• Title/Summary/Keyword: volatility asset model

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OPTION PRICING IN VOLATILITY ASSET MODEL

  • Oh, Jae-Pill
    • Korean Journal of Mathematics
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    • v.16 no.2
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    • pp.233-242
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    • 2008
  • We deal with the closed forms of European option pricing for the general class of volatility asset model and the jump-type volatility asset model by several methods.

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Asset Pricing From Log Stochastic Volatility Model: VKOSPI Index (로그SV 모형을 이용한 자산의 가치평가에 관한 연구: VKOSPI 지수)

  • Oh, Yu-Jin
    • The Korean Journal of Applied Statistics
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    • v.24 no.1
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    • pp.83-92
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    • 2011
  • This paper examines empirically Durham's (2008) asset pricing models to the KOSPI200 index. This model Incorporates the VKOSPI index as a proxy for 1 month integrated volatility. This approach uses option prices to back out implied volatility states with an explicitly speci ed risk-neutral measure and risk premia estimated from the data. The application uses daily observations of the KOSPI200 and VKOSPI indices from January 2, 2003 to September 24, 2010. The empirical results show that non-affine model perform better than affine model.

Volatility clustering in data breach counts

  • Shim, Hyunoo;Kim, Changki;Choi, Yang Ho
    • Communications for Statistical Applications and Methods
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    • v.27 no.4
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    • pp.487-500
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    • 2020
  • Insurers face increasing demands for cyber liability; entailed in part by a variety of new forms of risk of data breaches. As data breach occurrences develop, our understanding of the volatility in data breach counts has also become important as well as its expected occurrences. Volatility clustering, the tendency of large changes in a random variable to cluster together in time, are frequently observed in many financial asset prices, asset returns, and it is questioned whether the volatility of data breach occurrences are also clustered in time. We now present volatility analysis based on INGARCH models, i.e., integer-valued generalized autoregressive conditional heteroskedasticity time series model for frequency counts due to data breaches. Using the INGARCH(1, 1) model with data breach samples, we show evidence of temporal volatility clustering for data breaches. In addition, we present that the firms' volatilities are correlated between some they belong to and that such a clustering effect remains even after excluding the effect of financial covariates such as the VIX and the stock return of S&P500 that have their own volatility clustering.

Dynamics of Asset Returns Considering Asymmetric Volatility Effects: Evidences from Korean Asset Markets (우리나라 자산가격 변동의 기준점 효과 및 전망이론적 해석 가능성 검정)

  • Kim, Yun-Yeong;Lee, Jinsoo
    • KDI Journal of Economic Policy
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    • v.33 no.1
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    • pp.93-124
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    • 2011
  • In this paper, we claim the asymmetric response of asset returns on the past asset returns' signs may be explained from the market behavioral portfolio choice of investors. For this, we admit the anchor and adjustment mechanism of investors which partly explains the momentum in the asset prices. We also claim the prospect theory based on the risk aversions may simultaneously work with the anchor and adjustment effect, whenever the lagged asset return was positive and investors accrued the gain. To identify these effects empirically in a threshold autoregressive model, we suppose the risk aversions inducing the volatility effect is related with the past volatility of asset returns. In application of suggested method to Korean stock and real estate markets, we found these effect exist as expected.

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Performances of Simple Option Models When Volatility Changes

  • Jung, Do-Sub
    • Journal of Digital Convergence
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    • v.7 no.1
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    • pp.73-80
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    • 2009
  • In this study, the pricing performances of alternative simple option models are examined by creating a simulated market environment in which asset prices evolve according to a stochastic volatility process. To do this, option prices fully consistent with Heston[9]'s model are generated. Assuming this prices as market prices, the trading positions utilizing the Black-Scholes[4] model, a semi-parametric Corrado-Su[7] model and an ad-hoc modified Black-Scholes model are evaluated with respect to the true option prices obtained from Heston's stochastic volatility model. The simulation results suggest that both the Corrado-Su model and the modified Black-Scholes model perform well in this simulated world substantially reducing the biases of the Black-Scholes model arising from stochastic volatility. Surprisingly, however, the improvements of the modified Black-Scholes model over the Black-Scholes model are much higher than those of the Corrado-Su model.

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Variance Swap Pricing with a Regime-Switching Market Environment

  • Roh, Kum-Hwan
    • Management Science and Financial Engineering
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    • v.19 no.1
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    • pp.49-52
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    • 2013
  • In this paper we provide a valuation formula for a variance swap with regime switching. A variance swap is a forward contract on variance, the square of realized volatility of the underlying asset. We assume that the volatility of underlying asset is governed by Markov regime-switching process with finite states. We find that the proposed model can provide ease of calculation and be superior to the models currently available.

The Stochastic Volatility Option Pricing Model: Evidence from a Highly Volatile Market

  • WATTANATORN, Woraphon;SOMBULTAWEE, Kedwadee
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.2
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    • pp.685-695
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    • 2021
  • This study explores the impact of stochastic volatility in option pricing. To be more specific, we compare the option pricing performance between stochastic volatility option pricing model, namely, Heston option pricing model and standard Black-Scholes option pricing. Our finding, based on the market price of SET50 index option between May 2011 and September 2020, demonstrates stochastic volatility of underlying asset return for all level of moneyness. We find that both deep in the money and deep out of the money option exhibit higher volatility comparing with out of the money, at the money, and in the money option. Hence, our finding confirms the existence of volatility smile in Thai option markets. Further, based on calibration technique, the Heston option pricing model generates smaller pricing error for all level of moneyness and time to expiration than standard Black-Scholes option pricing model, though both Heston and Black-Scholes generate large pricing error for deep-in-the-money option and option that is far from expiration. Moreover, Heston option pricing model demonstrates a better pricing accuracy for call option than put option for all level and time to expiration. In sum, our finding supports the outperformance of the Heston option pricing model over standard Black-Scholes option pricing model.

Dynamic Glide Path using Retirement Target Date and Forecast Volatility (은퇴 시점과 예측 변동성을 고려한 동적 Glide Path)

  • Kim, Sun Woong
    • Journal of Convergence for Information Technology
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    • v.11 no.2
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    • pp.82-89
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    • 2021
  • The objective of this study is to propose a new Glide Path that dynamically adjusts the risky asset inclusion ratio of the Target Date Fund by simultaneously considering the market's forecast volatility as well as the time of investor retirement, and to compare the investment performance with the traditional Target Date Fund. Forecasts of market volatility utilize historical volatility, time series model GARCH volatility, and the volatility index VKOSPI. The investment performance of the new dynamic Glide Path, which considers stock market volatility has been shown to be excellent during the analysis period from 2003 to 2020. In all three volatility prediction models, Sharpe Ratio, an investment performance indicator, is improved with higher returns and lower risks than traditional static Glide Path, which considers only retirement date. The empirical results of this study present the potential for the utilization of the suggested Glide Path in the Target Date Fund management industry as well as retirees.

Elaboration of Real Options Model and the Adequacy of Volatility

  • Sung, Tae-Eung;Park, Hyun-Woo
    • Asian Journal of Innovation and Policy
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    • v.6 no.2
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    • pp.225-244
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    • 2017
  • When evaluating the economic value of technology or business project, we need to consider the period and cost for commercialization. Since the discounted cash flow (DCF) method has limitations in that it can not consider consecutive investment or does not reflect the probabilistic property of commercialization cost, we often take it desirable to apply the concept of real options with key metrics of underlying asset value, commercialization cost, and volatility, while regarding the value of technology and investment as the opportunity value. We at this moment provide more elaborated real options model with the effective region of volatility, which reflects the uncertainty in the option pricing model (OPM).