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This study examines the effects of crisis-related factors on the returns of KOSPI200 index options using a factor model, which was introduced by Constantinides, Jackwerth and Savov (2013). Three factors incorporating price jumps, changes in volatility, and volatility jumps are considered as the crisis-related factors. With the data for the period from 2004 to 2015, we find followings : First, most of the crisis-related factor premia are statistically significant, and their signs are consistent with those expected. Second, these crisis-related factors contribute to improve the understanding of the cross-sectional variation in KOSPI200 index option returns. Third, the crisis-related factor premia became much more significant after the global financial crisis in 2008. Finally, our empirical findings are robust to whether the long options and the in-the-money options are included in the sample or not, and to whether the factor premia are constrained to equal the corresponding premia estimated from the cross-section of equities.

The purpose of this paper is to apply the real option method with fuzzy logic to value the government‐sponsored projects of advanced technology development for strategic selection in an uncertain competitive environment.

For strategic selection of government‐sponsored industrial R&D projects, in this paper, Carlsson and Fúller's model was adopted which employs fuzzy logic to estimate the benefits and costs calculated from various scenarios and utilizes Black‐Scholes‐Merton model. The model of strategic selection is suggested for government R&D with fuzzy real option valuation (FROV) and the portfolio planning model from GE‐Mckinsey matrix as well.

FROV was found to be more appropriate to measure the strategic value than the traditional financial method (net present value, NPV, etc.). When the NPV is ambiguous in deciding whether to go or not to go, for instance, just below zero NPV and high volatility of expected benefit, FROV can offer the additive value of the project reflecting volatility of benefit due to the volatility.

Based on insufficient practical data, this methodology should be verified with various projects and measuring volatility of pay‐off requires precise analysis. In addition, research opportunities are in the stepwise R&D project with fuzzy compound real option.

Many papers on economic analysis of R&D project are focused on NPV or cost‐benefit analysis in the public sector. Several attempts with real option have been conducted in the pharmaceutical field or the aerospace (NASA) industry but are not concerned with the fuzziness of expected benefit. Hence, in this paper, fuzzy logic is added to handle imprecise information on the Black‐Scholes‐Merton model with dividend paying.

Due to disequilibrium between supply and demand in the option market, the option market‐maker is under exposure to certain risks because of their net option positions. This paper aims to pay attention to whether the risk award affects the option price and the shape of implied volatility in the market‐maker system.

The paper first eliminates the part of implied volatility explained by underlying asset's stochastic volatility‐jump price process, and second sorts out market investors' net demand data from TAIEX Options tick by tick deal data and then finally considers three market maker's risks – unhedgeable risk, capital constrain risk and asymmetric information risk, and how they affect implied volatility's level and slope.

Through the research in the TAIEX Option market, the paper finds that, under unhedgeable risk, net demand pressure has a significant impact on implied volatility. Especially, unhedgeable risk due to underlying asset's stochastic volatility has the best explanation for implied volatility level, and unhedgeable risk due to underlying asset's jump can explain implied volatility slope to some extent. Capital constrain risk and asymmetric information risk have an insignificant impact on implied volatility.

The findings in this study suggest that the risk award affects the option price and the shape of implied volatility in the market‐maker system and different risks have different effects on the level and slope of option implied volatility.

This paper finds the influence factors of the option price in the market‐maker system. It's useful for China's financial government and investors to learn the price tendency and regular pattern in the future China option market.

This is the first time that a net demand pressure based option pricing model is used, which is derived by Garleanu, Pedersen and Poteshman, to study the TAIEX Options' implied volatility. And the paper improves the methods eliminating the part of implied volatility explained by underlying asset's stochastic volatility‐jump price process.

This paper aims to examine the nexus between the pricing of market-wide volatility risk and distress risk in the cross-section of portfolio returns for the 1990-2011 time period. The author expands upon prior research by constructing an ex post factor that mimics aggregate volatility risk based on the new VIX index of the Chicago Board Options Exchange, termed FVIX, as well as focuses on volatility risk in crisis versus non-crisis time periods.

The author investigates the relationship between volatility and distress risk using several techniques in the empirical finance literature. Specifically, the author investigates the behavior of correlations between risk factors as well as the correlations between factor loadings when using the Fama and French research portfolios as our test assets for different time periods. Additionally, the author examines the variation in the volatility factor loadings across the size- and value-sorted portfolios and assesses whether augmenting conventional pricing models with a volatility factor leads to a higher goodness-of-fit in pricing the 25 size- and value-sorted portfolios.

The author’s results suggest that factor volatilities are high during periods of market turmoil. In addition, the author presents evidence indicating that a factor mimicking innovation in volatility (based on the new VIX) is correlated with the market and momentum factors, while exhibiting the uncorrelated behavior with respect to the size, value and liquidity factors when using data from 1990 through 2011. In this paper, the author finds that the aggregate volatility factor’s correlation with the market and momentum factors increases during crisis periods. In periods of relative market tranquility, correlations decrease significantly. In examining multivariate factor loadings for the test assets, the results provide no clear pattern with regard to the variation of the volatility loadings across the book-to-market and size dimensions. Furthermore, the author finds that conventional pricing models are comparable to FVIX-augmented pricing models, in terms of goodness-of-fit, when pricing the 25 Fama-French size- and value-sorted portfolios. Additionally, when using the FVIX volatility factor to proxy for aggregate volatility risk, the coefficients are never significant statistically, thus revealing that innovations in aggregate volatility based on the new VIX index do not constitute a priced risk factor in the cross-section of returns.

The author’ finding indicates an absence of strong variation of the volatility factor loadings across the Fama-French research portfolios. In particular, the asset pricing results cast doubt on whether a factor mimicking innovations in aggregate volatility based on the new VIX index is priced. In agreement with prior research, the author believes that the inseparability of volatility and jump risk in the VIX can be a possible explanation of the current findings in this paper.

The purpose of this paper is to consider a monetary-jump model to measure the contribution of jumps to the total volatility of interest rates in the Brazilian interbank market and to assess the extent to which the central bank’s unanticipated monetary policy decisions are driving these jumps.

The authors use a sample of swap rates contracts with different maturities to estimate a mixture GARCH-jump model that disentangles two components of interest rate volatility: a GARCH-type specification that models conditional heteroskedasticity to account for the volatility during “normal” times and a Poisson process that models the occurrence of abrupt changes in interest rates.

The contribution of jumps to the total volatility is substantial, and monetary policy decisions partly explain the occurrence of those jumps. In particular, the authors find that the likelihood of a jump occurring during a meeting day of the Brazilian central bank’s monetary policy committee (COPOM) is higher in comparison to that of a non-meeting day.

The occurrence of jumps in the term structure of interest rates raises the question of the transmission mechanism of the monetary policy through the asset price channel as well as the relation between jumps and economic fundamentals.

Communication between the central bank and the market will affect expectations and asset values. If the central bank’s decisions generate fewer jumps, then the variance of the interest rate-linked asset values will also be reduced.

The paper employs a new approach to assess monetary policy surprises to a set of Brazilian interest rate data and relates the occurrence of jumps to the macroeconomic environment.

The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory.

This paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry.

This paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased.

This paper has research limitations in variable measurement and data selection.

This study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration.

The existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.

The purpose of this paper is to propose a model for ruin‐contingent life annuity (RCLA) contracts under a jump diffusion model, where the dynamics of volatility is governed by the Heston stochastic volatility framework. The paper aims to illustrate that the proposed jump diffusion process, for both asset price and stochastic volatility, will provide a more realistic pricing model for RCLA contracts in comparison to existing models.

Under the assumption of the deterministic withdrawals, the authors use a partial integro differential equation (PIDE) approach to develop the pricing scheme for the fair value of the lump sum charges of RCLA contracts. Consequently, the authors employ an elegant numerical scheme, finite difference method, for solving the PIDEs for the reference portfolio, as well as the volatility. The findings show that a different pricing behaviour of the RCLA contracts under the authors' model parameters is obtained compared to that in the existing literature.

RCLA pricing in the complete market often underestimates the jump risk and the persistent factor in the volatility process. The authors' generalized model shows how these two random sources of risks can be precisely characterized.

The parameter values used in the numerical analysis require more empirical evidence. Hence, in order for more precise pricing practice, the calibration from real data is needed.

The model, as adopted in this study, for pricing of RCLA contracts should provide a general guideline for the commercialization of this product by insurance companies.

The demand for RCLA contracts as an alternative to the commonly‐practised annuitization option has recently increased, rapidly, among the soon‐to‐retire baby boomers. This paper investigates the fair value of this particular product, which could be beneficial to researchers for a better understanding of the product design.

This is the first research paper which prices the RCLA contracts in the incomplete market. The gap between RCLA contract pricing and studies of jump diffusion models for derivative pricing, in the literature, is therefore filled.

The authors examine the relation between noise trading in equity markets and stochastic volatility by estimating a two-factor jump diffusion model. Their analysis shows that contemporaneous price deviations in the derivatives market are statistically significant in explaining movements in index futures prices and option-market volatility measures.

To understand the impact noise may have in the S&P 500 derivatives market, the authors first measure and evaluate the influence noise exerts on futures prices and then investigate its influence on option volatility.

In the period from 1996 to 2003, this study finds significant changes in the volatility and mean reversion in the noise level and a significant increase in its relation to implied volatility in option prices. The results are consistent with a bubble in technology stocks that occurred with significant increases in noise trading.

This study provides estimates for this model during the periods preceding and during the technology bubble. The study analysis shows that the volatility and mean reversion in the noise level are much stronger during the bubble period. Furthermore, the relation between noise trading and implied volatility in the futures market was of a significantly larger magnitude during this period. The study results support the importance of noise trading in market bubbles.

Bloomfield, O'Hara and Saar (2009) find that noise traders lower bid–ask spreads and improve liquidity through increases in trading volume and market depth. Such improved market conditions could have positive effects on market quality, and this impact could be evidenced by lower implied volatility when noise traders are more active. Indeed, the results in this study indicate that the level and characteristics of noise trading are fundamentally different during the technology bubble, and this noise trading activity has a larger impact during this period on implied volatility in the options market.

This paper uniquely analyzes derivatives on the S&P 500 Index in order to detect the presence and influence of noise traders. The authors derive and implement a two-factor jump diffusion noise model. In their model, noise rectifies the difference of analysts' opinions, market information and beliefs among traders. By incorporating a reduced-form temporal expression of heterogeneities among traders, the model is rich enough to capture salient time-series characteristics of equity prices (i.e. stochastic volatility and jumps). A singular feature of the authors’ model is that stochastic volatility represents the random movements in asset prices that are attributed to nonmarket fundamentals.

We propose a new discrete-time model of returns in which jumps capture persistence in the conditional variance and higher-order moments. Jump arrival is governed by a heterogeneous Poisson process. The intensity is directed by a latent stochastic autoregressive process, while the jump-size distribution allows for conditional heteroskedasticity. Model evaluation focuses on the dynamics of the conditional distribution of returns using density and variance forecasts. Predictive likelihoods provide a period-by-period comparison of the performance of our heterogeneous jump model relative to conventional SV and GARCH models. Furthermore, in contrast to previous studies on the importance of jumps, we utilize realized volatility to assess out-of-sample variance forecasts.

The topic of volatility measurement and estimation is central to financial and more generally time-series econometrics. In this chapter, we begin by surveying models of volatility, both discrete and continuous, and then we summarize some selected empirical findings from the literature. In particular, in the first sections of this chapter, we discuss important developments in volatility models, with focus on time-varying and stochastic volatility as well as nonparametric volatility estimation. The models discussed share the common feature that volatilities are unobserved and belong to the class of missing variables. We then provide empirical evidence on “small” and “large” jumps from the perspective of their contribution to overall realized variation, using high-frequency price return data on 25 stocks in the DOW 30. Our “small” and “large” jump variations are constructed at three truncation levels, using extant methodology of Barndorff-Nielsen and Shephard (2006), Andersen, Bollerslev, and Diebold (2007), and Aït-Sahalia and Jacod (2009a, 2009b, 2009c). Evidence of jumps is found in around 22.8% of the days during the 1993–2000 period, much higher than the corresponding figure of 9.4% during the 2001–2008 period. Although the overall role of jumps is lessening, the role of large jumps has not decreased, and indeed, the relative role of large jumps, as a proportion of overall jumps, has actually increased in the 2000s.