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

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1 Applying the Principles of Quantitative Finance to the Construction of Model-Free Volatility Indices Christopher Ting Christopher Ting : christopherting@smu.edu.sg : : LKCSB 5036 October 24, 2017 Christopher Ting QF 101 October 24, /41

2 Lesson Plan 1 Introduction 2 What is VIX? 3 Model-Free Formula 4 Proof 5 Market Reality 6 Method 7 Volatility Index 8 Takeaways Christopher Ting QF 101 October 24, /41

3 Two Uncertainties Market Uncertainty Price fluctuation = volatility Correlation of volatility with return Volatility Uncertainty Volatility is also fluctuating Volatility clusters Ex ante increase or decrease in volatility is a risk Christopher Ting QF 101 October 24, /41

4 Important Questions How to forecast ex ante volatility? Volatility index fear gauge VIX, VXD, VXN, RVX, VDAX-NEW, VSMI, VSTOXX,... How to estimate the volatility risk premium? Investors who bear the volatility risk demand a volatility risk premium. Investors who don t want to bear the higher volatility forecasted pay the volatility risk premium. Christopher Ting QF 101 October 24, /41

5 VIX: Fear Gauge? Christopher Ting QF 101 October 24, /41

6 What is VIX? Created in 1993, VIX is the ticker symbol for the CBOE Volatility Index for S&P 500 Index. VIX quantifies option traders expectation of future volatility for the next 30 calendar days. The old version of VIX relied on the Black-Scholes model to back out an implied volatility for each of the 8 options that are near-the-money. Old VIX is the average of these implied volatilities. Current new version is model-free, and uses as many out-of-the-money S&P 500 index options as possible. Why called the fear gauge? Contributions of OTM put options are larger than OTM call options. S&P 500 Index tends to be lower when VIX is higher. Christopher Ting QF 101 October 24, /41

7 Relation with the Underlying S&P 500 Index Christopher Ting QF 101 October 24, /41

8 VXN and Nasdaq 100 Index Christopher Ting QF 101 October 24, /41

9 VXO and Dow Jones Industrial Average Index Christopher Ting QF 101 October 24, /41

10 VIX in Investment & Trading The popularity of VIX allows CBOE to roll out futures and options on VIX. Speculation on the future level of volatility in a pure manner. short the VIX futures when VIX is unusually high long the VIX futures when VIX is unusually low Hedge against long equity exposure. Hedge against a high correlation market condition, which typically makes stock selection more difficult. Tracking of aggregate credit spread Tracking of carry trade benchmark (read Carry Trade Defined) Christopher Ting QF 101 October 24, /41

11 VIX and CDX Christopher Ting QF 101 October 24, /41

12 VIX and Carry Trade Benchmark Christopher Ting QF 101 October 24, /41

13 Applications of VIX Volatility becomes a tradable asset class. CBOE offers futures and options on VIX revenue generation for the exchange. Speculation: Express a view on future volatility through trading. Hedging: Reduction of NAV fluctuation. VIX 2 as the fair value for a 30-day variance swap. The payoff function (same as P&L in this case) of this forward contract of amount A for the buyer is, at maturity T = 30, P&L of Buyer T = A ( Realized Variance - VIX 2), where the realized variance is the variance of future daily returns from day 0 up to T. Christopher Ting QF 101 October 24, /41

14 Implied Volatility Implied volatility used to be model-dependent. Black-Scholes option pricing formula Binomial tree Model risk All models are wrong... George Box A smile surface that extends well into the wings, which are suspect of model risk Model-free approach to implied volatility VIX discrete computations Academics smooth computations Christopher Ting QF 101 October 24, /41

15 Model-Free Variance σ 2 MF Direct computation given the midquotes of puts and call options σmft 2 := E Q ( ) 0 V (0, T ) = 2e rt ( F0 0 p(x, S 0, T ) X 2 dx + Strike price: X Time to maturity: T Underlying asset price at time 0: S 0 Forward price of the underlying asset: Risk-free rate of tenor T : r European put s midquote: p(x, S 0, T ) European call s midquote: c(x, S 0, T ) c(x, S 0, T ) X 2 ) dx Christopher Ting QF 101 October 24, /41 (1)

16 Features of Model-Free Approach No requirement for an option pricing model = No model risk! No worry about parameters The only exogenous inputs are risk-free interest rate and dividend yields No bias σ MF reflects volatility across all out-of-the-money strike prices and thus reflects the option skew Uses both put and call options = σ MF is less sensitive to individual option prices. Christopher Ting QF 101 October 24, /41

17 First Principle for Option Prices Recall that the first principle involves a risk-free rate r 0. The price P 0 today and the expected payoff Z T, which will be settled T years from today, are related by the first principle: P 0 = e r 0T E Q 0 (Z T ). For European call option, the payoff is (S T X) + =: max(s T X, 0). So in general, given the strike price X, c 0 = e r 0T E Q 0 ( (ST X) +). For European put option, p 0 = e r 0T E Q 0 ( (X ST ) +). Christopher Ting QF 101 October 24, /41

18 Variance as a Difference of Two Returns With R t being the simple return, Pre-U Maclaurin s series gives ln ( 1 + R t ) = Rt 1 2 R2 t + O(R 3 t ). In other words, the following approximation holds because at daily frequency or higher, R t is generally very small. R 2 t 2 ( R t ln ( 1 + R t )). (2) Since the mean E ( R t ) 0, E ( R 2 t ) V ( Rt ), i.e. R 2 t may be regarded as the variance σ 2 t of time t. Insight: Twice the difference between the simple return R t and the log return ln ( 1 + R t ) is the variance. Is it guaranteed that R t ln ( 1 + R t ) 0? YES! Christopher Ting QF 101 October 24, /41

19 Integrated Variance Next, we consider the integrated variance V (0, T ) defined as V (0, T ) := T 0 σ 2 t dt. The variance V (0, T ) is the sum of instantaneous variances σ 2 t realized over time 0 to time T. From (2) T 0 σ 2 t dt = 2 T 0 R t dt 2 T 0 ln(1 + R t ) dt. (3) Christopher Ting QF 101 October 24, /41

20 Integrated Variance as Model-Free Variance Now, under the risk neutral measure Q, and assuming a risk-free rate r 0 that remains constant from time 0 to time T, ( T ) T E Q 0 R t dt = E Q ( ) T 0 Rt dt = r 0 dt = r 0 T On the other hand, telescoping sum ( ) ( ) S1 S2 ln + ln + + ln S 0 S 1 0 ( ST 1 S T 2 ) ( ) ( ) ST ST + ln = ln S T 1 S 0 for tiny time interval t = 1 unit results in ( T ) ( ( )) E Q 0 ln(1 + R t ) dt = E Q ST 0 ln, S 0 since 1 + R t = S t+ t S T. Christopher Ting QF 101 October 24, /41

21 Under Risk-Neutral Measure Putting all terms together, we have σ 2 MF T := EQ 0 ( ( ) T V (0, T ) = E Q 0 = 2r 0 T 2E Q 0 ( ( ST ln S 0 0 ) σt 2 dt )). (4) Christopher Ting QF 101 October 24, /41

22 Forward Price For the second term on the right-hand side in (4), we consider known at time t = 0, and we express ln(s T / ) as ( ) ( ST 1 ln = ln S T ln S T 1 ) + S T 1 = = ST 1 ST X dx S T S T X X 2 ST S T 1 X 2 dx + S T 1 dx + S T 1. (5) For any z > 1, ln(1 + z) is a strictly concave function, hence ln(1 + z) < z. The left side of equation (5) is ln(1 + z) with z := S T 1. ST S T X It follows that the integral X 2 dx equals z ln(1 + z) and hence is strictly positive. Christopher Ting QF 101 October 24, /41

23 Strictly Positive We can then rewrite the integral as ST S T X ST X 2 dx = 1 ST > = 1 ST > ST = 1 ST > ST = (S T X) + X 2 dx + S T X ST X 2 dx + 1 ST < S T X X 2 dx 1 ST < S T X X 2 dx + 1 ST < F0 0 F0 S T F0 S T (X S T ) + S T X X 2 S T X X 2 X S T X 2 dx dx dx X 2 dx. (6) In the last step, we have used the fact that the asset price S T, which is unknown at time t = 0, can potentially attain a low value 0, or appreciate substantially to a high value. Christopher Ting QF 101 October 24, /41

24 Risk-Neutral Expectation In view of (6), (5) becomes, under the risk-neutral measure Q, ( ( )) E Q ST 0 ln = e rt c(s 0, X, T ) F0 X 2 dx e rt p(s 0, X, T ) 0 X 2 dx ( ) + E Q ST 0 1 = e rt c(x, S 0, T ) X 2 dx e rt F0 0 p(x, S 0, T ) X 2 dx. (7) To arrive at this result, E Q 0 ( ST ) = F0 has been applied: E Q 0 ( ) ST 1 = EQ 0 (S T ) 1 = 0. Christopher Ting QF 101 October 24, /41

25 Last Step Finally, we write Substituting (7) into (4), we obtain σ 2 MFT = 2r 0 T + 2e rt ( 2 ln S 0, ln S T S 0 = ln S T + ln S 0. (8) c(x, S 0, T ) X 2 dx + F0 0 p(x, S 0, T ) X 2 ) dx Since = e r 0T S 0, the first and last terms cancel out and the model-free formula (1) is obtained. Christopher Ting QF 101 October 24, /41

26 Advantages and Limitations The model-free approach incorporates information from out-of-the-money puts and calls (with respect to the forward price ) to produce a single implied volatility σ MF for a given maturity. 1 Given the weight, out-of-the-money puts contribute more to K2 σ MF, hence fear gauge. The model-free approach to implied volatility is applicable only for European options. Equity index options are typically European but stock options are American. Christopher Ting QF 101 October 24, /41

27 Issues in Implementation Strike price is not continuous but discrete. Strike prices in the option chain are not from 0 to. Most options are illiquid and most have only ask prices but not bid prices. Christopher Ting QF 101 October 24, /41

28 Market Reality Example Calls Puts Bid Ask Strike Bid Ask Option chain of BKX, KBW Nasdaq Bank Index 95.57) Expiration: Sep 15, Source: Marketwatch.com, as at end of Aug 15, Discrete strike price Limited strike range lowest strike L=$ highest strike H=$ Not liquid But quotes are firm, ready for trades Christopher Ting QF 101 October 24, /41

29 CBOE s Implementation According to a CBOE s white paper, the generalized formula used in the VIX calculation is σ 2 CBOE = 2er 0T K i T Ki 2 Q(K i ) 1 ( ) F 2 1, T K 0 i=1 where K 0 is the first strike below the forward index level, F ; K i is the strike price of the i-th out-of-the-money option; a call if K i > K 0 ; and a put if K i < K 0 ; both put and call if K i = K 0 ; K i is the interval between strike prices half the difference between the strike on either side of K i ; Q(K i ) is the midpoint of the bid-ask spread for each option with strike K i. Christopher Ting QF 101 October 24, /41

30 CBOE s Method in Detail SPX option selection criteria Out-of-the-money with respect to K 0 Non-zero bid price Once two puts (calls) with consecutive strike prices are found to have zero bid prices, no puts (calls) with lower (higher) strikes are considered for inclusion. Determine the forward SPX level, F Identify the strike price K s at which the absolute difference between the call and put prices is smallest. Then F = K s + e r0t ( c(k s ) p(k s ) ) Determine K 0 as the strike price immediately below F Christopher Ting QF 101 October 24, /41

31 Current Academic Practice Compute the midquotes. Convert the midquotes into implied volatilities with either Black-Scholes or binomial tree pricing model. Interpolate the implied volatilities with a spline into a smirk and smile (read TheOptionsGuide.com). Sample as many synthetic implied volatilities as possible from the volatility smirk/smile. Apply the pricing model to obtain the synthetic option prices. Christopher Ting QF 101 October 24, /41

32 Interpolation of Implied Volatilities OptionMetrics IvyDB US database Christopher Ting QF 101 October 24, /41

33 Fully Model-Free and Exact Option pricing models at all stages of computation are not involved. Rely exclusively on put-call parity c 0 p 0 = S 0 e qt Xe rt, where q is the dividend yield. The synthetic option o k (X, T ) over any small sub-interval (X k, X k+1 ] of strikes is represented locally as a cubic polynomial function: o k (X, T ) = s k 1X 3 + s k 2X 2 + s k 3X + s k 4. (9) Every cubic spline is defined by its coefficients s k 1 to sk 4. Integration over each sub-interval (X k, X k+1 ] admits a closed form expression: Xk+1 o k (X, T ) X k X 2 dx = s k Xk+1 2 X2 k 1 + s k 2 2(X k+1 X k ) ( ) ( + s k Xk ln s k 4 1 ). X k X k+1 X k (10) Christopher Ting QF 101 October 24, /41

34 Exact Representation Let there be M sub-intervals for the integration from L to for puts, and N sub-intervals for the integration from to H for calls in the model-free formula, (1). We obtain an exact representation of (1) as follows: ( 1 σmft 2 = 2e rt i= M ( N 1 + 2e rt i=0 p i Xi+1 2 X2 i p i 3 ln c i Xi+1 2 X2 i 1 2 +c i 3 ln + p i 2(X i+1 X i ) ( Xi+1 X i ( Xi+1 ) ( 1 p i 4 1 )) X i+1 X i + c i 2(X i+1 X i ) X i ) ( 1 c i 4 1 )) (11) X i+1 X i Christopher Ting QF 101 October 24, /41

35 No Risk-Free Arbitrage For three strike prices X a, X b, and X c such that X a < X b < X c the conditions necessary for the absence of arbitrage are (I) Price monotonicity (II) Gradient bounds (III) Convexity p a p b ; c b c a. (12) 0 p b p a X b X a 1 ; 1 c b c a X b X a 0. (13) p b p a X b X a p c p b X c X b ; c b c a X b X a c c c b X c X b. (14) Christopher Ting QF 101 October 24, /41

36 Interpolation of Prices with Three Constraints OptionMetrics IvyDB US database Christopher Ting QF 101 October 24, /41

37 Volatility Index To obtain the annualized volatility index σ for a fixed time horizon or constant maturity T, we interpolate the model-free variances σ 2 at a and σ 2 b T b with T a < T < T b. At time 0, following CBOE s practice, the model-free volatility index σ is obtained by linear interpolation as follows: σ 2 T = σat 2 T b T a + σb 2 T b T T T T a b. (15) a T b T a The Actual/365 day-count convention is used to annualize the variance. Example: In Slide 36, we have T a = 22/365 and T b = 50/365. We obtain σ a = 20.81% and σ b = 24.20%. For 30-day constant maturity, i.e., T = 30/365, applying (15) results in a model-free volatility index σ of 22.49% for August 30, Christopher Ting QF 101 October 24, /41

38 Takeaways VIX, known as the fear gauge, has become an important index in the financial market. Volatility, like credit, is now a tradable asset class. Implied volatilities such as VIX are forward looking, i.e., ex ante. Volatility risk premium can be estimated by the P&L of variance swaps. Existing methods are not fully model-free. Our fully model-free method is better and robust. No risk-free arbitrary opportunity through the constraints Exact Christopher Ting QF 101 October 24, /41

39 Acceptance of a Quantitative Finance Model In the end, a theory is accepted not because it is confirmed by conventional empirical tests, but because researchers persuade one another that the theory is correct and relevant. Fischer Black (1986) Christopher Ting QF 101 October 24, /41

40 Assignment Question A The risk-free zero-coupon interest rate is 0.97%, and the dividend yield is assumed to be zero. Refer to Slide Compute the midquotes of ITM calls to obtain the corresponding OTM put option prices for strike prices from $67.5 to $ Likewise, compute the midquotes of ITM puts to obtain the corresponding OTM call option prices for strike prices from $95 to $ Select a pair of near-the-money put option prices (midquote and/or the price obtained from ITM call option). You have to exercise judgment on which of these prices should be less incorrect. Likewise, select a pair of near-the money call options. Using linear interpolation, find the implied forward price. Christopher Ting QF 101 October 24, /41

41 Assignment (cont d) Question B Revise and reflect on all the materials covered so far. Write in one page (A4 size paper) about a few specific concepts that are most difficult and you are struggling with. What are the gaps that you think exist between your current level of math proficiency and finance literacy in relation to a concept difficult for you? Christopher Ting QF 101 October 24, /41

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