CHAPTER IV THE VOLATILITY STRUCTURE IMPLIED BY NIFTY INDEX AND SELECTED STOCK OPTIONS
|
|
- Griffin White
- 5 years ago
- Views:
Transcription
1 CHAPTER IV THE VOLATILITY STRUCTURE IMPLIED BY NIFTY INDEX AND SELECTED STOCK OPTIONS 4.1 INTRODUCTION The Smile Effect is a result of an empirical observation of the options implied volatility with same expiration date, across different exercise prices. It typically describes a U-shape from showing higher implied volatility for in-the-money and out-of-the-money options than its corresponding implied volatility figures for at-the-money options. The Black-Scholas (1975) option pricing model is widely used to value options written on a large variety of underlying assets. Despite this widespread acceptance among practitioners and academics, the discrepancies between market and Black-Scholes prices are obvious and systematic. If the market were to price options according to the Black-Scholas model, all options on one stock would have the same implied volatility. However, it is well-known that, at any moment of time implied volatility obtained by the Black-Scholes model varies across time to maturity as well as strike prices. The pattern of implied volatility for different time to maturity is known as the term structure of implied volatility, and the pattern across strike prices is known as the volatility smile or the volatility sneer. A term of volatility structure is used generally to refer to the pattern across both strike prices and time to maturity. ~ Chapter 4 ~ 88
2 Many early studies have documented a U-shape smile pattern for implied volatility in many options markets prior to the 1987 stock market crash. For example, Macbeth and Mervilli (1979) found in-the-money stock option with a short remaining time to expiration tend to have higher implied volatilities than corresponding options with a longer time to expiration. Rubinstein (1985) concluded that systematic deviation from the Black-Scholas model appears to exist, but the pattern of deviations varies over time. Shastri and Wethyavivorn (1987) found that implied volatilities from foreign currency options traded on the Philadelphia Stock Exchange (PHLX) in 1983 and 1984 are a U-shaped function of the exchange rate divided by the strike price. Sheikh (1991) argued that a U-shaped pattern occurred for the S&P 100 options during various sub-periods between 1983 and Fung and Hsieh (1991) discuss informally some empirical smile effect for foreign currency options traded on the Chicago Mercantile Exchange (CME). Heyen (1993) has shown that U- Shaped functions can describe the pattern of implied volatility from nine months transaction prices of European Options Exchange (EOE) stock index options, which are European style options on an index of 25 active stocks on the Amsterdam stock Exchange. Taylor and Xu (1994) fit U-shaped functions to implied volatilities from foreign currency options traded on the PHLX over a longer period from November 1984 to January They also found that the magnitude of smile effect is a decreasing function of time to maturity. Duque and Paxson (1994) provided evidence of smile pattern in equity call options on ~ Chapter 4 ~ 89
3 LIFFE and relatively high implied volatility for in-the-money options. Bakshi, et al. (1997) exhibit a clear U-shaped pattern across moneyness, with the most distinguished smile evident for options near expiration. Brown and Taylor (1997) used Asay Model on the SPI futures option and found that the model tends to overprice call options and under-price put options. Pena, et al. (1999) found smile pattern in Spanish IBEX-35 index. In contrast to the above theoretical and empirical results showing a symmetric pattern for implied volatility against strike price, Rubinstein (1994) develops a new method for inferring risk-neutral probabilities from the simultaneously observed prices of S&P 500 index option for the post-crash of 1987 and found smile pattern in the implied volatilities during pre-crash changed into a sneer in the post-crash period. Dumas, Fleming and whaley (1998) also illustrate that the volatility structure for S&P 500 index options has changed from the symmetric smile pattern to more a sneer since the stock market crash of In other terms, implied call (put) volatilities decrease monotonically as the call (put) option goes deeper out-of-the-money (in-the-money). Brown (1999) extends Dumas, Fleming and Whaley s (1998) results to the SPI 200 futures options on the Sydney Futures Exchange (SFE), covering the period from June 1993 to June In-the-money call (put) options are generally trading at higher (lower) implied volatilities than out-of-the-money call (put) options. Duque and Lopes (2003) empirical support for the notion is ~ Chapter 4 ~ 90
4 that options tend to die smiling in call option quotes on nine heavily traded stock on the London International Financial Futures and Options Exchange (LIFFE) between August 1990 and December However, the changes in the smile pattern are asymmetric. The wry grin found for longer-term options is converted into a reverse grin for options near expiration. For medium term options, the smile is more symmetric. Malin Engstrom (2002) shows U-shaped smile pattern from 27 individual stock options traded in the Stockholm Stock Exchange (StSE) during the period from July 1, 1995 to February 1, Bollen and Whaley (2004) show a sneer pattern for S&P 500 index options and U-shaped smile pattern from 20 individual stock options traded in the Chicago Board Options Exchange (CBOE) over the period from June 1995 to December In contrast, limited research on this subject is available for India due to the nascent nature of its derivatives market. The previous work provides evidence for existence of volatility smile in the Indian options market. Varma (2002) observes mispricing in the Indian index options market and estimates the volatility smile for call & put options and found that is different across option types. Misra. D, et al. (2006) found that deeply in-the- money and deeply out-of-the-money options have higher implied volatility than at the money options, as well as it is higher for far the month option contracts than for near the month option contracts. Malabika, Devanadhen and Srinivasan (2008) found out U-shaped smile pattern for 26 individual stock call options traded in ~ Chapter 4 ~ 91
5 the National Stock Exchange (NSE) covering the period from January to December The implied volatility structure of call option is similar to Duque and Lopes (2003). The Indian studies fail to provide any strong evidence on volatility structure in accordance with time left to maturity, because of small sample size and short study periods, and immaturity of market structure in the initial phase. The study attempts to fill this research gap for the Indian option market literature. The study specifically examines whether asymmetry volatility structure implied by Indian option market is in accordance with time left to maturity. The rest of Chapter 4 is organized as follows. Section 4.2 provides the data and methodology applied in chapter 4. Section 4.3 presents the two dimensional pattern of implied volatility structure against moneyness. Section 4.4 presents three dimensional pattern of implied volatility structure against moneyness and time left to maturity. Section 4.5 provides summary and the conclusion for chapter 4. ~ Chapter 4 ~ 92
6 4.2 DATA AND METHODOLOGY Data Data description This empirical analysis focuses on S&P CNX Nifty option and selected five stock options for the period from January 1, 2002 to June 30, S&P CNX Nifty option is European style and expires on the last Thursday of the expiry month or the preceding trading day, if the last Thursday is a trading holiday. Individual stock options are American style and expire on the last Thursday of the expiry month or before. The corresponding future contract of underlying Nifty index and selected individual stocks were taken during the sample period. The future contract has exactly the same contract specification as the S&P CNX Nifty index and selected five individual stock options. Estimating implied volatilities requires estimates of the time to maturity and risk-free interest rate. Time to maturity is measured as the number of calendar days between the trade date and the expiration date. Mumbai Inter Bank Offer Rate (MIBOR) was used as proxy for risk-free interest rate. 14 days, one month and three month interest rate were taken and converted into continuous rate. One of these three interest rates will be employed depending upon the time to maturity mentioned below: ~ Chapter 4 ~ 93
7 Time to Maturity Interest rate < 30 days 14 days days 1 month > 61 days 3 month Sampling Procedure Nifty option and selected individual stock option constitute 5,94,589 observations for call and put option each. To obtain a relatively accurate measurement of implied volatility, the option must be chosen carefully. Options devoid of informative contents are excluded from the sample based on the following criteria: 1. Option whose time to maturity is lower than 7 days and higher than 90 days. The shorter term options have relatively small time premium, hence the estimation of volatility is extremely sensitive to any possible measurement errors, particularly if the option is not at-the-money. Hentschel (2000) shows other liquidity biases arising due to the rolling over attitude of fund managers. The long term options, on the other hand, are simply not traded. (similar exclusionary criteria are applied among other by Bakshi, et al. (1997) or by Dumas, et al. (1998)). 2. Options which are not traded ~ Chapter 4 ~ 94
8 3. The option price violates the arbitrage condition i.e. the price of the call or put is smaller than Black-Scholas-Merton price in the limit of zero volatility. Options dropped from the sample because they meet at least one of these conditions amount to 1,70,812 for call and 82,949 for put. Only 18.13% of call sample and 13.95% of put sample were taken for the study. Table 4.1 reports the number and percentage of the quotation cleared off the database according to each exclusion criterion. ~ Chapter 4 ~ 95
9 TABLE 4.1 Number of Observations and Percentage of the Contracts Filtered This table presents the number of observations of S&P CNX Nifty index and five selected stock options over the period from January 2002 to June The individual as well as total number of observations and percentage of the quotations cleared off the database according to following criterion. (1) Option whose time to Maturity (TTM) is lower than 7 days and higher than 90 days. (2) Options which are not traded. (3) Option price violates the arbitrage condition. Panel A contains number of observations and percentage of the contracts filtered of the call option sample according to each exclusion criterion. Panel B contains number of observations and percentage of the contracts filtered of the put option sample according to each exclusion criterion. Panel A: Call Option Particular S&P CNX NIFTY INFOSYS ITC RANBAXY RELIANCE SBI Total % Total TTM < 7 days TTM > 90 days No option traded Violation of Arbitrage Final sample Panel B: Put Option Particular S&P CNX NIFTY INFOSYS ITC RANBAXY RELIANCE SBI Total % Total TTM < 7 days TTM > 90 days No option traded Violation of Arbitrage Final sample ~ Chapter 4 ~ 96
10 4.2.2 Methodology Implied volatility is the volatility implied by an option price observed in the market based on an option pricing model. Thus, this study is dependent on option pricing model. In this subsection, the original Black-Scholas model (BS) and its limitations are briefly discussed. Then a dividend-adjusted BS model is presented in detail and used as the option pricing model in this study The Option Pricing Model Black-Scholes (1973) Model The assumptions underlying the original Black-Scholas option pricing model are as follows: 1. The stock price follows a lognormal distribution with a constant volatility. 2. The short selling of securities with full use of proceeds is permitted. 3. There are no transactions cost or taxes. All securities are perfectly divisible. 4. There are no dividends during the life of the derivative. 5. There are no riskless arbitrage opportunities. 6. Securities trading are continuous. 7. The risk-free rate of interest is constant and is the same for all maturities. Based on the assumption above, the Black-Scholas option pricing formulae for call and Put option can be written as ~ Chapter 4 ~ 97
11 (4-1) and (4-2) Respectively, where Where C = the call option price P = the put option price S = the current stock price X = the exercise price of the option T = the time remaining until expiration of the option r = the continuously compounded annualized risk-free rate of interest for the period of T = the annualized volatility of the stock return N ( ) = the cumulative normal density function of ( ) One of the assumptions employed by Black-Scholas (1973) is that the stock pays no dividend (Assumption 4). However, dividends on some stock may be substantial and can have a significant effect on the valuation of option. Therefore a dividend adjustment must be allowed in the option pricing formulae. ~ Chapter 4 ~ 98
12 Merton (1973) Model Merton model generalizes the Black and Scholas (1973) model by relaxing the assumption of no dividend. Merton (1973) allows for a constant continuous dividend yield on the stock and/or stock index. Let denote the volatility implied by an option price and denote the annualized continuously compounded dividend yield during the remaining life of the option, then the Merton (1973) formulae for call and put options can be expressed as (4-3) (4-4) respectively, where Hence, an estimate of the dividend yield is needed. For the purpose of this study, future contracts are utilized, which have exactly the same expiry cycle, expiry date and underlying asset with the option contract. The cost-of-carry model (see, e.g. Hull, 2003) gives ~ Chapter 4 ~ 99
13 (4-5) Combining (2.5) with (2.3) and (2.4) yields and (4-6) (4-7) where, By solving (4-6) and (4-7) numerically, the implied volatility series for call and put options can be constructed. Inverting an option model like that of Black-Scholas is a difficult task, therefore generally implied volatility is extracted from the option prices by using some numerical methods viz., Newton-Rapshson Method; by equating the difference between observed price of the option and the theoretical price of the option to zero ~ Chapter 4 ~ 100
14 and solving for volatility. (See Appendix for Newton-Raphson algorithm implementation in Visual basic) Then two dimensional graphs are plotted to examine the effect of volatility smile or sneer, i.e. how implied volatilities vary across different group of moneyness in the data set. Within the each group moneyness, the average implied volatility is calculated. It is necessary to mention that there is an implicit assumption when taking average in each group, as suggested by Brown (1999), that the trends in implied volatility will influence implied volatilities from options with different strike prices and different maturities equally. Furthermore, to examine how volatility smile or sneer is affected by time to maturity, three dimensional graphs of implied volatility against moneyness and maturity of the option are plotted. This is done by assigning all the options records under investigation into groups according to moneyness and time to maturity, and then taking an average for implied volatilities in each group Moneyness The term Moneyness is defined by various authors in different models. Models prescribed by various authors in literature are given in Table 4.2. ~ Chapter 4 ~ 101
15 TABLE 4.2 Models for Computing Moneyness Authors Moneyness (Mn) formula Gross & Waltners (1995) Malin Engstorm (2001) Natenbery (1994), Dumas (1998) & Tompkin (1999) Jackwerth & Rubinstein (1996) F K log Mn = T X Mn = ln S log Mn = T Mn = X F X F Bollen & Whaley (2002) rt ( S PVD) e Mn = 1 X Brown (1999) F Mn = 1 K S ΣNPV ( Div) Duque & Lopes (2003) Mn = r ( T t) Xe Alessandro Beber (2001) X log F Mn = δ T atm,t X Dumas, Fleming & Whaley (1996) Mn = 1 F ~ Chapter 4 ~ 102
16 Moneyness is defined as the ratio of the exercise price to the future price. The options are divided into five subgroups according to Moneyness. The upper and lower bounds of the moneyness categories are listed in Table 4.3. Options are classified as follows: Deep in-the-money (Deep ITM) A Deep in-the-money (Deep ITM) option is an option that would lead to a high positive cash flow to the holder if it were exercised. An option on the index or individual stock option is said to be Deep in-the-money when Moneyness [Exercise price (X)/Future price (F)] is less than or equal to 0.90 i.e If the moneyness is less than or equal to.90, the call is said to be Deep ITM. In the case of a put, the put is Deep OTM (Deep out-of-the-money) In-the-money (ITM) An In-the-money (ITM) option is an option that would lead to a positive cash flow to the holder if it were exercised. An option on the index or individual stock option is said to be In-the-money when moneyness [Exercise price (X)/Future price (F)] is between 0.90 and 0.98 ( ). If the moneyness is between 0.90 and 0.98, the call is said to be ITM. In the case of a put, the put is OTM (Out-of-themoney). ~ Chapter 4 ~ 103
17 At-the-money (ATM) At-the-money (ATM) option is an option that has zero cash flow at the time of exercising. An option on the index or individual stock option is said to be at-themoney when moneyness [Exercise price (X)/Future price (F)] is between 0.98 and 1.02 ( ). If the moneyness is between 0.98 and 1.02, the call and put is said to be ATM Out-of-the-money (OTM) Out-of-the-money (OTM) option is an option that would lead to a negative cash flow if it was exercised. An option on the individual stock option is said to be out-of-the-money when Moneyness [Exercise price (X)/Future price (F)] is between 1.02 and 1.10 ( ). If the moneyness is between 1.02 and 1.10, the call is said to be OTM. In the case of a put, the put is ITM (In-the-money) Deep out-the-money (Deep OTM) Deep out-the-money (Deep OTM) option is an option that would lead to a high negative cash flow when it was exercised. An option on the individual stock option is said to be Deep out-of-the-money when moneyness [Exercise price (X)/Future price (F)] is greater than If the moneyness is greater than 1.10, the call is said to be Deep OTM. In the case of a put, the put is Deep ITM (Very Deep In-the-money). ~ Chapter 4 ~ 104
18 ~ Chapter 4 ~ 105
19 TABLE 4.3 Moneyness Category Definitions This table displays category number, labels and Corresponding moneyness (Mn) and ranges of option in the sample Category Label Range Deep in-the-money (DITM) call Deep out-of-the-money (DOTM) put In-the-money (DITM) call Out-ofthe-money (DOTM) put At-the-money (ATM) call At-the-money (ATM) put Out-of-the-money (DOTM) call Inthe-money (DITM) put Deep out-of-the-money (DOTM) call Deep in-the-money (DITM) put Mn < Mn < Mn < Mn 1.10 Mn > 1.10 In particular, the 40,952 index call option and 39,242 index put option contracts in the data set are assigned to five groups according to moneyness and nine groups according to the number of days left to expiration, i.e. 45 groups. Time to maturity is measured as the number of calendar days between the trade date and the expiration date. Similarly, the 66,860 stock call option and 43,707 stock put option contracts are placed into five groups according to moneyness and six groups according to the number of days left to expiration, i.e. 30 groups. Since stock options with time to maturity larger than 50 days are not traded as frequently as other stock options with shorter life. These stock options are assigned to one expiration interval. Within each group the average implied volatility is calculated. It ~ Chapter 4 ~ 106
20 is necessary to mention that there is an implicit assumption when taking average in each group, as suggested by Brown (1999), that the trends in implied volatility will influence implied volatilities from options with different strike prices and different maturities equally. 4.3 PATTERN IN IMPLIED VOLATILITY In Table 4.4, the implied volatility of both index call and put options is average over the moneyness groups. For index call options, the result indicates a clear U-shaped smile pattern, with the lowest average implied volatility found for atthe-money options. In the call option sample, the average implied volatility of the category 1 option (DITM calls) is 52.29%, more than 7% higher than the average implied volatility of category 5 options (DOTM calls), 48.76%. For the put option, the average implied volatility of the category 5 option (DITM puts) is 73%, more than 68% higher than the average implied volatility of category 1 options (DOTM puts), 43.54%. The implied volatility of the ATM options generally being lowest and symmetrically increasing with movement in either direction. For the call options, the average implied volatility of the category 1 options (DITM calls) is 141% higher than average implied volatility of ATM options. On the other end of the spectrum, the average implied volatility of category 5 options (DOTM calls) is 125% higher than the average implied volatility of ATM options. For put option, the average implied volatility of the category 1 options ~ Chapter 4 ~ 107
21 (DOTM puts) is 62% higher than the average implied volatility of ATM option (category 3 options). On the other hand, the average implied volatility of category 5 options (DITM puts) is 172% higher than the average implied volatility of ATM option (category 3 options). Index put option exhibits a skew shaped pattern. While comparing to index call option, higher average implied volatility can be viewed in index put option except DITM options. ~ Chapter 4 ~ 108
22 TABLE 4.4 Average Implied Volatility of Index Option against Moneyness This table displays implied volatilities, option premium, contract and number of observations of S&P CNX Nifty index option over the period from January 2002 to June Implied volatilities as well as premium are average into one of 5 groups of moneyness category. Moneyness category is defined as category 1 options for DITM call & DOTM put, category 2 options for ITM call & OTM put, category 3 options for ATM call & put, category 4 options for OTM call & ITM put and category 5 options for DOTM call & DITM put. Category Call Implied Volatility Option premium Contract No. of Observation Put Implied Volatility Option premium Contract No. of Observation ~ Chapter 4 ~ 109
23 CHART 4.1 Graphs of Index Option Average Implied Volatility against Moneyness This figure illustrates the volatility structure implied by the S&P CNX Nifty index option over the period from January 2002 to June The 40,952 call option and 39,242 put option contracts in the data set are assigned to one of 5 groups of moneyness category. Average implied volatility is calculated for each group. Moneyness category is defined as category 1 options for DITM call & DOTM put, category 2 options for ITM call & OTM put, category 3 options for ATM call & put, category 4 options for OTM call & ITM put and category 5 options for DOTM call & DITM put Implied volatility Call Put Moneyness ~ Chapter 4 ~ 110
24 Table 4.5 contains the average implied volatilities of 5 individual stocks in the sample. As the results in the table show, the implied volatilities of stock call and put option exhibit U-shape and skew shape respectively. In stock call option, the average implied volatility of the category 1 options (DITM calls) is 64.54%, more than 15% higher than the average implied volatility of category 5 options (DOTM calls), 56.20%. For the stock put option, the average implied volatility of the category 5 options (DITM puts) is 86.31%, more than 68% higher than the average implied volatility of category 1 options (DOTM puts), 51.53%. For stock call options, the average implied volatility of the category 1 options (DITM calls) is 93% higher than the average implied volatility of ATM options (category 3 options). On the other end, the average implied volatility of category 5 options is 68% higher than the implied volatility of ATM options (category 3 options). In case of stock put options, the average implied volatility of category 1 options (DOTM puts) is 41% higher than the average implied volatility of the ATM options (category 3 options). On the other side, the average implied volatility of category 5 options (DITM puts) is 135% higher than the average implied volatility of ATM options (category 3 options). ~ Chapter 4 ~ 111
25 TABLE 4.5 Average Implied Volatility of Stock Option against Moneyness This table displays implied volatilities, option premium, contract and number of observations of five individual stock options over the period from January 2002 to June Implied volatilities as well as premium are average into one of 5 groups of moneyness category. Moneyness category is defined as category 1 options for DITM call & DOTM put, category 2 options for ITM call & OTM put, category 3 options for ATM call & put, category 4 options for OTM call & ITM put and category 5 options for DOTM call & DITM put. Category Call Implied Volatility Option premium Contract No. of Observation Put Implied Volatility Option premium Contract No. of Observation ~ Chapter 4 ~ 112
26 CHART 4.2 Graphs of Stock Option Average Implied Volatility against Moneyness This figure illustrates the volatility structure implied by the five selected individual stock options over the period from January 2002 to June The 66,860 stock call option and 43,707 stock put option contracts in the data set are assigned to one of 5 groups of moneyness category. Average implied volatility is calculated for each group. Moneyness category is defined as category 1 options for DITM call & DOTM put, category 2 options for ITM call & OTM put, category 3 options for ATM call & put, category 4 options for OTM call & ITM put and category 5 options for DOTM call & DITM put Implied volatility Call Put Moneyness ~ Chapter 4 ~ 113
27 4.4 THREE DIMENSIONAL VIEW OF VOLATILITY STRUCTURE To investigate the volatility structure further, three dimensional graphs of implied volatilities against moneyness and time to maturity of the option are plotted in Chart 4.3 and Chart 4.4 for index call and put options, respectively. This is done by assigning all the options records under investigation to groups according to moneyness and time to maturity, and then taking an average for implied volatilities in each group. Tables 4.6 and 4.7 report the average implied volatility within each group for index call and put options respectively. The average implied volatilities differ for the different time to maturity groups. For index call options, the longer the time left to maturity, the lower the implied volatility tends to be each moneyness group. In addition, the shorter the time left to maturity, the more pronounced the U-shape of the smile appears to be. This pattern is something referred to as the options are dying smiling. When time to maturity is greater than 30 days and less than 80 days, it is expected deep out-of-the-money call options on average trade at higher implied volatilities than deep in-the-money call options. But for short time period, implied volatilities of deep in-the-money are higher than deep out-of-the-money implied volatilities. This finding is similar to Duque & Lopes (2003) that the change in smile shape as time to maturity dies out. This may be seen as in support of the expected wry grin for long term options. But the reverse grin is deeply unexpected for short-term options. In fact, the maturity approach changes the options smile asymmetry, converting a wry grin typical for longer term series into a reverse ~ Chapter 4 ~ 114
28 grin for nearly expiring options, with a more or less symmetric smile in a three dimensional domain. For the index put options, the shorter the time left to maturity, the more pronounced U-shape of the smile appear to be. The deep in-the-money options on average trade are higher implied volatilities than deep out-of-the-money options. Longer the time left to maturity, the lower the implied volatilities tend to be in deep in-the-money put options and more or less constant implied volatilities tend to be in deep out-of-the-money. ~ Chapter 4 ~ 115
29 TABLE 4.6 Group Average of Index Call Option Implied Volatilities with Respect of Moneyness and Time to Maturity The table presents implied volatilities of S&P CNX Nifty index call option over the period from January 2002 to June The 40,952 index call option contracts in the data set are assigned to five groups according to moneyness and nine groups according to the number of days left to expiration, i.e. 45 groups. Average implied volatility is calculated for each group. Time to maturity is measured as the number of calendar days between the trade date and the expiration date. In parentheses the number of observations is given according to the moneyness and time to maturity of call options. Moneyness category is defined as category 1 options for DITM call & DOTM put, category 2 options for ITM call & OTM put, category 3 options for ATM call & put, category 4 options for OTM call & ITM put and category 5 options for DOTM call & DITM put. Moneyness Category Total Time to Maturity (days) Total (230) (323) (301) (146) (148) (50) (10) (5) (4) (1217) (1422) (2255) (2707) (1299) (1030) (608) (294) (180) (89) (9884) (1912) (2871) (3432) (1864) (1526) (861) (515) (461) (262) (13704) (1511) (2010) (2431) (1537) (1229) (584) (482) (402) (175) (10361) (898) (1068) (1268) (879) (669) (417) (299) (175) (113) (5786) (5973) (8527) (10139) (5725) (4602) (2520) (1600) (1223) (643) (40952) ~ Chapter 4 ~ 116
30 CHART D view of Volatility Structure for Index Call Options This figure illustrates a 3-D view of the volatility structure implied by the S&P CNX Nifty index call option over the period from January 2002 to June The 40,952 index call option contracts in the data set are assigned to one of 45 groups by moneyness and time to maturity. Average implied volatility is calculated for each group. Time to maturity is measured as the number of calendar days between the trade date and the expiration date. Moneyness category is defined as category 1 options for DITM call & DOTM put, category 2 options for ITM call & OTM put, category 3 options for ATM call & put, category 4 options for OTM call & ITM put and category 5 options for DOTM call & DITM put. 0.8 Implied volatility Moneyness Time to Maturity ~ Chapter 4 ~ 117
31 TABLE 4.7 Group Average of Index Put Option Implied Volatilities with Respect of Moneyness and Time to Maturity This table presents implied volatilities of S&P CNX Nifty index put option over the period from January 2002 to June2010. The 39,242 put option contracts in the data set are assigned to five groups according to moneyness and nine groups according to the number of days left to expiration, i.e. 45 groups. Average implied volatility is calculated for each group. Time to maturity is measured as the number of calendar days between the trade date and the expiration date. In parentheses the number of observations is given according to the moneyness and time to maturity of call options. Moneyness category is defined as category 1 options for DITM call & DOTM put, category 2 options for ITM call & OTM put, category 3 options for ATM call & put, category 4 options for OTM call & ITM put and category 5 options for DOTM call & DITM put. Moneyness Category Total Time to Maturity (days) Total (802) (1129) (1414) (654) (493) (263) (111) (54) (25) (4945) (2236) (3503) (4198) (1981) (1646) (1064) (610) (433) (235) (15906) (1644) (2438) (2886) (1450) (1125) (598) (344) (294) (165) (10944) (797) (1111) (1241) (776) (85) (247) (150) (97) (54) (5058) (344) (397) (554) (373) (256) (181) (132) (90) (62) (2389) (5823) (8578) (10293) (5234) (4105) (2353) (1347) (968) (541) (39242) ~ Chapter 4 ~ 118
32 CHART D View of Volatility Structure for Index Put Options This figure illustrates a 3-D view of the volatility structure implied by the S&P CNX Nifty index call option over the period from January 2002 to June The 40,952 index call option contracts in the data set are assigned to one of 45 groups by moneyness and time to maturity. Average implied volatility is calculated for each group. Time to maturity is measured as the number of calendar days between the trade date and the expiration date. Moneyness category is defined as category 1 options for DITM call & DOTM put, category 2 options for ITM call & OTM put, category 3 options for ATM call & put, category 4 options for OTM call & ITM put and category 5 options for DOTM call & DITM put Implied volatility Moneyness Time to Maturity ~ Chapter 4 ~ 119
33 In Table 4.8, the implied volatilities for five selected stock call options are averaged over the five groups of moneyness and six groups of time to maturity i.e. 30 groups. Since stock options with time to maturity larger than 50 days are not traded as frequently as other stock options with shorter life. These stock options are assigned into one maturity interval. Within each group the average implied volatility is calculated. The results indicate a clear U-shape smile pattern, with lowest average implied volatility found for the at-the-money in all the groups. When time to maturity is greater than 30 days, it is expected deep out-of-the-money stock call options on average trade at higher implied volatilities than deep in-the-money stock call options. But for short time period, implied volatilities of deep in-the-money are higher than deep out-of-the-money implied volatilities. The volatility term structure of stock call option is similar to that of index call option. That is, the term structure is similar to Duque & Lopes (2003) that the change in smile shape as time to maturity dies out. This may be seen as in support of the expected wry grin for long term options. But the reverse grin is deeply unexpected for short-term options. In fact, the maturity approach changes the options smile asymmetry, converting a wry grin typical for longer term series into a reverse grin for nearly expiring options, with a more or less symmetric smile in a three- dimensional domain (Chart 4.5). For the stock put option, the Table 4.9 and Chart 5.6 show, the shorter the time left to maturity, the more pronounced U-shape of the smile appears to be. The deep in-the-money options on average trade are higher implied volatilities than deep out-of-the-money options. Longer the time left to maturity, the lower the implied ~ Chapter 4 ~ 120
34 volatilities tend to be in deep in-the-money stock put options and more or less constant implied volatilities tend to be in deep out-of-the-money. The volatility term structure of stock put option is similar to that of index put option. That is, the term structure is close to be flat for at-the-money stock put options, and tends to be fluctuant for deep in-the-money option. ~ Chapter 4 ~ 121
35 TABLE 4.8 Group Average of Stock Call Option Implied Volatilities with Respect of Moneyness and Time to Maturity This table presents implied volatilities of five selected stock call option over the period from January 2002 to June The 66,860 stock call option contracts in the data set are assigned into five groups according to moneyness and six groups according to the number of days left to expiration, i.e. 30 groups. Average implied volatility is calculated for each group. Time to maturity is measured as the number of calendar days between the trade date and the expiration date. In parentheses the number of observations is given according to the moneyness and time to maturity of stock call options. Moneyness category is defined as category 1 options for DITM call & DOTM put, category 2 options for ITM call & OTM put, category 3 options for ATM call & put, category 4 options for OTM call & ITM put and category 5 options for DOTM call & DITM put. Moneyness Category Total Time to Maturity (days) Total (536) (682) (626) (113) (39) (8) (2004) (2783) (4542) (5202) (1847) (665) (192) (15231) (2610) (4002) (5139) (2701) (1278) (282) (16012) (3921) (5952) (7475) (3722) (1630) (333) (23033) (2328) (2922) (3244) (1393) (531) (162) (10580) (12178) (18100) (21686) (9776) (4143) (977) (66860) ~ Chapter 4 ~ 122
36 CHART D View of Volatility Structure for Stock Call Options This figure illustrates a 3-D view of the volatility structure implied by the five selected stock call options over the period from January 2002 to June The 66,860 stock call option contracts in the data set are assigned to one of 30 groups by moneyness and time to maturity. Average implied volatility is calculated for each group. Time to maturity is measured as the number of calendar days between the trade date and the expiration date. Moneyness category is defined as category 1 options for DITM call & DOTM put, category 2 options for ITM call & OTM put, category 3 options for ATM call & put, category 4 options for OTM call & ITM put and category 5 options for DOTM call & DITM put Implied volatility Moneyness Time to Maturity ~ Chapter 4 ~ 123
37 TABLE 4.9 Group Average of Stock Put Option Implied Volatilities with Respect of Moneyness and Time to Maturity This table presents implied volatilities of five selected stock put option over the period from January 2002 to June The 43,707 stock put option contracts in the data set are assigned to five groups according to moneyness and six groups according to the number of days left to expiration, i.e. 30 groups. Average implied volatility is calculated for each group. Time to maturity is measured as the number of calendar days between the trade date and the expiration date. In parentheses the number of observations is given according to the moneyness and time to maturity of stock put options. Moneyness category is defined as category 1 options for DITM call & DOTM put, category 2 options for ITM call & OTM put, category 3 options for ATM call & put, category 4 options for OTM call & ITM put and category 5 options for DOTM call & DITM put. Moneyness category Total Time to Maturity (days) Total (1471) (2214) (2538) (667) (152) (38) (7080) (3771) (5931) (7173) (2529) (735) (88) (20227) (2120) (3097) (3588) (1302) (346) (30) (10483) (1331) (1684) (1522) (413) (81) (8) (5039) (277) (292) (223) (68) (13) (5) (878) (8970) (13218) (15044) (4979) (1327) (169) (43707) ~ Chapter 4 ~ 124
38 CHART D View of Volatility Structure for Stock Put Options This figure illustrates a 3-D view of the volatility structure implied by the five selected stock put options over the period from January 2002 to June The 43,707 stock put option contracts in the data set are assigned to one of 30 groups by moneyness and time to maturity. Average implied volatility is calculated for each group. Time to maturity is measured as the number of calendar days between the trade date and the expiration date. Moneyness category is defined as category 1 options for DITM call & DOTM put, category 2 options for ITM call & OTM put, category 3 options for ATM call & put, category 4 options for OTM call & ITM put and category 5 options for DOTM call & DITM put Implied volatility Moneyness Time to Maturity ~ Chapter 4 ~ 125
39 4.5 CONCLUSION In this chapter, the volatility structure implied by S&P CNX Nifty option and selected five individual stock options are illustrated by two dimensional graphs of implied volatility against moneyness and three dimensional graphs of implied volatility against both moneyness and time to maturity. It is found that the U pattern is more pronounced for the call and put options implied volatilities. For both call and put option, deep in-the-money is higher than deep out-of-the-money implied volatilities. In accordance with time left to maturity, the implied volatility of deep inthe-money call options is higher than deep out-of-the-money for shorter time left to maturity. Implied volatility of deep out-of-the money call options is higher than deep in-the-money for longer time left to maturity. In case of put options, the shorter the time left to maturity, deep in-the-money options are higher than deep out-of-the-money. Longer the time left to maturity, the deep in-the-money options implied volatilities tend to decrease monotonically. The term structure of implied volatility is nearly flat of at-the-money call and put options, both more fluctuant for deep in-the-money and deep out-of-the-money options. ~ Chapter 4 ~ 126
40 In general, these results indicate that the Black-Scholes-Merton model applied in this study tends to overprice in-the-money call options and out-of-themoney put options and underprice in-the-money put option and out-of-the-money call options. At-the-money options are often most actively traded and hence they are less likely to be mispriced. ~ Chapter 4 ~ 127
Factors in Implied Volatility Skew in Corn Futures Options
1 Factors in Implied Volatility Skew in Corn Futures Options Weiyu Guo* University of Nebraska Omaha 6001 Dodge Street, Omaha, NE 68182 Phone 402-554-2655 Email: wguo@unomaha.edu and Tie Su University
More informationBeyond Black-Scholes: The Stochastic Volatility Option Pricing Model and Empirical Evidence from Thailand. Woraphon Wattanatorn 1
1 Beyond Black-Scholes: The Stochastic Volatility Option Pricing Model and Empirical Evidence from Thailand Woraphon Wattanatorn 1 Abstract This study compares the performance of two option pricing models,
More informationImplied Adjusted Volatility by Leland Option Pricing Models: Evidence from Australian Index Options
Implied Adjusted Volatility by Leland Option Pricing Models: Evidence from Australian Index Options Mimi Hafizah Abdullah, Hanani Farhah Harun, Nik Ruzni Nik Idris Abstract With the implied volatility
More informationNOTES ON THE BANK OF ENGLAND OPTION IMPLIED PROBABILITY DENSITY FUNCTIONS
1 NOTES ON THE BANK OF ENGLAND OPTION IMPLIED PROBABILITY DENSITY FUNCTIONS Options are contracts used to insure against or speculate/take a view on uncertainty about the future prices of a wide range
More informationSmiles, Bid-ask Spreads and Option Pricing
Smiles, Bid-ask Spreads and Option Pricing Ignacio PenÄ a Universidad Carlos III de Madrid, Spain e-mail: ypenya@eco.uc3m.es Gonzalo Rubio Universidad del PaÿÂs Vasco, Bilbao, Spain e-mail: jepruirg@bs.ehu.es
More informationImplied Volatility Surface
Implied Volatility Surface Liuren Wu Zicklin School of Business, Baruch College Options Markets (Hull chapter: 16) Liuren Wu Implied Volatility Surface Options Markets 1 / 1 Implied volatility Recall the
More informationLecture Quantitative Finance Spring Term 2015
implied Lecture Quantitative Finance Spring Term 2015 : May 7, 2015 1 / 28 implied 1 implied 2 / 28 Motivation and setup implied the goal of this chapter is to treat the implied which requires an algorithm
More informationThe Performance of Smile-Implied Delta Hedging
The Institute have the financial support of l Autorité des marchés financiers and the Ministère des Finances du Québec Technical note TN 17-01 The Performance of Delta Hedging January 2017 This technical
More informationHedging the Smirk. David S. Bates. University of Iowa and the National Bureau of Economic Research. October 31, 2005
Hedging the Smirk David S. Bates University of Iowa and the National Bureau of Economic Research October 31, 2005 Associate Professor of Finance Department of Finance Henry B. Tippie College of Business
More information1. What is Implied Volatility?
Numerical Methods FEQA MSc Lectures, Spring Term 2 Data Modelling Module Lecture 2 Implied Volatility Professor Carol Alexander Spring Term 2 1 1. What is Implied Volatility? Implied volatility is: the
More information& 26 AB>ก F G& 205? A?>H<I ก$J
ก ก ก 21! 2556 ก$%& Beyond Black-Scholes : The Heston Stochastic Volatility Option Pricing Model :;$?AK< \K & 26 AB>ก 2556 15.00 F 16.30. G& 205? A?>HH< LA < Beyond Black-Scholes The Heston Stochastic
More informationAppendix A Financial Calculations
Derivatives Demystified: A Step-by-Step Guide to Forwards, Futures, Swaps and Options, Second Edition By Andrew M. Chisholm 010 John Wiley & Sons, Ltd. Appendix A Financial Calculations TIME VALUE OF MONEY
More information15 Years of the Russell 2000 Buy Write
15 Years of the Russell 2000 Buy Write September 15, 2011 Nikunj Kapadia 1 and Edward Szado 2, CFA CISDM gratefully acknowledges research support provided by the Options Industry Council. Research results,
More informationQueens College, CUNY, Department of Computer Science Computational Finance CSCI 365 / 765 Fall 2017 Instructor: Dr. Sateesh Mane.
Queens College, CUNY, Department of Computer Science Computational Finance CSCI 365 / 765 Fall 2017 Instructor: Dr. Sateesh Mane c Sateesh R. Mane 2017 20 Lecture 20 Implied volatility November 30, 2017
More informationImplied Volatility Surface
Implied Volatility Surface Liuren Wu Zicklin School of Business, Baruch College Fall, 2007 Liuren Wu Implied Volatility Surface Option Pricing, Fall, 2007 1 / 22 Implied volatility Recall the BSM formula:
More informationEmpirical Performance of Option Pricing Models: Evidence from India
International Journal of Economics and Finance; Vol. 5, No. ; 013 ISSN 1916-971X E-ISSN 1916-978 Published by Canadian Center of Science and Education Empirical Performance of Option Pricing : Evidence
More informationEdgeworth Binomial Trees
Mark Rubinstein Paul Stephens Professor of Applied Investment Analysis University of California, Berkeley a version published in the Journal of Derivatives (Spring 1998) Abstract This paper develops a
More informationAnalysis of The Efficacy of Black-scholes Model - An Empirical Evidence from Call Options on Nifty-50 Index
Analysis of The Efficacy of Black-scholes Model - An Empirical Evidence from Call Options on Nifty-50 Index Prof. A. Sudhakar Professor Dr. B.R. Ambedkar Open University, Hyderabad CMA Potharla Srikanth
More informationImplied Volatility Structure and Forecasting Efficiency: Evidence from Indian Option Market CHAPTER V FORECASTING EFFICIENCY OF IMPLIED VOLATILITY
CHAPTER V FORECASTING EFFICIENCY OF IMPLIED VOLATILITY 5.1 INTRODUCTION The forecasting efficiency of implied volatility is the contemporary phenomenon in Indian option market. Market expectations are
More informationThe Effect of Net Buying Pressure on Implied Volatility: Empirical Study on Taiwan s Options Market
Vol 2, No. 2, Summer 2010 Page 50~83 The Effect of Net Buying Pressure on Implied Volatility: Empirical Study on Taiwan s Options Market Chang-Wen Duan a, Ken Hung b a. Department of Banking and Finance,
More informationHEDGING AND ARBITRAGE WARRANTS UNDER SMILE EFFECTS: ANALYSIS AND EVIDENCE
HEDGING AND ARBITRAGE WARRANTS UNDER SMILE EFFECTS: ANALYSIS AND EVIDENCE SON-NAN CHEN Department of Banking, National Cheng Chi University, Taiwan, ROC AN-PIN CHEN and CAMUS CHANG Institute of Information
More informationOULU BUSINESS SCHOOL. Tommi Huhta PERFORMANCE OF THE BLACK-SCHOLES OPTION PRICING MODEL EMPIRICAL EVIDENCE ON S&P 500 CALL OPTIONS IN 2014
OULU BUSINESS SCHOOL Tommi Huhta PERFORMANCE OF THE BLACK-SCHOLES OPTION PRICING MODEL EMPIRICAL EVIDENCE ON S&P 500 CALL OPTIONS IN 2014 Master s Thesis Department of Finance December 2017 UNIVERSITY
More informationPricing of Stock Options using Black-Scholes, Black s and Binomial Option Pricing Models. Felcy R Coelho 1 and Y V Reddy 2
MANAGEMENT TODAY -for a better tomorrow An International Journal of Management Studies home page: www.mgmt2day.griet.ac.in Vol.8, No.1, January-March 2018 Pricing of Stock Options using Black-Scholes,
More informationFORECASTING AMERICAN STOCK OPTION PRICES 1
FORECASTING AMERICAN STOCK OPTION PRICES 1 Sangwoo Heo, University of Southern Indiana Choon-Shan Lai, University of Southern Indiana ABSTRACT This study evaluates the performance of the MacMillan (1986),
More informationIndian Institute of Management Calcutta. Working Paper Series. WPS No. 796 March 2017
Indian Institute of Management Calcutta Working Paper Series WPS No. 796 March 2017 Comparison of Black Scholes and Heston Models for Pricing Index Options Binay Bhushan Chakrabarti Retd. Professor, Indian
More informationMispricing of Volatility in the Indian Index Options Market. Jayanth R. Varma Working Paper No April 2002
Mispricing of Volatility in the Indian Index Options Market Jayanth R. Varma jrvarma@iimahd.ernet.in Working Paper No. 00-04-01 April 00 The main objective of the working paper series of the IIMA is to
More informationThe early exercise premium in American put option prices
Journal of Multinational Financial Management 10 (2000) 461 479 www.elsevier.com/locate/econbase The early exercise premium in American put option prices Malin Engström, Lars Nordén * Department of Corporate
More informationFIN FINANCIAL INSTRUMENTS SPRING 2008
FIN-40008 FINANCIAL INSTRUMENTS SPRING 2008 The Greeks Introduction We have studied how to price an option using the Black-Scholes formula. Now we wish to consider how the option price changes, either
More informationCONSTRUCTING NO-ARBITRAGE VOLATILITY CURVES IN LIQUID AND ILLIQUID COMMODITY MARKETS
CONSTRUCTING NO-ARBITRAGE VOLATILITY CURVES IN LIQUID AND ILLIQUID COMMODITY MARKETS Financial Mathematics Modeling for Graduate Students-Workshop January 6 January 15, 2011 MENTOR: CHRIS PROUTY (Cargill)
More informationThe Black-Scholes Model
The Black-Scholes Model Liuren Wu Options Markets (Hull chapter: 12, 13, 14) Liuren Wu ( c ) The Black-Scholes Model colorhmoptions Markets 1 / 17 The Black-Scholes-Merton (BSM) model Black and Scholes
More informationOption Pricing under NIG Distribution
Option Pricing under NIG Distribution The Empirical Analysis of Nikkei 225 Ken-ichi Kawai Yasuyoshi Tokutsu Koichi Maekawa Graduate School of Social Sciences, Hiroshima University Graduate School of Social
More informationThe Black-Scholes Model
The Black-Scholes Model Liuren Wu Options Markets Liuren Wu ( c ) The Black-Merton-Scholes Model colorhmoptions Markets 1 / 18 The Black-Merton-Scholes-Merton (BMS) model Black and Scholes (1973) and Merton
More informationThe objective of Part One is to provide a knowledge base for learning about the key
PART ONE Key Option Elements The objective of Part One is to provide a knowledge base for learning about the key elements of forex options. This includes a description of plain vanilla options and how
More informationOption Pricing with Aggregation of Physical Models and Nonparametric Learning
Option Pricing with Aggregation of Physical Models and Nonparametric Learning Jianqing Fan Princeton University With Loriano Mancini http://www.princeton.edu/ jqfan May 16, 2007 0 Outline Option pricing
More informationTEACHING NOTE 98-01: CLOSED-FORM AMERICAN CALL OPTION PRICING: ROLL-GESKE-WHALEY
TEACHING NOTE 98-01: CLOSED-FORM AMERICAN CALL OPTION PRICING: ROLL-GESKE-WHALEY Version date: May 16, 2001 C:\Class Material\Teaching Notes\Tn98-01.wpd It is well-known that an American call option on
More informationMispricing of Index Options with Respect to Stochastic Dominance Bounds?
Mispricing of Index Options with Respect to Stochastic Dominance Bounds? June 2017 Martin Wallmeier University of Fribourg, Bd de Pérolles 90, CH-1700 Fribourg, Switzerland. Email: martin.wallmeier@unifr.ch
More informationVolatility Surface. Course Name: Analytical Finance I. Report date: Oct.18,2012. Supervisor:Jan R.M Röman. Authors: Wenqing Huang.
Course Name: Analytical Finance I Report date: Oct.18,2012 Supervisor:Jan R.M Röman Volatility Surface Authors: Wenqing Huang Zhiwen Zhang Yiqing Wang 1 Content 1. Implied Volatility...3 2.Volatility Smile...
More informationThe Risk and Return Characteristics of the Buy Write Strategy On The Russell 2000 Index
The Risk and Return Characteristics of the Buy Write Strategy On The Russell 2000 Index Nikunj Kapadia and Edward Szado 1 January 2007 1 Isenberg School of Management, University of Massachusetts, Amherst,
More informationAssessing the Incremental Value of Option Pricing Theory Relative to an "Informationally Passive" Benchmark
Forthcoming in the Journal of Derivatives September 4, 2002 Assessing the Incremental Value of Option Pricing Theory Relative to an "Informationally Passive" Benchmark by Stephen Figlewski Professor of
More informationCHAPTER 10 OPTION PRICING - II. Derivatives and Risk Management By Rajiv Srivastava. Copyright Oxford University Press
CHAPTER 10 OPTION PRICING - II Options Pricing II Intrinsic Value and Time Value Boundary Conditions for Option Pricing Arbitrage Based Relationship for Option Pricing Put Call Parity 2 Binomial Option
More informationOptions Order Flow, Volatility Demand and Variance Risk Premium
1 Options Order Flow, Volatility Demand and Variance Risk Premium Prasenjit Chakrabarti Indian Institute of Management Ranchi, India K Kiran Kumar Indian Institute of Management Indore, India This study
More informationFinancial Derivatives Section 5
Financial Derivatives Section 5 The Black and Scholes Model Michail Anthropelos anthropel@unipi.gr http://web.xrh.unipi.gr/faculty/anthropelos/ University of Piraeus Spring 2018 M. Anthropelos (Un. of
More informationChapter -7 CONCLUSION
Chapter -7 CONCLUSION Chapter 7 CONCLUSION Options are one of the key financial derivatives. Subsequent to the Black-Scholes option pricing model, some other popular approaches were also developed to value
More informationSensex Realized Volatility Index (REALVOL)
Sensex Realized Volatility Index (REALVOL) Introduction Volatility modelling has traditionally relied on complex econometric procedures in order to accommodate the inherent latent character of volatility.
More informationLECTURE 12. Volatility is the question on the B/S which assumes constant SD throughout the exercise period - The time series of implied volatility
LECTURE 12 Review Options C = S e -δt N (d1) X e it N (d2) P = X e it (1- N (d2)) S e -δt (1 - N (d1)) Volatility is the question on the B/S which assumes constant SD throughout the exercise period - The
More informationZ. Wahab ENMG 625 Financial Eng g II 04/26/12. Volatility Smiles
Z. Wahab ENMG 625 Financial Eng g II 04/26/12 Volatility Smiles The Problem with Volatility We cannot see volatility the same way we can see stock prices or interest rates. Since it is a meta-measure (a
More informationChapter 9 - Mechanics of Options Markets
Chapter 9 - Mechanics of Options Markets Types of options Option positions and profit/loss diagrams Underlying assets Specifications Trading options Margins Taxation Warrants, employee stock options, and
More informationYour use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at
American Finance Association On Valuing American Call Options with the Black-Scholes European Formula Author(s): Robert Geske and Richard Roll Source: The Journal of Finance, Vol. 39, No. 2 (Jun., 1984),
More informationThe Black-Scholes Model
IEOR E4706: Foundations of Financial Engineering c 2016 by Martin Haugh The Black-Scholes Model In these notes we will use Itô s Lemma and a replicating argument to derive the famous Black-Scholes formula
More informationMeasuring and Explaining Skewness in Pricing Distributions Implied from Livestock Options
Measuring and Explaining Skewness in Pricing Distributions Implied from Livestock Options by Andrew M. McKenzie, Michael R. Thomsen, and Michael K. Adjemian Suggested citation format: McKenzie, A. M.,
More informationModeling the Implied Volatility Surface:
Modeling the Implied Volatility Surface: An Empirical Study for S&P 5 Index Option by Tiandong Zhong B.B.A, Shanghai University of Finance of Economics, 29 and Chenguang Zhong B.Econ, Nankai University,
More informationNeural networks as a semiparametric option pricing tool
Neural networks as a semiparametric option pricing tool Michaela Baruníkova Institute of Economic Studies, Charles University, Prague. e-mail: babenababena@gmail.com Jozef Baruník Institute of Information
More informationWhich GARCH Model for Option Valuation? By Peter Christoffersen and Kris Jacobs
Online Appendix Sample Index Returns Which GARCH Model for Option Valuation? By Peter Christoffersen and Kris Jacobs In order to give an idea of the differences in returns over the sample, Figure A.1 plots
More informationFin 4200 Project. Jessi Sagner 11/15/11
Fin 4200 Project Jessi Sagner 11/15/11 All Option information is outlined in appendix A Option Strategy The strategy I chose was to go long 1 call and 1 put at the same strike price, but different times
More informationOPTION POSITIONING AND TRADING TUTORIAL
OPTION POSITIONING AND TRADING TUTORIAL Binomial Options Pricing, Implied Volatility and Hedging Option Underlying 5/13/2011 Professor James Bodurtha Executive Summary The following paper looks at a number
More informationB. Combinations. 1. Synthetic Call (Put-Call Parity). 2. Writing a Covered Call. 3. Straddle, Strangle. 4. Spreads (Bull, Bear, Butterfly).
1 EG, Ch. 22; Options I. Overview. A. Definitions. 1. Option - contract in entitling holder to buy/sell a certain asset at or before a certain time at a specified price. Gives holder the right, but not
More informationVolatility By A.V. Vedpuriswar
Volatility By A.V. Vedpuriswar June 21, 2018 Basics of volatility Volatility is the key parameter in modeling market risk. Volatility is the standard deviation of daily portfolio returns. 1 Estimating
More informationOptions Markets: Introduction
17-2 Options Options Markets: Introduction Derivatives are securities that get their value from the price of other securities. Derivatives are contingent claims because their payoffs depend on the value
More informationDepartment of Mathematics. Mathematics of Financial Derivatives
Department of Mathematics MA408 Mathematics of Financial Derivatives Thursday 15th January, 2009 2pm 4pm Duration: 2 hours Attempt THREE questions MA408 Page 1 of 5 1. (a) Suppose 0 < E 1 < E 3 and E 2
More informationLecture Notes: Basic Concepts in Option Pricing - The Black and Scholes Model (Continued)
Brunel University Msc., EC5504, Financial Engineering Prof Menelaos Karanasos Lecture Notes: Basic Concepts in Option Pricing - The Black and Scholes Model (Continued) In previous lectures we saw that
More informationThe Impact of Computational Error on the Volatility Smile
The Impact of Computational Error on the Volatility Smile Don M. Chance Louisiana State University Thomas A. Hanson Kent State University Weiping Li Oklahoma State University Jayaram Muthuswamy Kent State
More informationF A S C I C U L I M A T H E M A T I C I
F A S C I C U L I M A T H E M A T I C I Nr 38 27 Piotr P luciennik A MODIFIED CORRADO-MILLER IMPLIED VOLATILITY ESTIMATOR Abstract. The implied volatility, i.e. volatility calculated on the basis of option
More informationLecture 4: Forecasting with option implied information
Lecture 4: Forecasting with option implied information Prof. Massimo Guidolin Advanced Financial Econometrics III Winter/Spring 2016 Overview A two-step approach Black-Scholes single-factor model Heston
More informationThe Black-Scholes-Merton Model
Normal (Gaussian) Distribution Probability Density 0.5 0. 0.15 0.1 0.05 0 1.1 1 0.9 0.8 0.7 0.6? 0.5 0.4 0.3 0. 0.1 0 3.6 5. 6.8 8.4 10 11.6 13. 14.8 16.4 18 Cumulative Probability Slide 13 in this slide
More informationThe performance of GARCH option pricing models
J Ö N K Ö P I N G I N T E R N A T I O N A L B U S I N E S S S C H O O L JÖNKÖPING UNIVERSITY The performance of GARCH option pricing models - An empirical study on Swedish OMXS30 call options Subject:
More informationOptions, Futures and Structured Products
Options, Futures and Structured Products Jos van Bommel Aalto Period 5 2017 Options Options calls and puts are key tools of financial engineers. A call option gives the holder the right (but not the obligation)
More informationEfficiency of Black-Scholes Model for Pricing NSE INDEX Nifty50 Put Options and Observed Negative Cost of Carry Problem
Efficiency of Black-Scholes Model for Pricing NSE INDEX Nifty50 Put Options and Observed Negative Cost of Carry Problem Rajesh Kumar 1, Dr. Rachna Agrawal 2 1 Assistant Professor, Satya College of Engg.
More informationCalculation of Volatility in a Jump-Diffusion Model
Calculation of Volatility in a Jump-Diffusion Model Javier F. Navas 1 This Draft: October 7, 003 Forthcoming: The Journal of Derivatives JEL Classification: G13 Keywords: jump-diffusion process, option
More informationFINANCE 2011 TITLE: RISK AND SUSTAINABLE MANAGEMENT GROUP WORKING PAPER SERIES
RISK AND SUSTAINABLE MANAGEMENT GROUP WORKING PAPER SERIES 2014 FINANCE 2011 TITLE: Mental Accounting: A New Behavioral Explanation of Covered Call Performance AUTHOR: Schools of Economics and Political
More informationFX Derivatives. Options: Brief Review
FX Derivatives 2. FX Options Options: Brief Review Terminology Major types of option contracts: - calls give the holder the right to buy the underlying asset - puts give the holder the right to sell the
More informationOULU BUSINESS SCHOOL. Ilkka Rahikainen DIRECT METHODOLOGY FOR ESTIMATING THE RISK NEUTRAL PROBABILITY DENSITY FUNCTION
OULU BUSINESS SCHOOL Ilkka Rahikainen DIRECT METHODOLOGY FOR ESTIMATING THE RISK NEUTRAL PROBABILITY DENSITY FUNCTION Master s Thesis Finance March 2014 UNIVERSITY OF OULU Oulu Business School ABSTRACT
More informationVolatility Trade Design
Volatility Trade Design J. Scott Chaput* Louis H. Ederington** May 2002 * Assistant Professor of Finance ** Oklahoma Bankers Professor of Finance University of Otago Michael F. Price College of Business
More informationPredicting the Market
Predicting the Market April 28, 2012 Annual Conference on General Equilibrium and its Applications Steve Ross Franco Modigliani Professor of Financial Economics MIT The Importance of Forecasting Equity
More informationPricing Currency Options with Intra-Daily Implied Volatility
Australasian Accounting, Business and Finance Journal Volume 9 Issue 1 Article 4 Pricing Currency Options with Intra-Daily Implied Volatility Ariful Hoque Murdoch University, a.hoque@murdoch.edu.au Petko
More informationApplying the Principles of Quantitative Finance to the Construction of Model-Free Volatility Indices
Applying the Principles of Quantitative Finance to the Construction of Model-Free Volatility Indices Christopher Ting http://www.mysmu.edu/faculty/christophert/ Christopher Ting : christopherting@smu.edu.sg
More informationEstimating the Dynamics of Volatility. David A. Hsieh. Fuqua School of Business Duke University Durham, NC (919)
Estimating the Dynamics of Volatility by David A. Hsieh Fuqua School of Business Duke University Durham, NC 27706 (919)-660-7779 October 1993 Prepared for the Conference on Financial Innovations: 20 Years
More informationRichardson Extrapolation Techniques for the Pricing of American-style Options
Richardson Extrapolation Techniques for the Pricing of American-style Options June 1, 2005 Abstract Richardson Extrapolation Techniques for the Pricing of American-style Options In this paper we re-examine
More informationComputational Efficiency and Accuracy in the Valuation of Basket Options. Pengguo Wang 1
Computational Efficiency and Accuracy in the Valuation of Basket Options Pengguo Wang 1 Abstract The complexity involved in the pricing of American style basket options requires careful consideration of
More informationOptions Order Flow, Volatility Demand and Variance Risk Premium
Options Order Flow, Volatility Demand and Variance Risk Premium Prasenjit Chakrabarti a and K Kiran Kumar b a Doctoral Student, Finance and Accounting Area, Indian Institute of Management Indore, India,
More informationNo ANALYTIC AMERICAN OPTION PRICING AND APPLICATIONS. By A. Sbuelz. July 2003 ISSN
No. 23 64 ANALYTIC AMERICAN OPTION PRICING AND APPLICATIONS By A. Sbuelz July 23 ISSN 924-781 Analytic American Option Pricing and Applications Alessandro Sbuelz First Version: June 3, 23 This Version:
More informationMispricing of S&P 500 Index Options
Mispricing of S&P 500 Index Options George M. Constantinides Jens Carsten Jackwerth Stylianos Perrakis University of Chicago University of Konstanz Concordia University and NBER Keywords: Derivative pricing;
More informationNotes: This is a closed book and closed notes exam. The maximal score on this exam is 100 points. Time: 75 minutes
M339D/M389D Introduction to Financial Mathematics for Actuarial Applications University of Texas at Austin Sample In-Term Exam II - Solutions Instructor: Milica Čudina Notes: This is a closed book and
More informationMixing Di usion and Jump Processes
Mixing Di usion and Jump Processes Mixing Di usion and Jump Processes 1/ 27 Introduction Using a mixture of jump and di usion processes can model asset prices that are subject to large, discontinuous changes,
More informationMispriced Index Option Portfolios George Constantinides University of Chicago
George Constantinides University of Chicago (with Michal Czerwonko and Stylianos Perrakis) We consider 2 generic traders: Introduction the Index Trader (IT) holds the S&P 500 index and T-bills and maximizes
More informationChapter 15: Jump Processes and Incomplete Markets. 1 Jumps as One Explanation of Incomplete Markets
Chapter 5: Jump Processes and Incomplete Markets Jumps as One Explanation of Incomplete Markets It is easy to argue that Brownian motion paths cannot model actual stock price movements properly in reality,
More informationYour use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at
Predictable Dynamics in the S&P 500 Index Options Implied Volatility Surface Author(s): Sílvia Gonçalves and Massimo Guidolin Source: The Journal of Business, Vol. 79, No. 3 (May 2006), pp. 1591-1635 Published
More informationFX Derivatives. 2. FX Options. Options: Brief Review
FX Derivatives 2. FX Options Options: Brief Review Terminology Major types of option contracts: - calls gives the holder the right to buy the underlying asset - puts gives the holder the right to sell
More informationCircular no.: MCX/TRD/373/2017 October 09, 2017
Circular no.: MCX/TRD/373/2017 October 09, 2017 Approval for Gold Option Contracts with Gold (1 Kg) Futures as underlying In terms of the provisions of the Rules, Bye-Laws and Business Rules of the Exchange,
More informationAbstract. Keywords: Equity Options, Investment, S&P CNX Nifty 50, out the money (OTM), at the money (ATM), in the money (ITM)
Abstract This paper examines the historical time-series performance of trading strategies involving options on the S&P CNX Nifty 50 Index. Each option strategy is examined over different maturities and
More informationECONOMICS DEPARTMENT WORKING PAPER. Department of Economics Tufts University Medford, MA (617)
ECONOMICS DEPARTMENT WORKING PAPER 214 Department of Economics Tufts University Medford, MA 2155 (617) 627-356 http://ase.tufts.edu/econ Implied Volatility and the Risk-Free Rate of Return in Options Markets
More informationAdvanced Corporate Finance. 5. Options (a refresher)
Advanced Corporate Finance 5. Options (a refresher) Objectives of the session 1. Define options (calls and puts) 2. Analyze terminal payoff 3. Define basic strategies 4. Binomial option pricing model 5.
More informationA Kaleidoscopic Study of Pricing Performance of Stochastic Volatility Option Pricing Models: Evidence from Recent Indian Economic Turbulence
R E S E A R C H includes research articles that focus on the analysis and resolution of managerial and academic issues based on analytical and empirical or case research A Kaleidoscopic Study of Pricing
More informationUniversity of Colorado at Boulder Leeds School of Business MBAX-6270 MBAX Introduction to Derivatives Part II Options Valuation
MBAX-6270 Introduction to Derivatives Part II Options Valuation Notation c p S 0 K T European call option price European put option price Stock price (today) Strike price Maturity of option Volatility
More informationThe Favorite-Longshot Bias in S&P 500 and FTSE 100 Index Futures Options: The Return to Bets and the Cost of Insurance *
The Favorite-Longshot Bias in S&P 500 and FTSE 100 Index Futures Options: The Return to Bets and the Cost of Insurance * Stewart D. Hodges Financial Options Research Centre University of Warwick Coventry,
More informationCovered Option Strategies in Nordic Electricity Markets
Covered Option Strategies in Nordic Electricity Markets Antti Klemola Jukka Sihvonen Abstract We test the performance of popular option strategies in the Nordic power derivative market using 12 years of
More informationUNIVERSITÀ DEGLI STUDI DI TORINO SCHOOL OF MANAGEMENT AND ECONOMICS SIMULATION MODELS FOR ECONOMICS. Final Report. Stop-Loss Strategy
UNIVERSITÀ DEGLI STUDI DI TORINO SCHOOL OF MANAGEMENT AND ECONOMICS SIMULATION MODELS FOR ECONOMICS Final Report Stop-Loss Strategy Prof. Pietro Terna Edited by Luca Di Salvo, Giorgio Melon, Luca Pischedda
More informationS&P/JPX JGB VIX Index
S&P/JPX JGB VIX Index White Paper 15 October 015 Scope of the Document This document explains the design and implementation of the S&P/JPX Japanese Government Bond Volatility Index (JGB VIX). The index
More informationFX Smile Modelling. 9 September September 9, 2008
FX Smile Modelling 9 September 008 September 9, 008 Contents 1 FX Implied Volatility 1 Interpolation.1 Parametrisation............................. Pure Interpolation.......................... Abstract
More informationThe accuracy of the escrowed dividend model on the value of European options on a stock paying discrete dividend
A Work Project, presented as part of the requirements for the Award of a Master Degree in Finance from the NOVA - School of Business and Economics. Directed Research The accuracy of the escrowed dividend
More informationCircular no.: MCX/TRD/185/2018 May 11, Commencement of Silver Options Contract with Silver (30 Kilograms) Futures as underlying
Circular no.: MCX/TRD/185/ May 11, Commencement of Silver Options Contract with Silver (30 Kilograms) Futures as underlying In terms of the provisions of the Rules, Bye-Laws and Business Rules of the Exchange,
More information