standard benchmarks.

Many studies have documented that daily realized volatility estimates based on intraday returns provide volatility forecasts that are superior to forecasts constructed from daily returns only. We investigate whether these forecasting improvements translate into economic value added. To do so, we develop a new class of affine discrete-time option valuation models that use daily returns as well as realized volatility. We derive convenient closed-form option valuation formulas, and we assess the option valuation properties using Standard & Poor’s (S&P) 500 return and option data. We find that realized volatility reduces the pricing errors of the benchmark model significantly across moneyness, maturity, and volatility levels.

Nous prsentons les rsultats d'une tude portant sur l'valuation de crances ventuelles de style europen pour une grande varit de caractristiques lies au rendement des actifs sousjacents. Les rsultats de notre valuation proposent en temps discret une formule tat-espace infinie, partir du principe de non-arbitrage et d'une mesure de martingale quivalente. Notre approche permet de tenir compte de formes gnrales d'htroscdasticit dans les rendements et d'obtenir, dans des cas spciaux, des rsultats d'valuation lis aux processus homoscdastiques. Elle permet aussi de considrer les innovations conditionnellement non normales en matire de rendement, ce qui reprsente un facteur critique, compte tenu du fait que l'htroscdasticit ne permet pas, elle seule, de saisir pleinement le caractre ironique de l'option. Nous analysons une catgorie de mesures de martingale quivalentes dont la dynamique du rendement risque-neutre obtenu est de la mme famille de distribution que la dynamique du rendement physique. Dans ce cas, notre cadre d'tude soutient les rsultats d'valuation obtenus par Nous tendons ces rsultats aux mesures de martingale quivalentes plus gnrales et aux modles de volatilit stochastique en temps discret et analysons aussi la relation entre nos rsultats et ceux obtenus dans le cas des modles en temps continu.

We estimate a continuous-time model for the stock market index where the stochastic volatility and crash probability depend on the realized spot variance and the stock market illiquidity. We find that market illiquidity is a useful economic covariate in the modeling of time-varying stock market crash risk embedded in index options. The relative contribution of spot variance in the time-varying crash risk is weakened once the market illiquidity variable is added to the model, and out-of-sample option pricing error also improves. Examining the relationship between market illiquidity and option-implied crash risk, we find that the availability of arbitrage capital and adverse selection facing liquidity providers are potential economic links. Our study highlights the benefits of adding a market illiquidity measure to index return models with time-varying crash risk.

This paper investigates the importance of U.S. macroeconomic news in driving low-frequency fluctuations in the term structure of interest rates in Canada, Sweden, and the U.K. We follow two complementary approaches: First, we apply a regression-based framework that aggregates the impact of daily macroeconomic news on bond yields to a lower quarterly frequency. Next, we estimate a macro-finance affine term structure model linking the daily news to lower-frequency changes in bond yields and its expectations and term premia components. Both approaches show that U.S. macroeconomic news is an important source of lower-frequency quarterly fluctuations in bond yields in these open economies, and even more important than their respective domestic macroeconomic news. Furthermore, the macro-finance model shows that U.S. macroeconomic news is particularly important in explaining low-frequency changes in the expectation components of the nominal, real, and break-even inflation rates.