Volatility By A.V. Vedpuriswar

Similar documents
The Forecasting Power of the Volatility Index: Evidence from the Indian Stock Market

Sensex Realized Volatility Index (REALVOL)

Chapter 18 Volatility Smiles

Valuing Stock Options: The Black-Scholes-Merton Model. Chapter 13

John Hull, Risk Management and Financial Institutions, 4th Edition

Interpreting Volatility-Related Indicators & Benchmarks

VIX Fear of What? October 13, Research Note. Summary. Introduction

CHAPTER 10 OPTION PRICING - II. Derivatives and Risk Management By Rajiv Srivastava. Copyright Oxford University Press

Empirical Option Pricing

Developments in Volatility-Related Indicators & Benchmarks

Factors in Implied Volatility Skew in Corn Futures Options

Financial Derivatives: A hedging tool 6/21/12

FINA 695 Assignment 1 Simon Foucher

Table of contents. Slide No. Meaning Of Derivative 3. Specifications Of Futures 4. Functions Of Derivatives 5. Participants 6.

[Uncovered Interest Rate Parity and Risk Premium]

The Greek Letters Based on Options, Futures, and Other Derivatives, 8th Edition, Copyright John C. Hull 2012

Chapter 14. Exotic Options: I. Question Question Question Question The geometric averages for stocks will always be lower.

Z. Wahab ENMG 625 Financial Eng g II 04/26/12. Volatility Smiles

Short Volatility Trading with Volatility Derivatives. Russell Rhoads, CFA

Cboe S&P 500 3x Up, 1x Down Enhanced Growth Index Series. Cboe S&P 500 3x Up, 1x Down Enhanced Growth Index Series

Efficacy of Interest Rate Futures for Corporate

FRENCH ELECTION RESULTS IN VOLATILITY CRUSH

Black Scholes Equation Luc Ashwin and Calum Keeley

VIX ETPs, Inter-Relationships between Volatility Markets and Implications for Investors and Traders

Disclosure 6/26/2017. TEXPERS Derivatives Symposium. 6/20/2017 Chicago Board Options Exchange (CBOE)

Cboe S&P 500 2x Up, 1x Down, 10% Buffer Protect Index Series. Cboe S&P 500 2x Up, 1x Down, 10% Buffer Protect Index Series

Applying the Principles of Quantitative Finance to the Construction of Model-Free Volatility Indices

Of Option Trading PRESENTED BY: DENNIS W. WILBORN

HSI Volatility Index ( VHSI )

Volatility as a Tradable Asset: Using the VIX as a market signal, diversifier and for return enhancement

MORNING SESSION. Date: Friday, May 11, 2007 Time: 8:30 a.m. 11:45 a.m. INSTRUCTIONS TO CANDIDATES

Debunking Five Myths about Cash-Secured PutWrite Strategies

1. What is Implied Volatility?

EXTRACTING VALUE FROM VOLATILITY

Volatility Surface. Course Name: Analytical Finance I. Report date: Oct.18,2012. Supervisor:Jan R.M Röman. Authors: Wenqing Huang.

CHAPTER 6: RISK AND RISK AVERSION

An Introduction to CBOE Mini Options

Derivatives Questions Question 1 Explain carefully the difference between hedging, speculation, and arbitrage.

Modeling and Pricing of Variance Swaps for Local Stochastic Volatilities with Delay and Jumps

Risk and Return of Covered Call Strategies for Balanced Funds: Australian Evidence

Cboe S&P % Buffer Protect Index Series. Cboe S&P % Buffer Protect Index Series

Global Financial Management. Option Contracts

Table of Contents. Introduction

P-7. Table of Contents. Module 1: Introductory Derivatives

Learn To Trade Stock Options

S&P 500 Variance Futures: Exchange-Traded/OTC Conventions

Estimating 90-Day Market Volatility with VIX and VXV

Efficacy of Interest Rate Futures for Retail

Assicurazioni Generali: An Option Pricing Case with NAGARCH

Presentation Outline. What is the VIX? VIX and the Market VIX Futures Contract Specs & Pricing VIX Options Unique Characteristics Trading Case Studies

S&P/JPX JGB VIX Index

Options (2) Class 20 Financial Management,

Black Scholes Option Valuation. Option Valuation Part III. Put Call Parity. Example 18.3 Black Scholes Put Valuation

Empirical Option Pricing. Matti Suominen

Options Markets: Introduction

Options: How About Wealth & Income?

Interactive Brokers Webcast. VIX Trading Strategies Russell Rhoads, CFA Senior Instructor The Options Institute CBOE

Variance in Volatility: A foray into the analysis of the VIX and the Standard and Poor s 500 s Realized Volatility

CHAPTER 6: RISK AVERSION AND CAPITAL ALLOCATION TO RISKY ASSETS

Indian Institute of Management Calcutta. Working Paper Series. WPS No. 797 March Implied Volatility and Predictability of GARCH Models

Portfolio Management

Analysis of the Holiday Effect

Analysis of the Models Used in Variance Swap Pricing

Intro to Trading Volatility

Implied Volatility Surface

Queens College, CUNY, Department of Computer Science Computational Finance CSCI 365 / 765 Fall 2017 Instructor: Dr. Sateesh Mane.

Chapter 15: Jump Processes and Incomplete Markets. 1 Jumps as One Explanation of Incomplete Markets

Economic Risk and Decision Analysis for Oil and Gas Industry CE School of Engineering and Technology Asian Institute of Technology

Equity Volatility and Covered Call Writing

Chapter 14 Exotic Options: I

Investment Strategy On-Demand Webinar Series

Derivatives Analysis & Valuation (Futures)

Short Term Trading With Weeklys SM Options

The Old VIX vs. New VIX.

Final Exam. 5. (24 points) Multiple choice questions: in each case, only one answer is correct.

Regulatory Circular RG15-022

Trading Volatility with VIX Futures and Options. Peter Lusk. Instructor The Options Institute at CBOE

Implied Volatilities

Capital Projects as Real Options

Finance 527: Lecture 31, Options V3

Index Methodology Book. HSI Volatility Index

OPTIONS & GREEKS. Study notes. An option results in the right (but not the obligation) to buy or sell an asset, at a predetermined

Trading Volatility: Theory and Practice. FPA of Illinois. Conference for Advanced Planning October 7, Presented by: Eric Metz, CFA

FE570 Financial Markets and Trading. Stevens Institute of Technology

Volatility Forecasts for Option Valuations

Important Concepts LECTURE 3.2: OPTION PRICING MODELS: THE BLACK-SCHOLES-MERTON MODEL. Applications of Logarithms and Exponentials in Finance

Equity Portfolio November 25, 2013 BUS 421

May 2012 Examination

COMPARISON OF NATURAL HEDGES FROM DIVERSIFICATION AND DERIVATE INSTRUMENTS AGAINST COMMODITY PRICE RISK : A CASE STUDY OF PT ANEKA TAMBANG TBK

Volatility as investment - crash protection with calendar spreads of variance swaps

Measuring Market Fear

VelocityShares Equal Risk Weight ETF (ERW) Please refer to Important Disclosures and the Glossary of Terms section at the end of this material.

CBOE Volatility Index and VIX Futures Trading

Simple Robust Hedging with Nearby Contracts

MATH4143: Scientific Computations for Finance Applications Final exam Time: 9:00 am - 12:00 noon, April 18, Student Name (print):

Chapter 9 - Mechanics of Options Markets

Knowing When to Buy or Sell a Stock

SOLUTIONS 913,

Introduction. ISMR Derivatives Market Derivatives Market

CHAPTER 6: RISK AVERSION AND CAPITAL ALLOCATION TO RISKY ASSETS

Transcription:

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 Volatility Calculate daily return u 1 = ln S i / S i-1 Variance rate per day m 1 ( ) 1 u i m u i 1 2 We can simplify this formula by making the following simplifications. u i = (S i S i-1) / S i-1 ; ū = 0; m-1 = m If we want to weight ; m 2 1 m i 1 u 2 i 2 i u 2 i ( 1) i 2

Estimating Volatility Exponentially weighted moving average model means weights decrease exponentially as we go back in time. n 2 = 2 n-1+ (1 - ) u 2 n-1 = [ n-22 + (1- )u n-22 ] + (1- )u n-1 2 = (1- )[u n-12 + u n-22 ] + 2 n-2 2 = (1- ) [u n-12 + u 2 n-2 + 2 u n-32 ] + 3 2 n-3 If we apply GARCH model, n2 = γv L + u n-12 + 2 n-1 V L = Long run average variance rate, γ + + = 1. If γ = 0, = 1-, =, it becomes exponentially weighted model. GARCH incorporates the property of mean reversion. 3

Measuring Volatility : The VIX On March 26, 2004, the first-ever trading in futures on the VIX Index began on CBOE Futures Exchange (CFE). As of February 24, 2006, it became possible to trade VIX options contracts. The VIX is calculated and disseminated in real-time by the Chicago Board Options Exchange. It is a weighted blend of prices for a range of options on the S&P 500 index. 4

What VIX implies The VIX is quoted in percentage points and translates, roughly, to the expected movement in the S&P 500 index over the next 30-day period, which is then annualized. For example, if the VIX is 15, this represents an expected annualized change of 15% over the next 30 days. So the index option markets expect the S&P 500 to move up or down over the next 30-day period. 5

Volatility can mean movement in either direction This occurs when option buyers and sellers anticipate a likely sharp 6 move to the downside. VIX is often called the "fear index. But a high VIX is not necessarily bearish for stocks. Instead, the VIX is a measure of fear of volatility in either direction, including the upside. If investors anticipate large upside volatility, they will not sell upside call stock options unless they receive a large premium. Option buyers will be willing to pay such high premiums only if similarly anticipating a large upside move. The general increase in upside stock option call prices raises the VIX. VIX may also go up if there is a general increase in downside stock put option premiums.

Significance of VIX High VIX means investors see significant risk that the market will move sharply, whether downward or upward. Only when investors perceive neither significant downside risk nor significant upside potential will the VIX be low. The new VIX is based on the S&P 500 Index (SPXSM), the core index for U.S. equities. It estimates volatility by averaging the weighted prices of SPX puts and calls over a wide range of strike prices. 7

VIX components The components of VIX are near- and next-term put and call options, usually in the first and second SPX contract months. Near-term options must have at least one week to expiration. This is to minimize pricing anomalies that might occur close to expiration. When near-term options have less than a week to expiration, VIX rolls to the second and third SPX contract months. For example, on the second Friday in June, VIX would be calculated using SPX options expiring in June and July. On the following Monday, July would replace June as the near-term and August would replace July as the next-term. 8

VIX and portfolio insurance Volatility technically means unexpected moves up or down. But over time, the S&P 500 index option market has become dominated by hedgers. Hedgers buy index puts when they are concerned about a potential drop in the stock market. The more investors demand, the higher the price of portfolio insurance. VIX reflects the price of portfolio insurance. 9

VIX movements Over its entire history, the median daily closing level of VIX has been 18.88. 50% of time VIX closed between 14.60 and 23.66 (a range of 9.06 points). 75% of the time VIX closed between 12.04 and 29.14 (a range of 17.10 points). 95% of the time VIX closed between 11.30 and 37.22 (a range of 22.92 points). The widest range experienced was 2008, with VIX closing between 18.16 and 63.31 (a range of 45.15 index points) about 90%. The second widest range was in 1987. 10

VIX Chart 11

India VIX India VIX is a volatility index based on the index option prices of NIFTY. India VIX is computed using the best bid and ask quotes of the out-of-the-money near and mid-month NIFTY option contracts which are traded on the F&O segment of NSE. India VIX indicates the investor s perception of the market s volatility in the near term. The index depicts the expected market volatility over the next 30 calendar days. i.e. higher the India VIX values, higher the expected volatility and vice-versa. 12

Problem A stock s daily closing prices (in $) for the past 10 days have been 50, 49, 48, 47, 48, 49, 50, 51, 50, 49. Calculate the daily volatility. If a trader has invested $ 1 million in this stock, what is the 95% daily VAR? t Price Returns Square of Returns 1 50 2 49 -.0202.0004 3 48 -.02062.000425 4 47 -.02105.000443 5 48 +.02105.000443 6 49 +.02062.000425 7 50 +.0202.0004 8 51 +.0198.000392 9 50 -.0198.000392 10 49 -.0202.0004 Total -.0202.00372 Mean = -.0202/9 = -.00224 Variance =.00372/9 - (.00224) 2 =.0004083 Std devn =.0202 13

Infosys Volatility Date Opening price Return 5/23/2018 1196.8 5/24/2018 1192.45-0.00364 5/25/2018 1223 0.025297 Std Dev 5/28/2018 1228.3 0.004324 0.012807 5/29/2018 1220-0.00678 5/30/2018 1216.7-0.00271 5/31/2018 1217 0.000247 6/1/2018 1231.9 0.012169 6/4/2018 1220-0.00971 6/5/2018 1225.1 0.004172 6/6/2018 1221.2-0.00319 6/7/2018 1238 0.013663 6/8/2018 1249.9 0.009566 6/11/2018 1259.75 0.00785 6/12/2018 1265 0.004159 6/13/2018 1265 0 6/14/2018 1259-0.00475 6/15/2018 1238.7-0.01626 6/18/2018 1285 0.036696 6/19/2018 1266-0.0149 6/20/2018 1245-0.01673 6/21/2018 1250 0.004008 14 6/22/2018 1245.5-0.00361

BSE Sensex Volatility Date Opening price Return 23-May-18 34656.63 24-May-18 34404.14-0.00731 25-May-18 34753.47 0.010103 Std Deviation 28-May-18 35074.32 0.00919 0.006927 29-May-18 35213.14 0.00395 30-May-18 34876.13-0.00962 31-May-18 35083.81 0.005937 1-Jun-18 35373.98 0.008237 4-Jun-18 35503.24 0.003647 5-Jun-18 35029.45-0.01343 6-Jun-18 34932.49-0.00277 7-Jun-18 35278.38 0.009853 8-Jun-18 35406.47 0.003624 11-Jun-18 35472.59 0.001866 12-Jun-18 35525.3 0.001485 13-Jun-18 35835.44 0.008692 14-Jun-18 35743.1-0.00258 15-Jun-18 35656.26-0.00243 18-Jun-18 35698.43 0.001182 19-Jun-18 35552.47-0.0041 20-Jun-18 35329.61-0.00629 21-Jun-18 35644.05 0.008861 22-Jun-18 35428.42-0.00607 15

Problem The current estimate of daily volatility is 1.5%. The closing price of an asset yesterday was $30. The closing price of the asset today is $30.50. Using the EWMA model, with λ = 0.94, calculate the updated estimate of volatility. Solution Variance = λ σ 2 t-1 + ( 1 λ) r t-1 2 λ =.94 r t-1 = ln [(30.50 )/ 30] =.0165 Variance = (.94) (.015) 2 + (1-.94) (.0165) 2 =.000228 Volatility =.01509 = 1.509 % 16

Problem On Tuesday, return on a stock was 4% and volatility estimate was 1%. Find volatility estimate for Wednesday if = 0.94. Solution Variance estimate for Wednesday=(1-0.94)*(.04)^2 +(0.94)*(.01)^2 = =.00019 = 0.019%. Std. dev = (.00019) =.01378 = 1.378% Tuesday volatility (Std. Dev.) estimate was 1%. Actual return on Tuesday was 4%. Therefore, volatility estimate for Wednesday is more than that for Tuesday i.e. 1.378% as compared to 1%. 17

Problem Continuing the previous example, volatility estimate for Wednesday was 1.378%. Assume that actual return on Wednesday was 0%. What is the variance estimate for Thursday? Solution Variance estimate for Thursday = (1-0.94)*(0)^2 + 0.94*(.01378)^2=.0001785 Std Dev. =0.0134 In the very short-term, like daily returns, estimated volatility is the expected return. Since latest return of 0% was lesser than estimated volatility (and estimated return) of 1.378%, volatility for next day is revised downward from 1.378% to 1.34%. 18

Problem n2 = Y V L + u 2 n-1 + 2 n-1 Beginning price = 1040, Closing price = 1060 Most recent estimate of volatility = 0.01 Y V L = 0.000002, = 0.06, = 0.92 Find new variance estimate. Solution Return = 20/1040 = 0.01923 New variance estimate = 0.000002 + 0.06X.01923*.01923 + 0.92X.01*.01 =.0001162 New volatility estimate = 0.01078 19

Problem Suppose that the annualised volatility of an asset will be 20% from month 0 to 6, 22% from month 6 to month 12, and 24% from month 12 to 24. What volatility should be used in Black-Scholes to value a 2-year option? Solution The average variance rate is 6 0.20 2 6 0.22 24 2 12 0.24 2 0.0509 The volatility used should be 0.0509 0.2256 or 22.56% 20

Problem on Implied volatility Problem Option price = 4; Stock price = 45 Strike price = 50; Interest rate = 8% Time to maturity = 1 year Calculate implied volatility. Solution We use Deriva Gem Answer : 25.12% 21

Problem on Implied Volatility Stock price = 51, Strike price = 50, Time = 1, Interest rate = 8% We use Deriva Gem to work out Implied Volatility. Option Price Implied Volatility 5 7.51 6 15.36 7 21.39 8 27.07 9 32.59 10 38.05 22

Problem on Implied Volatility Suppose that the result of a major lawsuit affecting Microsoft is due to be announced tomorrow. Microsoft s stock price is currently $60. If the ruling is favourable to Microsoft, the stock price is expected to jump to $75. If it is unfavourable, the stock is expected to fall to $50. What is the riskneutral probability of a favourable ruling? Assume that the volatility of Microsoft s stock will be 25% for 6 months after the ruling if the ruling is favourable and 40% if it is unfavourable. Calculate the relationship between implied volatility and strike price for 6-month European options on Microsoft today. Microsoft does not pay dividends. Assume that the 6-month risk-free rate is 6%. Consider call options with strike price of 30, 40, 50, 60, 70 and 80. Ref : John C Hull, Options, Futures and Other Derivatives 23

Solution Suppose that P is the probability of a favourable ruling. The expected price of Microsoft tomorrow is 75p + 50 (1-p) = 50 + 25p This must be the price of Microsoft today. (We ignore the expected return to an investor over one day) Hence 50 + 25p = 60 Or p = 0.4, p being the risk neutral probability. 24

If the ruling is favourable, the volatility, σ, will be 25%. parameters are S 0 = 75, r = 0.06, and T = 0.5. Other option If K = 50, the price of a European call option Scholes is 26.502. as calculated by Black If the ruling is unfavourable, the volatility, σ will be 40%. Other option parameters are S 0 = 50, r = 0.06, and T = 0.5. If K = 50, the price of a European call option is 6.310. The price today of a European call option with a strike price 50 is the weighted average of 26.502 and 6.310 or: 0.4 x 26.502 + 0.6 x 6.310 = 14.387 The Black Scholes equation can now be used to calculate the implied volatility when the option has this price. S 0 =60,K=50, T = 0.5, r = 0.06 and c = 14.387. The implied volatility is 47.76%. 25

These calculations can be repeated for other strike prices. The results are shown in the table below. Strike Price Call option Price Favourable outcome Call option price Unfavourable outcome Weighted Price Implied Volatility 30 45.887 21.001 30.955 46.67 40 36.182 12.437 21.935 47.78 50 26.502 6.310 14.387 47.76 60 17.171 2.826 8.564 46.05 70 9.334 1.161 4.430 43.22 80 4.159 0.451 1.934 40.36 26

Volatility: Smile, Term Structure and Surface Item Description Volatility smile It is the relationship between implied volatility and strike price for options with a certain maturity. Volatility term structure The variation of implied volatility with the time to maturity of the option. The volatility term structure tends to be downward sloping when volatility is high and upward sloping when it is low. Volatility Surface The implied volatility as a function of the strike price and time to maturity is known as a volatility surface. 27