Earticle

Managers, stockholders, lenders and employees concern about their firm’s financial condition. This shared interest creates continual inquiries and recurrent attempt to answer the incessant question about how we predict financial distress or what reveals the credit risk of firms. Despite numerous attempts for bankruptcy prediction and their application over three decades after Altman (1968)’s seminal study, financial distress prediction research has not seemed to reach an unequivocal conclusion. We investigated our postulations concerning Altman’s Z-score and the option-based measure based on arguments that the Z-score should lose its significance since its introduction due to some reasons. Based on our results, we learned that Altman’s Z-score loses its significance as a bankruptcy prediction measure due to two possible grounds; it loses its prediction power for long-term prediction and it was not significance for recent years’ data. In addition, we found that the option-based measure does provide significant results as a prediction measure for later years. We believe that the reduction of prediction time span of Z-score and better performance of the option-based measure implies that the more efficient market shortens the information transition time in the market so that bankruptcy prediction should be based on immediate and continuously changing information about the event and discrete or sporadic variables would mislay the interpretation of information concerning bankruptcy.

옵션은 대표적인 파생상품의 하나로서 , 옵션의 가격이 다른 투자자산 가격의 미래가치에 대한 기대치를 포함하고 있다 . , 옵션 가격에서 옵션 만기시점의 기초자산 가격에 대한 위험중립 확률분포를 구하는 연구가 많이 진행되어져 왔다 . 본 연구에서는 반모수적 기법인 에지워드 확장(Edgeworth Expansion)에 가장 일치하는 위험중립 확률분포를 구하는 방법을 제시한다 . 또한, 기존의 에지워드 확장을 이용한 위험중립 확률분포를 추정하는 방법에 옵션 별 거래량시 한다. 본 방법론을 이용하여 한국 KOSPI 200 인덱스 옵션으로부터 기초자산인 KOSPI 200 인덱스의 미래시점의 위험중립 확률분포를 구해보고 옵션의 정 방법의 성능을 평가하였다 . 두 방법을 비교한 결과 out-of-the-money 옵션의 경우, 평균적으로 거래량 가중치를 이용한 경우가 더 작은 오차를 가지는 것으로 나타났다. 또한 에지워드 확장을 통해 도출한 기초자산 수익률의 변동성 값을 블랙-숄즈 모형(Black-Scholes formula)의 입력변수로 사용하는 방법을 제안하고 이를 평가하였다.

As one of the most representative derivatives, the price of option reflects the expectation value of an underlying asset at the maturity date. Due to the property, a lot of researches have been done on the subject of abstracting underlying asset’s risk neutral PDF(probability density function) at the maturity date from option price. This study implements and proposes a semi-parametric approach, called Edgeworth Expansion, to estimate the risk neutral PDF of underlying assets from option premium. We suggest the new approach by modifying previously studied Edgeworth Expansion to reflect the number of transactions of each option. This study applies the new method to KOSPI 200 Index Option on the Korean Stock Exchange Market and measure the appropriateness of the method by comparing it to the method used in the previous study. Furthermore, we suggests to use the volatility value abstracted from Edgeworth Expansion method as the input value of Black-Scholes model, and evaluate the erroneousness of this suggestion.

임의상환가능 상품은 옵션을 이용하여 수요창출과 리스크를 줄이는 서비스 파생상품의 일종이다. 본 논문은 임의상환가능 상품이 도입된 후, 예약 요청의 온라인승인 방법과 일괄승인 방법 둘의 성능을 비교한다. 최적의 예약관 리법을 계산하기 위하여, 역행 동적계획법(Backward Dynamic Programming)과 확률적 최적화(Stochastic Optimization) 방법을 이용한다. 직관적으로는 공급자는 수요정보를 이용하여 일괄승인 방법으로 더 높은 수익을 올 릴 것으로 예상되지만, 본 논문은 두 예약 요청 승인 방법의 예약관리전략과 수익은 동일하다는 것을 증명함으로써, 현행 예약 요청 승인 방법의 변화 없이 임의상환가능 상품을 빠르게 도입할 수 있다는 실무적인 기여도가 있다. 본 논문은 세 종류의 서비스 요금 클래스가 있을 경우 최적의 해를 정확히 구하였다. 향후 연구는 다양한 요금 모델에서 최적의 해를 구하는 것이다.

A callable product is one of service derivatives using options to generate demand and reduce risk. This paper compares two booking admission controls for callable products, the online and the batch admission controls. To this end, the paper computes the optimal booking policy by using the backward dynamic programming and the stochastic optimization method. Intuitively, the provider should outperform under the batch control by utilizing demand information. The contribution of the paper is to show that the two controls are equivalent in terms of the booking strategy and the expected profit, which enables the provider to keep its current control method. The paper develops the closed-form solutions for the three fare classes. The future work is to extend the result to the model with complicated fare structures.

This paper documents that median holding period in structured products based on market index is less than a day from initial purchase to liquidation even for retail investors. Less than 6% of all series ever traded by retail investors are held until maturity. More importantly, buy-and-hold strategies perform worse than frequent trading. Based on a unique proprietary dataset that provides the details of all transactions - including account identifier and direction of the trade - in the Korean ELW (equity linked warrant) market between 2009 and 2011, we find that both HFT (high frequency trader) accounts and non-HFT accounts perform worse when either average holding period is long or average end-of-the-day position is large. Such failure of buy-and-hold strategy likely reflects time decaying properties, i.e. theta, of option-like products. Our findings suggest that measuring expected returns for options simply assuming that they are held until maturity may underestimate the true expected return.

In recent years, financial industry has been growing rapidly. Especially the various products cause by financial engineering has been the major phenomenon of the development of financial market. If we call standard options, first generation options, then non-standard options can be called second generation options. Second generation options are exotic option. Option pricing method which used in this study were Closed-form formular assumed the log - normal price distribution and model followed underlying asset price distribution. In this study, Closed-form formular and Binomial model were programed by Excel Basic As a result of analysis. Binomial model is very convenient or speedy in exotic option which have special payoff pattern pricing.

본 연구의 목적은 오피스텔 개발에 따른 현금흐름 할인모형(DCF, Discounted Cash Flow)과 실물옵션 (Real Option)비교를 통해 가치평가 결과의 차이가 얼마나 나타나는가와 2개의 모형 중에 어떠한 모형이 가치평 가에 적합한지를 파악하는 것이다. 오피스텔 개발 사례는 서울 중구지역에 지하 5층 지상 14층 규모로 사업기간 총 3년의 부지면적 2,815㎡, 576세대이다. 토지매입비는 416억원, 건축비용은 507억원, 대출 이자율 6%, 현가할인 율은 3.72%였다. 자기자본비용을 구하기 위해서는 CAPM모형을 활용하였다. DCF법으로 사업 평가 결과, 사업성 이 있는 것으로 나타났다. NPV로 계산하면, 218억원 이익이 발생하는 것으로 나타났다. 미래의 불확실성을 반영 하는 실물옵션으로 사업 평가를 한 결과, 확장된 순현재가치(ENPV)는 263억원으로, 기존 순현재가치(NPV)의 이 익이 더 많았다. 결과적으로 DCF법과 리얼옵션은 장단점이 있지만, 부동산과 같이 경기변동이 심한 업종은 변동 성 변수를 통해 도출된 실물옵션이 더 사업성 분석에 적합함이 밝혀졌다.

The purpose of this study is to compare the DCF model and Real Option according to the development of officetel, how much difference is there in the valuation result? And, it is to figure out which of the two models is suitable for valuation. The case of officetel development is 5 stories below the ground and 14 stories above the ground in Jung-gu, Seoul. The land purchase cost was 41.6 billion won, the construction cost was 50.7 billion won, the loan interest rate was 6%, and the present discount rate was 3.72%. The CAPM model was used to calculate the cost of equity capital. As a result of the project evaluation using the discounted cash flow method, it was found to be feasible. Calculated by NPV, a profit of KRW 21.8 billion was found. As a result of project evaluation with real options that reflect future uncertainty, the expanded net present value (ENPV) was 26.3 billion won. The original net present value (NPV) was more profitable. As a result, DCF and real options have advantages and disadvantages, but In the case of industries with large economic fluctuations such as real estate, real options derived through volatility variables are more suitable for business feasibility analysis.

This paper newly focuses on an unexplored dimension of fund managers’ timing ability; marketwide tail risk implied by information in option market. We investigate whether hedge fund managers can strategically time market tail risk implied by option through adjusting their portfolios’ market exposure to changes of market tail risk. Using an extensive sample of 6147 equity-oriented hedge funds from 1996 to 2012, we find strong evidence of tail risk timing ability of hedge fund managers. We conduct bootstrap analysis and confirm that our tail risk timing ability is not attributed to pure luck. Furthermore, tail risk timing ability brings a significant economic value to investors. Specifically, in out-of-sample tests, top-ranked hedge funds outperform bottom-ranked funds by 5-7% annually after adjusting common risk factors. Also, we find that managers’ tail risk timing skill persists over time, suggesting that hedge fund managers’ tail risk timing ability reflects true managerial skill. Our overall results are robust to various hedge fund characteristics, subsample or sub-period analysis, the use of alternative timing abilities, and other hedge funds’ managerial skills. All the empirical examination emphasizes the role of market-wide option-implied tail risk in hedge fund managers’ skill and their performance.

This study sheds light on the role of option-implied investor sentiment in the credit default swap (CDS) market. Due to the limits to arbitrage caused by credit or counterparty risk and margin requirements, CDS spreads may deviate from fundamentals under the influence of sentiment-driven investors who possess excessively bearish or bullish perceptions to the market or to the firms. We derive several systematic and firm-specific sentiment measures from index options and individual stock options, respectively, and we investigate their impacts on CDS spreads. The sentiment influence is significant, even after controlling the fundamental variables, and is more pronounced for lower-rated CDS obligors during a turbulent period, which is consistent with the limits to arbitrage theory.

This paper constructs an equilibrium model of option-implied preferences with model uncertainty. Our theoretical model shows that an investor with model uncertainty has a higher level of risk aversion than an investor without model uncertainty, which is helpful in explaining the equity premium puzzle. Using the detection-error probabil- ity, we estimate the option-implied uncertainty aversion. Empirical ¯ndings show that the estimated option-implied risk aversion with model uncertainty is larger than that without model uncertainty. With the higher level of uncertainty aversion, the empirical uncertainty premium shows the steeper smirk pattern across the wealth, which looks very similar to the smirk pattern of the implied volatility of S&P 500 index options.

In this paper, we examine whether greater option-trading activity (volume and open interest) is associated with greater stock market volatility using a regime-switching GARCH model. We first partition KOPSI 200 option volume and open interest into expected, unexpected, and moving average components to highlight how differently stock market volatility is related to forecastable optiontrading activity and unexpected (informed) option volume. Next, we classify option-trading activity based on moneyness to study how each class is related to stock market volatility. Further, we partition stock market into volatile and stable regimes to investigate how informed option traders react differently in the option market according to the state of the stock market. Empirical results show that informed traders prefer to highly leveraged option in volatile market, while they prefer to relatively less leveraged options in stable market.

For the KOSPI 200 Index options, we examine the effect of extreme events for pricing options. We compare Black and Scholes(1973) model with Câmara and Heston(2008)’s options pricing model that allows for both big downward and upward jumps. It is found that Câmara and Heston(2008)’s extreme events option pricing models shows better performance than Black and Scholes(1973) model for both in-sample and out-of-sample pricing. Also downward jumps are more important factor for pricing stock index options than upward jumps. It is consistent with the empirical evidence that reports the sneers or negative skews in the stock index options market.

We compare quantum Monte Carlo with classic Monte Carlo methods in pricing vanilla options. The quantum Monte Carlo method was performed on a quantum computer using amplitude estimation, an algorithm that provides excellent convergence rates. We find that quantum simulations can achieve stability in computing price and sensitivity. Furthermore, the quantum Monte Carlo method is superior to classical ones in convergence speed and stability.

Psychological barriers are prevalent among various asset classes and it is important to consider their impacts on the prices of derivative securities. This paper shows the potential existence of such barriers on the S&P 500 Index and examines their impacts on the rate of return and the volatility of the index. It focuses on deriving analytic European option prices under the assumption that the dynamics of the stock price follow a threshold model; this paper also evaluates this model’s empirical performance in comparison with the Black- Scholes model and the constant elasticity of variance (CEV) model. The in-sample calibration result of the threshold model is found to be much superior. Furthermore it is found that the model provides an efficient hedging method in terms of dollar-value hedging errors.

This study examines the empirical performance of three model-based option valuation approaches in the KOSPI200 options market. We evaluate the in-sample pricing, out-of-sample pricing and hedging performance of the approaches based on the specification of option pricing models directly (a pricing model-based approach), on the pricing kernels implied by the option pricing models (an implied pricing kernel-based approach), and on parametric pricing kernels which are independently structured to have their own explicit functional forms (a parametric pricing kernel-based approach). Two option pricing models, a GARCH option pricing model and a Black-Scholes (BS) option pricing model, and their implied pricing kernels are analyzed and two parametric pricing kernel specifications suggested by Rosenberg and Engle (2002) are compared in a unified framework which extends the GARCH process of Duan (1995) to reflect the dynamics of asymmetric volatility. We find that the empirical performance of the approaches related to the GARCH and Black-Scholes option pricing models is moderately improved when we estimate the structural parameters using options data (options-based estimation) compared to the model performances when estimating the parameters using only a time-series of underlying returns data (underlying returns-based estimation). With the estimates under the underlying returns-based estimation, the pricing modelbased option valuation approach outperforms the implied pricing kernel-based option valuation approach for both the GARCH and BS option pricing models. However, with the estimates under the options-based estimation, this relationship is reversed in pricing OTM options in the case of the GARCH option pricing model. Although the BS option pricing model is generally the worst performer with the estimates under the underlying returns-based estimation, it yields better performance for pricing ITM options and similar performance for hedging compared to the GARCH option pricing model with the estimates under the options-based estimation. The option valuation approach based on the parametric pricing kernel of which functional form is a Chebyshev polynomial performs best out of all approaches and methods considered in this study

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.

This paper examines the effect of analyst stock recommendations on equity option market activity in US over the 1996 to 2002 period. I find that the implied volatilities of recommended stocks gradually increase up to the recommendation revision date and stay at the increased level after the revision, especially following downgrades. This pattern, however, seems to reflect changes in the past realized volatilities more than ex post future realized volatilities, indicating that option market may be overreacting to recommendation revisions. A delta hedged trading strategy that shorts call options on recommendation revision date yields significant positive profits before transaction costs, supporting the overreaction hypothesis. Analysis of cumulative returns and abnormal trading volume prior to the revision further suggests that there is more information trading in option market than in stock market.

This paper examines the source of the predictive power of option-implied skewness (OIS) for future stock returns. Given that informed investors prefer to trade in the options market and it takes time for the information contained in the option prices to get incorporated into stock prices, OIS can predict future stock returns. We hypothesize that the predictive power of OIS stems from a delayed response of stock prices to information observable in the options market, and document evidence in support. In particular, we find that the relation between OIS and future stock returns is stronger among stocks that are more costly to shortsell. Higher shorting costs are presumed to deter stock prices from reflecting the information embedded in OIS, resulting in a stronger positive relation between OIS and stock returns. Moreover, we reveal that the predictive power of OIS is more prominent and persistent during high-sentiment periods. It also supports that stock mispricing is associated with the positive OIS-return relation, considering that high sentiment produces overpricing more so than low sentiment produces underpricing.