Jump risk premia across major international equity markets

We decompose the non-diversifiable market risk into continuous and discontinuous components and jump systematic risks into positive vs. negative and small vs. large components. We examine their association with equity risk premia across major equity markets. We show that developed markets jumps are more closely linked to the aggregate market index than emerging and frontier ones. The reward for bearing both the continuous and downside jump risks is positive during the pre-crisis period whereas the reward for bearing the upside and large jump risks is negative during the crisis and post-crisis periods. We also provide evidence of significant continuous and discontinuous leverage effects during the pre-crisis period, suggesting that both continuous and discontinuous price and volatility risks share compensations for common underlying risk factors.     

Macroeconomic news, announcements, and stock market jump intensity dynamics

This paper examines the effect of macroeconomic releases on stock market volatility through a Poisson-Gaussian-GARCH process with time-varying jump intensity, which is allowed to respond to such information. The day of the announcement, per se, is found to have little impact on jump intensities. Employment releases are an exception. However, when macroeconomic surprises are considered, inflation shocks show persistent effects while monetary policy and employment shocks reveal only short-lived effects. Also, the jump intensity responds asymmetrically to macroeconomic shocks. Evidence on macroeconomic variables relevance in explaining jump dynamics and improving volatility forecasts on event days is provided.     

Momentum and default risk. Some results using the jump component

In this paper we separate the total stock return into its continuous and jump component to test whether stock return predictability should be attributed to omitted risk factors or behavioral finance theories. We extend results from the US market to the Spanish stock market, which, despite being a developed market, presents several differences in terms of stock characteristics, financial system, investor typology and cultural dimensions. The results show that the jump component has significant explanatory power for the premium of three characteristics (size, book-to-market and illiquidity), which is at odds with risk-based explanations. Using the same testing strategy, we try to shed some light on an important controversy concerning the relationship between default risk and momentum. The results suggest that default risk is not the source of momentum returns.     

Option-implied volatility factors and the cross-section of market risk premia

The main goal of this paper is to study the cross-sectional pricing of market volatility. The paper proposes that the market return, diffusion volatility, and jump volatility are fundamental factors that change the investors’ investment opportunity set. Based on estimates of diffusion and jump volatility factors using an enriched dataset including S&P 500 index returns, index options, and VIX, the paper finds negative market prices for volatility factors in the cross-section of stock returns. The findings are consistent with risk-based interpretations of value and size premia and indicate that the value effect is mainly related to the persistent diffusion volatility factor, whereas the size effect is associated with both the diffusion volatility factor and the jump volatility factor. The paper also finds that the use of market index data alone may yield counter-intuitive results.     

Dynamic jump intensities and risk premiums: Evidence from S&P500 returns and options

We build a new class of discrete-time models that are relatively easy to estimate using returns and/or options. The distribution of returns is driven by two factors: dynamic volatility and dynamic jump intensity. Each factor has its own risk premium. The models significantly outperform standard models without jumps when estimated on S&P500 returns. We find very strong support for time-varying jump intensities. Compared to the risk premium on dynamic volatility, the risk premium on the dynamic jump intensity has a much larger impact on option prices. We confirm these findings using joint estimation on returns and large option samples.     

The Impact of Jumps in Volatility and Returns

This paper examines continuous‐time stochastic volatility models incorporating jumps in returns and volatility. We develop a likelihood‐based estimation strategy and provide estimates of parameters, spot volatility, jump times, and jump sizes using S&P 500 and Nasdaq 100 index returns. Estimates of jump times, jump sizes, and volatility are particularly useful for identifying the effects of these factors during periods of market stress, such as those in 1987, 1997, and 1998. Using formal and informal diagnostics, we find strong evidence for jumps in volatility and jumps in returns. Finally, we study how these factors and estimation risk impact option pricing.     

货币政策对中国股市连续性波动和跳跃性波动的影响研究

股票价格包括连续和跳跃两个部分,本文基于股市高频数据将中国股市的已实现波动分解为连续性波动和跳跃性波动,通过建立多元线性回归模型和Tobit模型,研究了存款准备金政策和利率政策对不同类型股市波动的影响。研究表明,存款准备金率调整的信息发布对连续性波动没有显著影响,但对跳跃性波动存在显著的影响;存款准备金率的实际调整对连续性波动、跳跃性波动均存在显著影响,但跳跃性波动更多地受到了信息发布时的影响;利率政策的调整对连续性波动和跳跃性波动存在显著影响,中国股市对利率政策变化提前作出反应。     

The short-term timing of initial public offerings

This paper examines the effect of stock market conditions on the waiting time of initial public offering (IPO) candidates, from the date firms file a registration statement with the Securities and Exchange Commission (SEC) to the effective IPO date. I find that issuers are going public faster when time-varying stock market valuations are high, and when time-varying market returns and time-varying market volatility are low. The volatility effect is not driven by regulatory delays consecutive to changes in the terms of the offers during the IPO process. Taken together, these results indicate that firms use a short-term market timing strategy when deciding the right time to go public and are consistent with a real option interpretation of IPO timing.     

Do jumps contribute to the dynamics of the equity premium?

This paper investigates whether risks associated with time-varying arrival of jumps and their effect on the dynamics of higher moments of returns are priced in the conditional mean of daily market excess returns. We find that jumps and jump dynamics are significantly related to the market equity premium. The results from our time-series approach reinforce the importance of the skewness premium found in cross-sectional studies using lower-frequency data; and offer a potential resolution to sometimes conflicting results on the intertemporal risk-return relationship. We use a general utility specification, consistent with our pricing kernel, to evaluate the relative value of alternative risk premium models in an out-of-sample portfolio performance application.     

Out-of-sample density forecasts with affine jump diffusion models

We conduct out-of-sample density forecast evaluations of the affine jump diffusion models for the S&P 500 stock index and its options’ contracts. We also examine the time-series consistency between the model-implied spot volatilities using options & returns and only returns. In particular, we focus on the role of the time-varying jump risk premia. Particle filters are used to estimate the model-implied spot volatilities. We also propose the beta transformation approach for recursive parameter updating. Our empirical analysis shows that the inconsistencies between options & returns and only returns are resolved by the introduction of the time-varying jump risk premia. For density forecasts, the time-varying jump risk premia models dominate the other models in terms of likelihood criteria. We also find that for medium-term horizons, the beta transformation can weaken the systematic effect of misspecified AJD models using options & returns.     

Pricing mortgage insurance contracts under housing price cycles with jump risk: evidence from the U.K. housing market

Previous studies have investigated the determinants of housing price cycles in the housing market; however, we observed the phenomenon of housing price jumps in the 2007 subprime crisis. This paper presents a discussion on the housing price cycle and abnormal price jumps to describe the behavior of housing prices in the United Kingdom. The empirical results show that the impact factors of housing cycles are market risk and the switching factor. Furthermore, the impact factors of jump risks include the bursting of the housing bubble and financial crises. Therefore, in this paper, we employ the Markov switching model with jump risks to value the MI contracts and analyze the influences of housing price cycles, jump risks, risks of market interest rate, and the prepayment risks on MI premiums. The results of sensitivity analysis show that more volatile housing price index returns, as well as longer periods of higher volatility in housing prices, raise MI premiums. Moreover, the MI premium is positively related to the absolute value of the average jump amplitude and the shock frequency of abnormal events. There is the tradeoff between the market interest rate and the prepayment risk. The influences of market interest rate are different on MI premium with/without prepayment risks.     

Maximum Likelihood Estimation of Latent Affine Processes

This article develops a direct filtration-based maximum likelihoodmethodology for estimating the parameters and realizations oflatent affine processes. Filtration is conducted in the transformspace of characteristic functions, using a version of Bayes’rule for recursively updating the joint characteristic functionof latent variables and the data conditional upon past data.An application to daily stock market returns over 1953–1996reveals substantial divergences from estimates based on theEfficient Methods of Moments (EMM) methodology; in particular,more substantial and time-varying jump risk. The implicationsfor pricing stock index options are examined.     

The determinants of stock–bond return correlations

I study the options-implied market risks that affect US stock–bond correlations from 2007 to 2021. I discover that US stock and bond market uncertainty, stock market tail risk, and global credit-default risk are dominant contributors to changing stock–bond correlations during the global financial crisis (GFC) period. However, these market risks collectively contribute much less to time-varying correlations in the post-GFC period. Furthermore, stock–bond correlations rise in times of rising US and global bond market risks. Rising stock market uncertainty raises stock–bond correlations in the GFC period but lowers them in the post-GFC period. My results disentangle the risks of stock and bond markets and show that equity tail risk, bond market risk, and stock market uncertainty are dominant factors in changing stock–bond diversification benefits in periods of market turmoil.     

Monte Carlo Option Pricing for Tempered Stable (CGMY) Processes

Lévy processes are popular models for stock price behavior since they allow to take into account jump risk and reproduce the implied volatility smile. In this paper, we focus on the tempered stable (also known as CGMY) processes, which form a flexible 6-parameter family of Lévy processes with infinite jump intensity. It is shown that under an appropriate equivalent probability measure a tempered stable process becomes a stable process whose increments can be simulated exactly. This provides a fast Monte Carlo algorithm for computing the expectation of any functional of tempered stable process. We use our method to price European options and compare the results to a recent approximate simulation method for tempered stable process by Madan and Yor (CGMY and Meixner Subordinators are absolutely continuous with respect to one sided stable subordinators, 2005).     

The role of time-varying jump risk premia in pricing stock index options

This paper examines out-of-sample option pricing performances for the affine jump diffusion (AJD) models by using the S&P 500 stock index and its associated option contracts. In particular, we investigate the role of time-varying jump risk premia in the AJD specifications. Our empirical analysis shows strong evidence in favor of time-varying jump risk premia in pricing cross-sectional options. We also find that, during a period of low volatility, the role of jump risk premia becomes less pronounced, making the differences across pricing performances of the AJD models not as substantial as during a period of high volatility. This finding can possibly explain poor pricing perfomances of the sophisticated AJD models in some previous studies whose sample periods can be characterized by low volatility.     

Trading strategies with partial access to the derivatives market

This research analyzes trading strategies with derivatives when there are several assets and risk factors. We investigate portfolio improvement if investors have full and partial access to the derivatives markets, i.e. situations in which derivatives are written on some but not all stocks or risk factors traded on the market. The focus is on markets with jump risk. In these markets the choice of optimal exposures to jump and diffusion risk is linked. In a numerical application we study the potential benefit from adding derivatives to the market. It turns out that e.g. diffusion correlation and volatility or jump sizes may have a significant impact on the benefit of a new derivative product even if market prices of risk remain unchanged. Given the structure of risk investors may have different preferences for making risk factors tradable. Utility gains provided by new derivatives may be both increasing or decreasing depending on the type of contract added.     

The Statistical and Economic Role of Jumps in Continuous-Time Interest Rate Models

This paper analyzes the role of jumps in continuous‐time short rate models. I first develop a test to detect jump‐induced misspecification and, using Treasury bill rates, find evidence for the presence of jumps. Second, I specify and estimate a nonparametric jump‐diffusion model. Results indicate that jumps play an important statistical role. Estimates of jump times and sizes indicate that unexpected news about the macroeconomy generates the jumps. Finally, I investigate the pricing implications of jumps. Jumps generally have a minor impact on yields, but they are important for pricing interest rate options.     

Can Warren Buffett forecast equity market corrections?

Warren Buffett suggested that the ratio of the market value of all publicly traded stocks to the Gross National Product could identify potential overvaluations and undervaluations in the US equity market when this ratio deviates above 120% or below 80%. We investigate whether this ratio is a statistically significant predictor of equity market corrections and rallies. We find that Buffett's decision rule does not deliver satisfactory forecasts. However, when we adopt a time-varying decision rule, the ratio becomes a statistically significant predictor of equity market corrections. The two time-varying decision rules are: (i) predict an equity market correction when the ratio exceeds a 95% one-tail confidence interval based on a normal distribution, and (ii) predict an equity market correction when the ratio exceeds a threshold computed using Cantelli's inequality. These new decision rules are robust to changes in the two key parameters: the confidence level and the forecasting horizon. This paper also shows that the MV/GNP ratio performs relatively well against the four most popular equity market correction models, but the ratio is not a particularly useful predictor of equity market rallies.     

TIME-VARYING FACTORS AND CROSS-AUTOCORRELATIONS IN SHORT-HORIZON STOCK RETURNS

In this paper I show that the lead-lag pattern between large and small market value portfolio returns is consistent with differential variations in their expected return components. I find that the larger predictability of returns on the portfolio of small stocks may be due to a higher exposure of these firms to persistent (time-varying) latent factors. Additional evidence suggests that the asymmetric predictability cannot be fully explained by lagged price adjustments to common factor shocks: (i) lagged returns on large stocks do not have a strong causal effect on returns on small stocks; (ii) trading volume is positively related to own- and cross-autocorrelations in weekly portfolio returns; and (iii) significant cross-autocorrelation exists between current returns on large stocks and lagged returns on small stocks when trading volume is high.     

中国股债市场长期动态相关性的影响因素研究——基于宏观不确定性和协动性视角

本文借鉴现代宏观经济学中的无套利仿射模型,基于"定价核"的定价方式,将股票市场和债券市场收益率之间的相关系数分解为其主要驱动因素--通货膨胀、真实利率和股息率的不确定性,以及三者之间的协方差。在实证部分,采用DCC-MGARCH模型计算股票市场和债券市场收益率的动态相关系数,验证中国股债相关性的时变规则;进而通过回归分析探究所选取的解释变量对中国股债相关性的贡献。结果表明,通货膨胀和股息率的不确定性以及真实利率与通货膨胀和股息率各自之间的协动性是影响这种相关性的主要因素;通胀冲击、真实利率和股息率可以解释这种相关性与长期动态的暂时背离。其中,通货膨胀和股息率的不确定性对股债相关性的影响与其他欧美主要经济体有着不同的表现,反映了中国市场的特殊性;此外,相较于中国经济市场的平稳时期,股市动荡期间各经济因素的影响会发生改变,且模型解释力会降低。