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This study develops FinText, a financial word embedding compiled from 15 years of business news archives. The results show that FinText produces substantially more accurate results than general word embeddings based on the gold-standard financial benchmark we introduced. In contrast to well-known econometric models, and over the sample period from 27 July 2007 to 27 January 2022 for 23 NASDAQ stocks, using stock-related news, our simple natural language processing model supported by different word embeddings improves realised volatility forecasts on high volatility days. This improvement in realised volatility forecasting performance switches to normal volatility days when general hot news is used. By utilising SHAP, an Explainable AI method, we also identify and classify key phrases in stock-related and general hot news that moved volatility.

Tesla has run-up again. Between December 2, 2019 and April 30, 2021, the price of Tesla rose more than ten times creating over $600 billion in investor wealth. The analysis presented here implies that the 10x jump cannot be explained by the arrival of fundamental information regarding the macroeconomy, the auto industry, or company specific information related to Tesla as an electric vehicle manufacturer. The only feasible explanation for the run-up was a spreading narrative that Tesla is more than a car company. As stated by Mr. Musk, and echoed by Cathie Wood and others, Tesla is also going to be a renewable energy, artificial intelligence, ride sharing, and robotics company. Such narratives have the advantage that they can blossom and replicate with little in the way of capital expenditure and improvements in operations both of which tend to have a sluggish impact on value. They also benefit from a feedback effect by which spread of the narrative drives up the stock price and the rising stock price is then interpreted as evidence for the veracity of the narrative. Of course, the spread of a narrative and the feedback effect can operate in both directions. A narrative that arises, spreads, and drives stock prices to new highs can collapse just as quickly.

We propose a unified dynamic framework to study the economics of the supply side of bitcoin mining, such as endogenous transaction fees, the miners' liquidation policies, and endogenous inventory holdings, in the face of declining system block rewards and stochastic demand. The model yields two economic insights: First, high jump risk and transaction fees income can be major forces driving miners to significantly reduce their inventory even when bitcoin prices are relatively low. Second, the model explains the observed co-movements of average transaction fees, average block sizes, and Bitcoin prices.

This paper examines the distributional properties of cryptocurrency realized variation measures (RVM) and the predictability of RVM on future returns. We show the cryptocurrency volatility persistence and the importance of the asymmetry on volatility forecasting. Signed jumps variations contribute around 18% of the cryptocurrency return quadratic variations. The realized signed jump (RSJ) strongly predicts the cross-sectional future excess returns. Sorting the cryptocurrencies into portfolios sorted by RSJ yields statistically and economically significant differences in future excess returns. This jump risk premium remains significant after controlling for cryptocurrency market characteristics and existing risk factors. The standard cross-sectional regression convinces the cryptocurrency return predictability from RSJ by controlling multiple cryptocurrency characteristics. The investor attention explains the predictability of realized jump risk in future cryptocurrency returns.

Interconnected banks are prone to the propagation of negative shocks. In a network with banks borrowing from each other using collateral, the risk of financial contagion leads to the emergence of multiple equilibria, featuring different sizes of loans and collateral haircuts. Safe assets are traded at a premium because safe collateral helps the economy to deter the propagation of negative shocks. A small decrease in collateral quality, increasing the contagion risk, might lead to an equilibrium jump to one with a smaller amount of interbank lending, along with which the safety premium will suddenly increase.

I propose a novel framework to quantify frequency-dependent risks in the factor zoo. Empirically, the linear stochastic discount factor (SDF) comprised of the first few low-frequency principal components (PCs) yields an out-of-sample monthly Sharpe ratio of 0.37, and other smaller low-frequency PCs are redundant. In contrast, the SDFs consisting of high-frequency and canonical PCs are dense and fail to identify slow-moving conditional information in asset returns. Moreover, I decompose the low-frequency SDF into two orthogonal priced components. The first component, linear in high-frequency PCs, is almost serially uncorrelated and relates to discount-rate news, intermediary factors, jump risk, and investor sentiment. The second component exhibits a persistent conditional dynamic and captures business-cycle risks related to consumption and GDP growth. Overall, asset pricing theory has frequency-dependent relevance.

We show that sudden and large price moves in bitcoin prices, which we call jumps, explain a large portion of the variation in bitcoin returns. In order to do so, we use the general utility specification adopted in Maheu et al. (2013) for characterizing the conditional mean of daily bitcoin excess returns (the crypto premium), and we relate higher order moments of the return conditional distribution to the stochastic discount factor on the bitcoin. The conditional skewness and kurtosis are both significantly priced, and a relevant portion of the variability of bitcoin returns can be attributed to compensation for the jump term. We show that the price of crypto risk is time-varying, and higher in bad times for investors, when the conditional variance and kurtosis are high.

Jumps and diffusive changes in stock prices are different ways in which information is reflected in the prices. We use nonparametric methods to decompose returns on individual stocks into jumps and diffusive components. Contrary to the conventional assumption that jump intensity is positively related to diffusive variance, we find abundant evidence that realized jump intensity and diffusive variance are uncorrelated or negatively related for a majority of stocks. The jump-diffusion beta is found to positively contribute to the implied volatility smile of options on individual stocks. We also document a counter-cyclical pattern of realized jump sizes, which challenges the i.i.d. jump size assumption commonly seen in the literature. The findings provide useful guidance on modeling option prices.

We provide plausibly causal evidence for the role of cultural and linguistic diversity in stock price crash risk. Using unique data from China, we show that firms headquartered in linguistically diverse areas, instrumented by the extent of geographical isolation, have higher stock price crash risk, but not higher jump risk. The effect is more pronounced for stocks that are more likely to be held by local investors. Our evidence further shows that linguistic diversity is associated with investor disagreement, and not with companies’ fundamentals, propensities to hoard bad news, or innovation activities. Our results are consistent with models of investor disagreement.

In pricing extreme mortality risk, it is commonly assumed that the interest rate and mortality rate are independent. However, the recent COVID-19 outbreak calls this assumption into question. We propose a bivariate affine jump-diffusion structure to jointly model the interest rate and excess mortality, allowing for both correlated diffusions and joint jumps. Utilizing the latest US mortality and interest rate data, we find a strong negative correlation between the jump sizes of interest rate and excess mortality, and a much higher jump intensity when the pandemic data is included. Moreover, we construct a risk-neutral pricing measure that accounts for both a diffusion risk premium and a jump risk premium. We then solve for the market prices of risk based on mortality bond prices. Our results show that the pandemic experience can drastically change investors’ risk perception and will likely reshape the post-pandemic mortality risk market.

This paper explores the contagious propagation of jumps among international stock market indices by exploiting a rich panel of stock and options data. We propose a multivariate option pricing model designed to allow for, but not superimpose, time and space amplification of jumps in option markets. We develop a semi-parametric estimation procedure employing a continuum of moments conditions in GMM with implied states. We introduce a partial-information approach to reduce the computational complexity arising in the multivariate setting, derive the asymptotic properties of our estimators, and analyze their finite-sample performance. Our empirical results reveal evidence of jump contagion in option markets, both from the US to Europe and vice versa, with the US leading the UK and standing on equal footing with Germany. We illustrate the importance of capturing jump contagion for risk management, option pricing, and scenario analysis.

This paper subjects the global fear index (GFI) for the COVID-19 pandemic to an empirical investigation to determine its relationship with realized volatility, continuous volatility, and jump volatility for seven international indices. The results show evidence of a positive relationship between the measures of realized volatilities and the global fear index, which is persistent in periods of high volatility, and a negative relationship in periods of low volatility. It is observed that when serial dependence, systemic risk and alternative measure of volatility are considered the significance of the COVID-19 global fear index and volatility relationship decreases significantly.

Residual stress distributions as a function of depth in APS TBCs after different heat treatment times have been measured by synchrotron XRD. Three types of residual stress distribution were observed. The first type was a low stress state from the as-received sample; the second type was a compressive stress state increasing from the surface to the interface with a discontinuous "jump feature" in the trend near the interface; the third type of residual stress distribution has two "jump" features in the trend, one was observed near the sample surface and the other near the interface. To understand the double "jump" features in the third kind of trend, the 3D microstructures of the measured samples were systematically measured X-ray μ-CT. Vertical cracks and edge cracks were observed. We have shown by image based modelling that the vertical cracks do not have a large effect on the residual stress distribution and that edge cracks are related to the "jump features" near the sample surface. The edge crack can be observed in all the measured samples with the third kind of the residual stress trend; and the measured samples with the second kind of the residual stress trend are crack free. The relationship between the middle and the through edge cracks and the residual stress distribution was also investigated by image based modelling. It was found that the middle crack can also cause a "jump feature" near the sample surface while the through edge crack seems to not have a large effect on the residual stress distribution. A semi-destructive method was developed to determine the existence and the position of horizontal cracks in the APS TBC samples based on the above findings.

This paper examines the finite sample properties of novel theoretical tests that evaluate the presence of: a) Brownian motion, b) jumps; c) finite vs. infinite activity jumps. In allowing for Gaussian, t-distributed, and Gaussian-T mixture noise, our Monte Carlo experiment guides a search for optimal performance across sampling frequencies. Using 100 stocks and SPY, we find that: i) a Brownian and a jump component characterize 1-min stock data; ii) Jumps should allow for both finite and infinite activity; iii) Rejection rates are time-varying, such that more jump days are usually associated with an increase of infinite jumps vis-a-vis finite jumps.

We consider an extension of the variance-gamma process implying that the linear drift rate of the process can switch suddenly by a jump. The value of jump is modeled by the multidimensional distribution, the jump time is simulated by the exponential distribution. Together with the simplest properties of the new process, its distribution function is derived explicitly. Applications to the credit risk measurement are supplied. Obtained results exploit values of some special mathematical functions including the generalized hypergeometric ones.

We adopt a flexible filtering procedure to extract information from high-frequency data. Specifically, we provide a parsimonious framework to integrate realized measures from high frequency index and derivative prices. In a simulation study, we document the incremental information offered by realized measures and show that even though high-frequency index prices help identify spot variance and jump price dynamics, it is the addition of high-frequency option prices that enables variance jumps to be identified. A series of empirical studies based on the S&P 500 index and options show that estimation precision improves with the addition of information from intraday option prices.

We show that the quotient of Levy processes of jump-diffusion type has a fat-tailed distribution. An application is to price theory in economics. We show that fat tails arise endogenously from modeling of price change based on an excess demand analysis resulting in a quotient of arbitrarily correlated demand and supply whether or not jump discontinuities are present. The assumption is that supply and demand are described by drift terms, Brownian (i.e., Gaussian) and compound Poisson jump processes. If $P^{-1}dP/dt$ (the relative price change in an interval $dt$) is given by a suitable function of relative excess demand, $\left( \mathcal{D}% -\mathcal{S}\right) /\mathcal{S}$ (where $\mathcal{D}$ and $\mathcal{S}$ are demand and supply), then the distribution has tail behavior $F\left( x\right) \sim x^{-\zeta}$ for a power $\zeta$ that depends on the function $G$ in $P^{-1}dP/dt=G\left( \mathcal{D}/\mathcal{S}\right) $. For $G\left( x\right) \sim\left\vert x\right\vert ^{1/q}$ one has $\zeta=q.$ The empirical data for assets typically yields a value, $\zeta\tilde{=}3,$ or $\ \zeta \in\left[ 3,5\right] $ for some markets. The discrepancy between the empirical result and theory never arises if one models price dynamics using basic economics methodology, i.e., generalized Walrasian adjustment, rather than the usual starting point for classical finance which assumes a normal distribution of price changes. The function $G$ is deterministic, and can be calibrated with a smaller data set. The results establish a simple link between the decay exponent of the density function and the price adjustment function, a feature that can improve methodology for risk assessment. The mathematical results can be applied to other problems involving the relative difference or quotient of Levy processes of jump-diffusion type.

This paper studies the performance of pairs trading strategy under a specific spread model. Based on the empirical evidence of mean reversion and jumps in the spread between pairs of stocks, we assume that the spread follows a Levy-driven Ornstein-Uhlenbeck process with twosided jumps. To evaluate the performance of a pairs trading strategy, we propose the expected return per unit time as the value function of the strategy. Significantly different from the current related works, we incorporate an excess jump component into the calculation of return and time cost. Further, we obtain the analytic expression of strategy value function, where we solve out the probabilities of crossing thresholds via the Laplace transform of first passage time of the Levy driven Ornstein-Uhlenbeck process in one-sided and two-sided exit problems. Through numerical illustrations, we calculate the value function and optimal thresholds for a spread model with symmetric jumps, reveal the non-negligible contribution of incorporating the excess jumps into the value function, and analyze the impact of model parameters on the strategy performance.

In this paper, we examine time-varying correlations among stock returns of Apple, Microsoft, Amazon and Google. Employing a multivariate DCC-GARCH model, we find that there are strong linkages among these four assets. Starting from lower levels, correlation values for most asset pairs exhibit a stable ascending movement in recent upward trended markets to, in an exceptional case, almost hit the perfect positive correlation mark. We show that correlations among these assets jump during downturn market periods, suggesting limits in the diversification of risk within the segment of large cap U.S. technology stocks. Our results are helpful for portfolio management and asset allocation.

This paper studies competitions with rank-based reward among a large number of teams. Within each sizable team, we consider a mean-field contribution game in which each team member contributes to the jump intensity of a common Poisson project process; across all teams, a mean field competition game is formulated on the rank of the completion time, namely the jump time of Poisson project process, and the reward to each team is paid based on its ranking. On the layer of teamwise competition game, three optimization problems are introduced when the team size is determined by: (i) the team manager; (ii) the central planner; (iii) the team members' voting as partnership. We propose a relative performance criteria for each team member to share the team's reward and formulate some special cases of mean field games of mean field games, which are new to the literature. In all problems with homogeneous parameters, the equilibrium control of each worker and the equilibrium or optimal team size can be computed in an explicit manner, allowing us to analytically examine the impacts of some model parameters and discuss their economic implications. Two numerical examples are also presented to illustrate the parameter dependence and comparison between different team size decision making., Comment: Final version, forthcoming in Applied Mathematics and Optimization

This paper examines the effects of futures trading on the jump risk in the Bitcoin market. We use a nonparametric method to detect Levy-type jumps in Bitcoin prices and obtain jump risk measures (jump intensity and jump size) of big and small jumps by using 5-minute high-frequency data, and document that Bitcoin has obvious features of Levy jumps, i.e., prices include both big jumps and small jumps, and the jump risk is time-varying. We then investigate the changes of these jump risk measures before and after the introduction of the Bitcoin futures, and we find that the jump size of big and small jumps decreases after the introduction of Bitcoin futures, while the big jump intensity increases. Further, we examine whether greater futures-trading activity (volume and open interest) is associated with greater Bitcoin jump risk and document that, on the one hand, there exists bidirectional causality between unexpected volume and jump intensity and size of Bitcoin and the impulse response function analysis show that the shocks have temporary increasing effects, and the speculative trading in the futures market will increase the jump risk. On the other hand, unexpected open interest Granger causes jump risk but the inverse is not true, indicating that investors are unlikely to hedge jump risk by using Bitcoin futures. These findings disclose the fact that the hedging function of the Bitcoin futures does not work well and the functions of Bitcoin futures need to be further improved.

Several procedures to estimate daily risk measures in cryptocurrency markets have been recently proposed in the literature. Among them, procedures taking into account the presence of extreme observations, as well as procedures that include more than a single regime, have performed substantially better than standard methods in terms of volatility and Value-at-Risk forecasting. Three of those procedures are revisited in this paper, and their Value-at-Risk forecasting performance is evaluated using recent cryptocurrency data that includes periods of turbulence. Those procedures are also extended to estimate the Expected Shortfall, and a comprehensive backtesting exercise based on both calibration tests and scoring functions is performed. In order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of forecast combinations strategies. In our empirical application, procedures that are robust to outliers performed slightly better than regime-switching models. We found some evidence that combining strategies can improve the forecasting of Value-at-Risk and Expected Shortfall, particularly for the 1% risk levels, making them an interesting alternative to be used by practitioners.

Do hedge funds hedge? In negative states of the world, often not as much as they should. For several styles, we report larger market betas when market returns are low (i.e., “beta in the tails”). We justify this finding through a combination of negative-mean jumps in the market returns and large market jump betas: when moving to the left tail of the market return distribution jump dynamics dominate continuous dynamics and the overall systematic risk of the fund is driven by the higher systematic risk associated with return discontinuities. Methodologically, the separation of continuous and discontinuous dynamics is conducted by exploiting the informational content of the high-order infinitesimal cross-moments of hedge-fund and market returns.

This paper provides new empirical evidence for the way in which non-marketability affects asset prices in financial markets. Critically, the results rely on the unique trading friction "T+1" rule in the Chinese A-share market. Consistent with the predictions derived from Longstaff (1995) and option pricing theory, overnight returns of A-share stocks are negative on average, decreasing with asset volatilities, volatility risks, and jump risks. The upper bound of the marketability option is restricted by the average holding period of the asset and is smaller when there are substitutional marketability instruments. Thus, the marketability-option-related variables could explain the negative overnight returns of illiquidity, short-term reversal, and momentum.

We analyze market order arrivals on the intraday market for hourly electricity deliveries in Germany. As we distinguish between buys and sells, we work in a multivariate setting. We model the arrivals with a Hawkes process with exponentially increasing baseline intensity and exponentially decaying excitements. Our goodness-of-fit tests indicate that the models where the conditional intensity of each market order type is excited at least by events of the same type are the most promising ones. Based on the Akaike information criterion, models with only self-excitement are selected slightly more often than models with self and cross-excitement. The typical jump size of the conditional intensities in case of the arrival of a market order of the same type is quite large yet rather short-lived. Diurnal patterns in the parameters of the baseline intensity and the branching ratio of the self-excitement are easily observable. Relationships between different parameters such as the jump size and decay rate of self and cross-excitement are found.

We develop a tractable equilibrium pricing model to explain observed characteristics in equity returns, VIX futures, S&P 500 options, and VIX options data based on affine jump- diffusive state dynamics and representative agents endowed with Duffie-Epstein recursive preferences. A specific model aimed at capturing VIX options prices and other asset market data is shown to successfully replicate the salient features of consumption, dividends, and asset market data, including the first two moments of VIX futures returns, the average implied volatilities in SPX and VIX options, and first and higher-order moments of VIX options returns. We document a time variation in the shape of VIX option implied volatility and a time-varying hedging relationship between VIX and SPX options which our model both captures.

We develop and implement asymptotic theory to conduct inference on continuous-time asset pricing models using individual equity returns sampled at high frequencies over an increasing time horizon. We study the identification and estimation of risk premia for the continuous and jump components of risks. Our results generalize the Fama-MacBeth two-pass regression approach from the classical discrete-time factor setting to a continuous-time factor model with general dynamics for the factors, idiosyncratic components and factor loadings, while accounting for the fact that the inputs of the second-pass regression are themselves estimated in the first pass. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Utilizing frame duality and a FFT-based implementation of density projection we develop a novel and efficient transform method to price Asian options for very general asset dynamics, including regime switching Levy processes and other jump diffusions as well as stochastic volatility models with jumps. The method combines Continuous-Time Markov Chain (CTMC) approximation, with Fourier pricing techniques. In particular, our method encompasses Heston, Hull-White, Stein-Stein, 3/2 model as well as recently proposed Jacobi, alpha-Hypergeometric, and 4/2 models, for virtually any type of jump amplitude distribution in the return process. This framework thus provides a unified approach to pricing Asian options in stochastic jump diffusion models and is readily extended to alternative exotic contracts. We also derive a characteristic function recursion by generalizing the Carverhill-Clewlow factorization which enables the application of transform methods in general. Numerical results are provided to illustrate the effectiveness of the method. Various extensions of this method have since been developed, including the pricing of barrier, American, and realized variance derivatives.

Having observed a cluster of jumps in the discrete prices of a financial asset, we study and quantify the probability that the cluster is going to produce further jumps. Modeling the jump arrival times with an exponential Hawkes process, we provide some bounds for the future stochastic jump intensity under different hypotheses about the concentration of the past jumps arrivals. Moreover, we formalize the stochastic increasing property of the durations between two consecutive jumps and study the relation between the intensity decay time and the length of the possible clusters. Consequently, we provide formulas for the probability that, conditionally on the observation of a cluster of jumps, the generating phenomenon will produce a further jump, and bounds for the probability of observing a given number of consecutive jumps. As an empirical exercise, we apply our results to a record of the JPM asset prices. Firstly we show that the identified jumps display dependence and clustering behavior. Secondly, we find that, under the exponential Hawkes model delivering the best QQplot, the probability that a cluster of more than 1 jump produces a further jump is mostly close to 1.

Modeling of uncertainty by probability errs by ignoring the uncertainty in probability. When financial valuation recognizes the uncertainty of probability the best the market may offer is a two price framework of a lower and upper valuation. The martingale theory of asset prices is then replaced by the theory of nonlinear martingales. When dealing with pure jump compensators describing probability the uncertainty in probability is captured by introducing parametric measure distortions. The two price framework then alters asset pricing theory by requiring two required return equations one each for the lower upper valuation. Proxying lower and upper valuations by daily lows and highs the paper delivers the first empirical study of nonlinear martingales via the modeling and simultaneous estimation of the two required return equations.

Forecasting oil price volatility is considered of major importance for numerous stakeholders, including, policy makers, industries and investors. This paper examines and evaluates the main factors that oil price volatility forecasters should take before constructing their forecasting models. Such factors are related to: i) direct vs iterated forecasts, ii) the incorporation of continuous and jump components, iii) the importance of semi variance volatility measures, and iv) OLS vs time-varying parameter (TVP) estimation procedures. Even more, we evaluate the performance of these factors for both realized and implied volatility measures, based on statistical loss functions, as well as, their economic use. The results show that depending on whether end-users are interested in forecasting the realized or the implied volatility, the factors influencing the accuracy of forecasts are different. In particular, for the realized volatility, direct forecasting based on TVP estimation procedure, as well as, using the information obtained in the semi variance measures are capable of producing significantly superior forecasts. By contrast, separating the continuous and the jump components of the realized volatility does not provide any added value to these forecasts. Turning to the OVX, based on the economic evaluation of our forecasts, the TVP estimation procedure seems to performbetter. In addition, we find evidence that the continuous component and the semi variance measures of the realized volatility also yield better OVX forecasts in the longer run horizons.

The present article studies geometric step options in exponential Levy markets. Our contribution is manifold and extends several aspects of the geometric step option pricing literature. First, we provide symmetry and parity relations and derive various characterizations for both European-type and American-type geometric double barrier step options. In particular, we are able to obtain a jump-diffusion disentanglement for the early exercise premium of American-type geometric double barrier step contracts and its maturity-randomized equivalent as well as to characterize the diffusion and jump contributions to these early exercise premiums separately by means of partial integro-differential equations and ordinary integro-differential equations. As an application of our characterizations, we derive semi-analytical pricing results for (regular) European-type and American-type geometric down-and-out step call options under hyper-exponential jump-diffusion models. Lastly, we use the latter results to discuss the early exercise structure of geometric step options once jumps are added and to subsequently provide an analysis of the impact of jumps on the price and hedging parameters of (European-type and American-type) geometric step contracts.

This paper proposes a new reduced-form model for the pricing of VIX derivatives that includes an independent stochastic jump intensity factor and cojumps in the level and variance of VIX, while allowing the mean of VIX variance to be time varying. I fit the model to daily prices of futures and European options from April 2007 through December 2017. The empirical results indicate that the model significantly outperforms all other nested models and improves on benchmark by 21.6% in sample and 31.2% out of sample. The model more accurately portrays the tail behavior of VIX risk-neutral distribution for both short and long maturities, as it better captures the time-varying skew found to be largely independent of the level of the VIX smile. This paper was accepted by Kay Giesecke, finance.

We model the S&P500 index options dynamics using the CGMY distribution, with independent "up" and "down" return jumps, and diffusive jump intensities. Allowing the up and down parts to be separately parameterised accounts for the dynamic smirk effect, without correlation between returns and intensities. We filter the up and down intensity factors and associated options risk premia. Both factors are informative for dynamic risk premia of equity and corporate debt. We argue that the former is a liquidity effect, and the latter an equilibrium effect. The magnitudes of the estimated, dynamic equity premia are consistent with the "equity premium puzzle".

In recent years, commodity markets have become increasingly popular among financial investors. While previous studies document a factor structure, not much is known about how prominent anomalies are priced in commodity futures markets. We examine a large set of such anomaly variables. We identify sizable premia for jump risk, momentum, skewness, and volatility-of-volatility. Other prominent variables, such as downside beta, idiosyncratic volatility, and MAX, are not priced in commodity futures markets. Commodity investors should rebalance their portfolios regularly. Returns for annual holding periods are substantially weaker than for monthly rebalancing.

This paper investigates whether individual CEOs and CFOs have “styles” (i.e. managers’ fixed- effects) when it comes to withholding bad and good corporate news, which is captured using the firm-level future stock price crash and jump risk. Tracking managers that move across firms and employing a manager fixed effect model, we find that both CEOs and CFOs have fixed-effects on firm-level future stock price crash and jump risk, using multiple crash (jump) risk measures adopted from previous studies (for example, Kim et al. 2011a,b). Such effects are subjected to a battery of robustness tests, including using a placebo data. In addition, we document that besides bad news withholding channels suggested in the existing literature (e.g. earnings management, management guidance, and tax avoidance), managers have fixed-effects on “other channels” of news withholding, which is measured parsimoniously using a crash risk residual. We also find that CEOs have stronger fixed-effects than CFOs in affecting firm-level stock price crash and jump risk. Lastly, we find that certain demographic characteristics, including past professional qualifications (CPA license), educational background (MBA and JD degree), past military experience, and family status are associated with the crash and jump risk of the firm.

We study optimal trading strategy of a market maker with stock inventory in the presence of short-term market changes, especially changes in trading intensity of market participants and stock volatility. We employ Poisson jump processes in modelling such market condition changes. We provide closed form optimal bidding and asking strategies of the market maker, and analyze the market maker’s inventory changes accordingly.

We ask whether option prices contain information on the likelihood and direction of jumps in the underlying stock prices. Applying the partial least squares (PLS) approach to the entire surface of the implied volatilities (IV), we show that option prices can successfully predict downward jumps in stock prices, but not upward jumps. The PLS estimated downward jump factor can predict stock returns with a spread of 1.53% per month between stocks predicted to have the lowest probability of downward jumps and stocks predicted to have the highest probability of downward jumps. Both put and call option prices, and options of both short and long maturity contribute to the predictability. Furthermore, the predictability of the downward jump is robust to many firm characteristics as well as options related variables. Consistent with the notion that informed investors trade in the options markets to profit from negative information in order to circumvent the short-sale constraint, we find that stronger predictability is associated with tighter short-sale constraints in the equity market, and in periods when the market has poor performance.

We develop a modelling framework for multiple yield curves driven by continuous-state branching processes with immigration (CBI processes). Exploiting the self-exciting behavior of CBI jump processes, this approach can reproduce the relevant empirical features of spreads between different interbank rates. In particular, we introduce multi-curve models driven by a flow of tempered alpha-stable CBI processes. Such models are especially parsimonious and tractable, and can generate contagion effects among different spreads. We provide a complete analytical framework, including a detailed study of discounted exponential moments of CBI processes. The proposed approach allows for explicit valuation formulae for all linear interest rate derivatives and semi-closed formulae for non-linear derivatives via Fourier techniques and quantization. We show that a simple specification of the model can be successfully calibrated to market data.

The present article deals with intra-horizon risk in models with jumps. Our general understanding of intra-horizon risk is along the lines of the approach taken in [BRSW04], [Ro08], [BMK09], [BP10], and [LV20]. In particular, we believe that quantifying market risk by strictly relying on point-in-time measures cannot be deemed a satisfactory approach in general. Instead, we argue that complementing this approach by studying measures of risk that capture the magnitude of losses potentially incurred at any time of a trading horizon is necessary when dealing with (m)any financial position(s). To address this issue, we propose an intra-horizon analogue of the expected shortfall for general profit and loss processes and discuss its key properties. Our intra-horizon expected shortfall is well-defined for (m)any popular class(es) of Levy processes encountered when modeling market dynamics and constitutes a coherent measure of risk, as introduced in [CDK04]. On the computational side, we provide a simple method to derive the intra-horizon risk inherent to popular Levy dynamics. Our general technique relies on results for maturity-randomized first-passage probabilities and allows for a derivation of diffusion and single jump risk contributions. These theoretical results are complemented with an empirical analysis, where popular Levy dynamics are calibrated to the S&P 500 index and the Brent crude oil data, and an analysis of the resulting intra-horizon risk is presented.

This research investigates the fundamental components of corporate credit spreads through an integrated examination of how well different models, default probability functions, and market factors explain CDS prices. Individual company yield spreads are discovered to be determined by the interrelated risks of cash, income, and valuation insolvency, along with various measures of systematic downside risk. The empirical findings indicate that the priced risk of a jump to and at default is well explained by those fundamentals, including in ex-post tests, although the financial determinants of credit spreads are found to vary significantly across different groups of companies.

Under the stochastic volatility-of-volatility framework, we show that oil volatility-of-volatility risk is a significant pricing factor for cross-sectional delta-hedged gains constructed from 1-month United States Oil Fund (USO) options, and is negatively priced. Moreover, oil volatility-of-volatility risk can significantly and negatively predict one-period ahead delta-hedged option gains. The findings are robust after implementing several tests such as controlling for jump risk measures, another measure of oil volatility-of-volatility and delta-hedged gains constructed from 1-week USO options. The information content of oil volatility-of-volatility is also distinctive from its equity counterpart, which can contribute to predicting the future real personal consumption expenditure.

Option pricing models are tools for pricing and hedging derivatives. Good models are complex and the econometrician faces many design decisions when bringing them to the data. I show that strategically constructed low-dimensional filter designs outperform those that try to use all the available option data. I construct Unscented Kalman Filters around option portfolios that aggregate option data, and track changes in risk-neutral volatility and skewness. These low-dimensional filters perform equivalently to or better than standard approaches that treat full option panels. The performance advantage is greatest in empirically relevant settings: in models with strongly skewed jump components that are not driven by Brownian volatility.

Existing studies relate the puzzling low average returns on out-of-the-money (OTM) index call and put options to nonstandard preferences. We argue the low option returns are primarily due to the pricing of market volatility risk. When volatility risk is priced, expected option returns match the realized average option returns. Moreover, consistent with its theoretical effect on expected option returns, the volatility risk premium is positively related to future index option returns and this relationship is stronger for OTM options and at-the-money straddles. Finally, we find the jump risk premium contributes to some portion of OTM put option returns.

We revisit the optimal capital structure model with endogenous bankruptcy first studied by Leland \cite{Leland94} and Leland and Toft \cite{Leland96}. Differently from the standard case, where shareholders observe continuously the asset value and bankruptcy is executed instantaneously without delay, we assume that the information of the asset value is updated only at intervals, modeled by the jump times of an independent Poisson process. Under the spectrally negative L\'evy model, we obtain the optimal bankruptcy strategy and the corresponding capital structure. A series of numerical studies are given to analyze the sensitivity of observation frequency on the optimal solutions, the optimal leverage and the credit spreads., Comment: Forthcoming in Finance and Stochastics

We investigate how systematic, continuous, and discrete (jump) risk affects the cross section of expected stock returns in Southeast Asia. Using the latest econometric techniques and a high-frequency dataset, we construct two high-frequency betas associated with intraday continuous and discontinuous risk premia. To improve our consistency, we use several statistical robustness levels and multiple frequencies. We empirically find that both continuous and discontinuous risk premia are significant and positive in Indonesia, and these results are consistent for lower frequency data samples. We also reveal that diffusive and jump risk premia have different impacts in other countries, but the results are not consistent for lower frequency samples.

We propose a model-free method for measuring the jump skewness risk premium via a trading strategy. We find that in the S&P 500 option market, the premium is positive and greater in absolute terms than the variance premium. The trading strategy allows for examining the premium in different holding periods: daytime, when markets are open, and overnight, outside of trading hours. We demonstrate that both premia vary considerably between trading and non-trading periods, and also increase in periods of market distress. The daytime return on jump skewness is not spanned by other systematic risk factors, suggesting it is a systematic risk factor itself. Outside of trading hours, skewness risk does not seem to be distinguishable from variance risk. We also decompose total return skewness into a jump component, and a signed variance component, and demonstrate that only the jump component is priced. Our work also sketches a new set of stylized facts about variance and jump skewness premia that option pricing models should match.

We identify queue-jumping as a key mechanism that causes markets to fragment. We use the introduction (and partial removal) of the Order Protection Rule, which enforces strict inter-venue price (not time) priority, to observe its impacts on fragmentation. We document that brokers increasingly fragment their liquidity provision activities amongst alternative venues, and liquidity providers attempt to jump long queues on larger venues by increasing submissions to venues with short (or empty) queues, which reduces their adverse selection costs. Our findings help explain the acceleration of fragmentation in markets with trade-through prohibitions as compared to best executions, providing clear policy implications.

I use intraday data from 2013 to 2017 and a dataset of NASDAQ-100, MSCI USA and MSCI USA Small Cap Index constituent changes to investigate abnormal returns and trading volume around index rebalancings. The results show no pre-announcement speculation but a significantly positive (negative) post-announcement jump followed by an intraday drift for promotions (demotions). For small-cap changes, the post-announcement jump is smaller but abnormal returns with the same sign are also found on the rebalancing date and especially during the last 30 minutes of trading. Large-cap index changes have higher abnormal trading volume but much smaller abnormal returns on the rebalancing date. Consistent with the price pressure hypothesis, I find a reversal during the early trading hours on the day following the rebalancing date.

In an important paper, Bollerslev, Li, and Zhao (2019) find that there is significant predictive content in signed total jump variation. In this paper, we further decompose jumps into small and large (signed) variation. Our primary purpose is to investigate the marginal predictive content of small versus large jump variation, when forecasting oneweek ahead cross-sectional equity returns. However, we also examine earnings announcements, in order to shed new light on the linkages between (small and large) jumps and news. One of our key findings is that sorting on signed small jump variation leads to greater value-weighted return differentials between stocks in our highest and lowest quintile portfolios (i.e., high-low spreads) than when either signed total jump or signed large jump variation is sorted on. Moreover, in a key case, the high-low spread is not significantly different from zero when signed large jump variation is sorted on. Indeed, including large jump variation can actually decrease predictive accuracy, in the sense that average returns and alphas for high-low portfolios are lower when total jump variation is utilized in our prediction experiments rather than small jump variation. These results suggest that there may be a threshold, beyond which “large” jump variation contains no marginal predictive ability, relative to that contained in small jump variation. Analysis of returns and alphas based on industry double-sorts indicates that the benefit of small signed jump variation investing is driven by stock selection within an industry, rather than industry bets. Investors prefer stocks with a high probability of large positive jump variation, but they also tend to overweight safer industries. Additionally, the fact that large and small (signed) jump variation have differing marginal predictive content is explained at least in part by our observation that in double-sorted portfolios, the content of signed large jump variation is negligible when controlling for either signed total jump variation or realized skewness. By contrast, signed small jump variation has unique information for predicting future returns, even when controlling for total jump variation or realized skewness. Further, we find that large jumps are closely associated with “big” news, as might be expected. In particular, large earning announcement surprises increase both the magnitude and occurrence of large jumps. While such news related information is embedded in large jump variation, the information is generally short-lived, and dissipates too quickly to provide marginal predictive content for subsequent weekly returns. Finally, we find that while large jump variation is closely associated with large earnings surprises (“big” news), small jumps tend to be more closely associated with idiosyncratic risks, and can be diversified away.