# Estimating the Degree of Activity of jumps in High Frequency Financial Data. joint with Yacine Aït-Sahalia

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1 Estimating the Degree of Activity of jumps in High Frequency Financial Data joint with Yacine Aït-Sahalia

2 Aim and setting An underlying process X = (X t ) t 0, observed at equally spaced discrete times : X 0, X n,, X i n, on a fixed time interval [0, T ] Assuming X has jumps on [0, T ], determine the degree of activity of the jumps, when the time lag n goes to 0: finitely many? or, if infinitely many, how infinite is it?

3 Measuring the degree of activity - 1 If X is a Lévy process with Lévy measure F and tail F (x) = F ({y : y > x}): F (x) = mean number of jumps X t = X t X t with size X t > x over [0, 1] For all t > 0 the equivalence holds: s t X s r < a.s. ( x[ r 1)F (dx) <

4 The set I of all r as such has the form for some β [0, 2], and 2 I. I = (β, ), or I = [β, ) β is the Blumenthal-Getoor index, introduced in β is a sensible measure of the jump activity, since lim x 0 xβ+ε F (x) = 0, lim sup x 0 x β ε F (x) = When X is stable, the BG index equals the stable index, and β / I

5 Measuring the degree of activity - 2 X is an Itô semimartingale, that is JUMPS = X t = X 0 + t 0 t 0 b sds + t 0 σ sdw s + JUMPS { x 1} x(µ ν)(ds, dx) + t 0 { x >1} xµ(ds, dx) Here µ is the jump measure of X, and its predictable compensator ν can be factorized as ν(ω; dt, dx) = dt F t (ω, dx).

6 Assumptions: on b and σ: they are locally bounded (random or not, dependent on X or not). on F t : see later. Instantaneous index: I i t = {r 0 : ( x r 1)F t (dx) < }, β i t = inf(i i t) Index over [0, t]: I t = {r 0 : t 0 ds ( x r 1)F t (dx) < }, β t = inf(i t ) Warning: Those are random.

7 An unrealistic situation: [0, T ] the path of X t is fully observed on Then: σ(ω) t is known for t [0, T ] I(ω) T is also known, because (outside a null set) r I T iff s T X s r <. Apart from I T, the measures F t are essentially unknown, even under the (strong) additional assumption that F t (ω, dx) = F (dx) is non-random, independent of time. This motivates the estimation of β T, which is about the most we can infer, concerning the measures F t, even in this unrealistic situation.

8 The main challenge Consider the special case X = σw + Y, where Y is a β-stable process, so β t (ω) = β. Any increment n i X = X i n X (i 1) n satisfies (equality in law). Then: n i X = σ 1/2 n W 1 + 1/β n Y 1 Recalling β < 2 and n 0, with a large probability n i X is close to σ 1/2 n W 1 in law. those increments give essentially no information on Y, and are of order of magnitude 1/2 n However if Y has a big jump at time S, the corresponding increment is close to Y S.

9 Hence, one has to throw away all small increments. However, β is related to the behavior of F near 0, hence to the very small jumps of Y. In practice one uses only increments bigger than a cutoff level Asymptotically: α ϖ n for some ϖ (0, 1/2). those increments are big because, since 1/2 n contribution is due to Y. << ϖ n, the main those increments mostly contain a single big jump, of size of order at least ϖ n. we still get some information on small jumps, because ϖ n 0.

10 The same heuristics works for Itô semimartingales. This leads to consider, for fixed ϖ (0, 1/2) and α > 0, the functionals U(ϖ, α, n ) t = [t/ n ] i=1 1 { n i X >α ϖ n }. which simply counts the number of increments whose magnitude is greater than α ϖ n. This way, we are retaining only those increments of X that are predominantly made of contributions due to a single jump

11 The key property Again X = σw + Y with Y β-stable for a while. Essentially, U(ϖ, α, n ) t is the same as, or close to, the number V (ϖ, α, n ) t of jumps of Y which are bigger than α ϖ n, in the interval [0, t]. V (ϖ, α, n ) t is a Poisson random variable with parameter (C = suitable constant). Hence Ct/α β βϖ n. βϖ n V (ϖ, α, n ) t C/α β (in probability), ( 1 n βϖ/2 βϖ ) n V (ϖ, α, n ) t C/α β N (0, C/α β ) (in law).

12 These properties carry over to U(ϖ, α, n ) t in the case above, and also to more general semimartingales, subject to the following assumption, where 0 β < β < 2 are non-random: We have for all (ω, t): where F t = F t + F t + F t, F t is locally of the β stable form F t(dx) = 1 x 1+β ( a (+) t 1 {0<x z (+) t } + a( ) t 1 ( ) { z t x<0} for some predictable non-negative processes a (+) t, a ( ) t, z (+) t and z ( ) t. ) dx,

13 F t has a density, and has a Blumenthal-Getoor index β/2 and F t (R) = 0 if F t (R) = 0. F t is singular and has a Blumenthal-Getoor index β. Plus some (weak) technical conditions on a (+) t, a ( ) t, z t (+) z t ( ). and

14 For example, any process of the following form satisfies the assumption where: dx t = b t dt + σ t dw t + δ t dy t + δ t dy t δ and δ are càdlàg adapted processes Y is β stable Y is any Lévy process with Blumenthal-Getoor index less that β/2.

15 THEOREM Under the previous assumptions, and if A t = = 1 β t 0 ( a (+) s + a ( ) s ) ds, βϖ n U(ϖ, α, n ) t A t /α β in probability, 1 βϖ/2 n ( βϖ n U(ϖ, α, n ) t A t /α β ) converges stably in law to a variable which, conditionally on the process X is centered Gaussian with variance A t /α β. We also have the joint convergence (stably in law) for two or more values of α and/or ϖ.

16 The estimators We pick ϖ (0, 1/2) and 0 < α < α, and define β n (ϖ, α, α ) = log(u(ϖ, α, n) T /U(ϖ, α, n ) T ) log(α, /α) By the first part of the theorem, we have consistency: β n (ϖ, α, α ) P β, in restriction to the set where A T > 0. Another family of consistent estimator is β n(ϖ, α) = log(u(ϖ, α, n) T /U(ϖ, α, 2 n ) T ). ϖ log 2

17 A central limit theorem The second part of the key theorem, yields THEOREM As soon as ϖ < 1 set {AT > 0}, 2+β 2 5β, and in restriction to the 1) the variables 1 ϖβ/2 n (ˆβ n (ϖ, α, α ) β) converge stably in law to a variable which conditionally on the process X is centered Gaussian with variance (α β α β )/A T (log(α /α)) 2,

18 2) the variables ( 1 U(ϖ,α, n ) t log(α /α) ) 1 1/2 U(ϖ,α, n ) t ( β n (ϖ, α, α ) β ) converge stably in law to a standard normal variable independent of X. (similar results hold for the other family of estimators).

19 The qualifier in restriction to the set {A T > 0} is essential in this statement. On the (random) set {A T > 0}, the jump activity index is β. On the complement set {A T = 0}, the number β is not the jump activity index for X on [0, T ]. We do not know even the behavior of β n (ϖ, α, α ) in probability, not to speak about a central limit theorem. However we suspect that any convergent subsequence as a limit strictly smaller than β/2.

20 These results are model-free in a sense, because the drift and the volatility processes are totally unspecified; on the other hand the assumptions on the Lévy measures F t are quite strong. When those assumptions fail, we do not know how to prove the results, even in the case where X is a Lévy process.

21 0.1. Simulation Results The data generating process is dx t /X 0 = σ t dw t + dy t Y is a pure jump process, β stable or Compound Poisson (β = 0). Stochastic volatility σ t = v 1/2 t dv t = κ(η v t )dt + γv 1/2 t db t + dj t, Leverage effect: E[dW t db t ] = ρdt, ρ < 0 With jumps in volatility: J is a compound Poisson process with uniform jumps.

22 Simulations: β = 1.25 and β = 1 Estimator Based on Two Truncation Levels b β = 1.25 b β = bβ = β = bβ = b β = 0

23 Simulations: β = 0.75 and β = bβ = β = bβ = b β =

24 Simulations: β = 0.25 and β = bβ = b β =

25 0.2. Empirical Results: Intel & Microsoft 2005 INTC n 2 sec 5 sec 15 sec α Qtr Qtr Qtr Qtr All Year

26 MSFT n 2 sec 5 sec 15 sec α Qtr Qtr Qtr Qtr All Year

27 2.0 Degree of Jump Activity for INTC, Degree of Jump Activity for MSFT, 2005 Estimator of β Estimator of β α'/α α'/α

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