Suk Joon Byun

Continuing Overreaction and Stock Return Predictability

We study the return predictability of a measure of continuing overreaction based on the weighted average of signed volumes. We find that the strategies of buying stocks with upward continuing overreaction and selling stocks with downward continuing overreaction generate significant positive returns and that our measure of continuing overreaction is a better predictor of future returns than past returns. The results are stronger among stocks primarily held by investors more prone to biased self-attribution. Our results provide direct support for the model of return predictability based on overconfidence and biased self-attribution.

Conditional Volatility and the GARCH Option Pricing Model with Non-Normal Innovations

Based on the theory of a wedge between the physical and risk-neutral conditional volatilities in Christoffersen, Elkamhi, Feunou, and Jacobs (2009), we develop a modification on the GARCH option pricing model with the filtered historical simulation proposed in Barone-Adesi, Engle, and Mancini (2008). The current conditional volatilities under the physical and risk-neutral measures are the same in the previous model, but should have been allowed to be different. Using an extensive data on S&P 500 index options, our approach, which employs the current risk-neutral conditional volatility estimated from the cross-section of the option prices (in contrast to the existing GARCH option pricing models), maintains theoretical consistency under conditional non-normality as well as improves the empirical performances. Remarkably, the risk-neutral volatility dynamics are stable over time under this model. In addition, the comparison between the VIX index and the risk-neutral integrated volatility validates our approach economically.

Intraday volatility forecasting from implied volatility

Purpose - The purpose of this paper is to examine whether the superiority of the implied volatility from a stochastic volatility model over the implied volatility from the Black and Scholes model on the forecasting performance of future realized volatility still holds when intraday data are analyzed. Design/methodology/approach - Two implied volatilities and a realized volatility on KOSPI200 index options are estimated every hour. The grander causality tests between an implied volatility and a realized volatility is carried out for checking the forecasting performance. A dummy variable is added to the grander causality test to examine the change of the forecasting performance when a specific environment is chosen. A trading simulation is conducted to check the economic value of the forecasting performance. Findings - Contrary to the previous studies, the implied volatility from a stochastic volatility model is not superior to that from the Black and Scholes model for the intraday volatility forecasting even if both implied volatilities are informative on one hour ahead future volatility. The forecasting performances of both implied volatilities are improved under high volatile market or low return market. Practical implications - The trading strategy using the forecasting power of an implied volatility earns positively, in particular, more positively under high volatile market or low return market. However, it looks risky to follow the trading strategy because the performance is too volatile. Between two implied volatilities, it is hardly to say that one implied volatility beats another in terms of the economic value. Originality/value - This is the first study which shows the forecasting performances of implied volatilities on the intraday future volatility.

Valuation of Arithmetic Average Reset Options

Options whose payoffs are based on an arithmetic average of the price of the underlying can have some useful advantages over standard options. The payoff is less susceptible to manipulation of the price at expiration, and in many cases a hedger is more interested in locking in the average value of some cost or price over a period of time than in fixing it as of a specific date. For reset options, whose strike prices may be reset at fixed dates depending on the value of the underlying, arithmetic averaging substantially reduces the problem that the options delta can jump on the reset date. However, arithmetic averaging of lognormally distributed random variables produces a substantial problem for valuation, because the underlying average is not lognormal. In this article, Kim, Chang, and Byun provide an effective valuation methodology for arithmetic average reset options.

Implied risk aversion and volatility risk premiums

Since investor risk aversion determines the premium required for bearing risk, a comparison thereof provides evidence of the different structure of risk premium across markets. This article estimates and compares the degree of risk aversion of three actively traded options markets: the S&P 500, Nikkei 225 and KOSPI 200 options markets. The estimated risk aversions is found to follow S&P 500, Nikkei 225 and KOSPI 200 options in descending order, implying that S&P 500 investors require more compensation than other investors for bearing the same risk. To prove this empirically, we examine the effect of risk aversion on volatility risk premium, using delta-hedged gains. Since more risk-averse investors are willing to pay higher premiums for bearing volatility risk, greater risk averseness can result in a severe negative volatility risk premium, which is usually understood as hedging demands against the underlying assets downward movement. Our findings support the argument that S&P 500 investors with higher risk aversion pay more premiums for hedging volatility risk.

VALUING AND HEDGING AMERICAN OPTIONS UNDER TIME-VARYING VOLATILITY

There has been considerable interest in developing stochastic volatility and jump-diffusion option pricing models, e.g. Hull and White (1987, Journal of Finance, 42, 281–300) and Merton (1976, Journal of Financial Economics, 3, 125–144). These models, however, have some undesirable aspects that arise from introducing some non-traded sources of risks to the models. Furthermore, the models require much analytical complications; thus, if they are applied to American options then it is not easy to acquire practical implications for hedging and optimal exercise strategies. This paper examines the American option prices and optimal exercise strategies where the volatility of the underlying asset changes over time in a deterministic way. The paper considers two simple cases: monotonically increasing and decreasing volatilities. The discussion of these two simple cases gives useful implications for the possibility of early-exercise and optimal exercise strategies.

Ad Hoc Black and Scholes Procedures with the Time-to-Maturity

There are two ad hoc approaches to Black and Scholes model. The “relative smile” approach treats the implied volatility skew as a fixed function of moneyness, whereas the “absolute smile” approach treats it as a function of the strike price. Previous studies reveal that the “absolute smile” approach is superior to the “relative smile” approach as well as to other sophisticated models for pricing options. We find that the time-to-maturity factors improve the pricing and hedging performance of the ad hoc procedures and the superiority of the “absolute smile” approach still holds even after the time-to-maturity is considered.

Momentum Crashes and Investors’ Anchoring Bias

We explain why momentum strategy crashes. When the market rebounds, demand on stocks far from peaks motivated from anchoring bias increases as speculators flow into the market, which results in their price run-up. Momentum crashes are just a manifestation of such phenomenon. Consistent with our hypothesis, during the market rebound, stocks far from peaks outperform stocks near peaks and momentum measure loses its negative predictive power once the nearness to price peaks is taken into account. Furthermore, we find that a revised momentum strategy that is neutral on nearness to 52-week high is free of crashes without sacrificing its profitability.

The Disagreement with Herding, Market Bubble, and Excess Volatility

We construct a general equilibrium “disagreement with herding” model to identify the joint effect of the disagreement and herding among investors on the price bubble and excess return volatility. There are two classes of analysts one of which can capture the information in the public signal. An another analyst, on the other hand, do not have an enough ability to refine the public signal to exploit the information and therefore herd. i.e. tend to revise his opinion by moving toward the other’s opinion. As a consequence of the combinational dynamics of the disagreement and herding, the price bubble and the excess volatility is exaggerated especially when they are both huge.

Properties of the Integral Equation Arising in the Valuation of American Options

Unlike European options, American options can be exercised at any time before the expiration date. This fact makes it difficult to analyze the price and the optimal exercise boundary of an American option. The optimal exercise boundary of an American option is implicitly defined by a nonlinear integral equation. This article studies the properties of the integral equation arising in the valuation of American options. Based on the properties of the integral equation, this article also presents a simple upper bound for the optimal exercise boundary of the American put.