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highfrequency (version 0.8.0)

ReMeDIAsymptoticVariance: Asymptotic variance of ReMeDI estimator

Description

Estimates the asymptotic variance of the ReMeDI estimator.

Usage

ReMeDIAsymptoticVariance(pData, kn, lags, phi, i)

Arguments

pData

xts or data.table containing the log-prices of the asset

kn

numerical value determining the tuning parameter kn this controls the lengths of the non-overlapping interval in the ReMeDI estimation

lags

numeric containing integer values indicating the lags for which to estimate the (co)variance

phi

tuning parameter phi

i

tuning parameter i

Value

a list with components ReMeDI and asympVar containing the ReMeDI estimation and it's asymptotic variance respectively

Details

Some notation is needed for the estimator of the asymptotic covariance of the ReMeDI estimator. Let $$ \delta\left(n, i\right) = t_{i}^{n}-t_{t-1}^{n}, i\geq 1, $$ $$ \hat{\delta}_{t}^{n}=\left(\frac{k_{n}\delta\left(n,i+1+k_{n}\right)-t_{i+2+2k_{n}}^{n}+t_{i+2+k_{n}}^{n}}{\left(t_{i+k_{n}}^{n}-t_{i}^{n}\right)\vee\phi_{n}}\right)^{2}, $$

$$ U\left(1\right)_{t}^{n}=\sum_{i=0}^{n_{t}-\omega\left(1\right)_{n}}\hat{\delta}_{i}^{n}, $$ $$ U\left(2,\boldsymbol{j}\right)_{t}^{n}=\sum_{i=0}^{n_{t}-\omega\left(2\right)_{n}}\hat{\delta}_{i}^{n}\Delta_{\boldsymbol{j}}\left(Y\right)_{i+\omega\left(2\right)_{2}^{n}}^{n}, $$

$$ U\left(3,\boldsymbol{j},\boldsymbol{j}'\right)_{t}^{n}=\sum_{i=0}^{n_{t}-\omega\left(3\right)_{n}}\hat{\delta}_{i}^{n}\Delta_{\boldsymbol{j}}\left(Y\right)_{i+\omega\left(3\right)_{2}^{n}}^{n}\Delta_{\boldsymbol{j}'}\left(Y\right)_{i+\omega\left(3\right)_{3}^{n}}^{n}, $$

$$ U\left(4;\boldsymbol{j},\boldsymbol{j}'\right)_{t}^{n}=-\sum_{i=2^{q-1}k_{n}}^{n_{t}-\omega\left(4\right)_{n}}\Delta_{\boldsymbol{j}}\left(Y\right)\Delta_{\boldsymbol{j}^{\prime}}\left(Y\right)_{i+\omega\left(3\right)_{3}^{n}}^{n}, $$ $$ U\left(5,k;\boldsymbol{j},\boldsymbol{j}'\right)_{t}^{n}=\sum_{Q_{q}\in\mathcal{Q}_{q}}\sum_{i=2^{e\left(Q_{q}\right)}k_{n}}^{n_{t}-\omega\left(5\right)_{n}}\Delta_{\boldsymbol{j}_{Q_{q}\oplus\left(\boldsymbol{j}\prime_{Q_{q'}}\left(+k\right)\right)}}\left(Y\right)_{i}^{n}\prod_{\ell:l_{\ell}\in Q_{q}^{c}}\Delta_{\left(j_{l_{\ell}},j\prime_{l_{\ell}}+k\right)\left(Y\right)_{i+\omega\left(5\right)_{\ell+1}^{n}\prime}}, $$

\( U\left(6,k;\boldsymbol{j},\boldsymbol{j}^{\prime}\right)=\sum_{j_{l}\in\boldsymbol{j},j_{l^{\prime}}^{\prime}\in\boldsymbol{j}^{\prime}}\sum_{i=2k_{n}}^{n_{t}-\omega\left(6\right)n}\Delta_{\left(j_{l},j_{l^{\prime}}^{\prime}+k\right)}\left(Y\right)_{i}^{n}\Delta_{\boldsymbol{j}_{-l}}\left(Y\right)_{i+\omega\left(6\right)_{2}^{n}}^{n}\Delta_{\boldsymbol{j}_{-l^{\prime}}^{\prime}}\left(Y\right)_{i+\omega\left(6\right)_{3}^{n}}^{n} \\ -\sum_{j_{l}\in\boldsymbol{j}}\sum_{i=2^{q}k_{n}}^{n_{t}-\omega^{\prime}\left(6\right)_{n}}\Delta_{\left\{ j_{l}\right\} \oplus\boldsymbol{j}^{\prime}\left(+k\right)}\left(Y\right)_{i}^{n}\Delta_{\boldsymbol{j}-l}\left(Y\right)_{i+\omega^{\prime}\left(6\right)_{2}^{n}}^{n} \\ -\sum_{j_{l^{\prime}\in\boldsymbol{j}^{\prime}}^{\prime}}\sum_{i=2^{q}k_{n}}^{n_{t}-\omega^{\prime\prime}\left(6\right)n}\Delta_{\left\{ j_{l^{\prime}}^{\prime}+k\right\} \oplus\boldsymbol{j}}\left(Y\right)_{i}^{n}\Delta_{\boldsymbol{j}_{-l^{\prime}}^{\prime}}\left(Y\right)_{i+\omega^{\prime\prime}\left(6\right)_{2}^{n}\prime}^{n}, \)

$$ U\left(7,k;\boldsymbol{j},\boldsymbol{j}^{\prime}\right)_{t}^{n}=ReMeDI\left(\boldsymbol{j}\oplus\boldsymbol{j}^{\prime}\left(+k\right)\right)_{t}^{n}, $$ $$ U\left(k;\boldsymbol{j},\boldsymbol{j}^{\prime}\right)_{t}^{n}=\sum_{\ell=5}^{7}U\left(\ell,k;\boldsymbol{j},\boldsymbol{j}^{\prime}\right)_{t}^{n}, $$ $$ U\left(k;\boldsymbol{j},\boldsymbol{j}^{\prime}\right)_{t}^{n}=\sum_{\ell=5}^{7}U\left(\ell,k;\boldsymbol{j},\boldsymbol{j}^{\prime}\right)_{t}^{n}, $$

Where the indices are given by: $$ \omega\left(1\right)_{n}=2+2k_{n},\ \omega\left(2\right)_{2}^{n}=2+\left(3+2^{q-1}\right)k_{n},\ \omega\left(2\right)_{n}=\omega\left(2\right)_{2}^{n}+j_{1}+k_{n}, $$

$$ \omega\left(3\right)_{2}^{n}=2+\left(3+2^{q-1}\right)k_{n},\ \omega\left(3\right)_{3}^{n}=2+\left(5+2^{q-1}+2^{q^{\prime}-1}\right)k_{n}+j_{1}, $$

$$ \omega\left(3\right)_{n}=\omega\left(3\right)_{3}^{n}+j_{1}^{\prime}+k_{n},\ \omega\left(4\right)_{2}^{n}=2k_{n}+q_{n}^{\prime}+j_{1},\ \omega\left(4\right)_{n}=\omega\left(4\right)_{2}^{n}+j_{1}^{\prime}+k_{n}, $$ $$ e\left(Q_{q}\right)=\left(2\left|Q_{q}\right|+q^{\prime}-q-1\right)\vee1,\ \omega\left(5\right)_{\ell+1}^{n}=4\ell k_{n}+\sum_{\ell^{\prime}=1}^{\ell}j_{l_{\ell^{\prime}}}\vee\left(j_{l_{\ell}}^{\prime}+k\right)\textrm{for}\ell\geq 1, $$ $$ \omega\left(5\right)_{n}=\omega\left(5\right)_{\left|Q_{q}^{c}\right|+1}^{n}+j_{l_{\left|Q_{q}^{c}\right|}}\vee\left(j_{l_{\left|Q_{q}^{c}\right|}}+k\right)+k_{n}, $$

$$ \omega\left(6\right)_{2}^{n}=\left(2^{q-2}+2\right)k_{n}+j_{\ell}\vee\left(j_{\ell^{\prime}}^{\prime}+k\right),\ \omega\left(6\right)_{3}^{n}=\left(2^{q-2}+2^{q^{\prime}-2}+2\right)k_{n}+j_{1}+j_{\ell}\vee\left(j_{\ell}^{\prime}+k\right), $$

$$ \omega^{\prime}\left(6\right)_{2}^{n}=\left(2^{q-2}+2\right)k_{n}+j_{\ell}\vee\left(j_{1}^{\prime}+k\right),\ \omega^{\prime\prime}\left(6\right)_{2}^{n}=\left(2^{q^{\prime}-2}+1\right)k_{n}+\left(j_{\ell^{\prime}}^{\prime}+k\right)\vee j_{1}, $$ $$ \omega\left(6\right)_{n}=\omega\left(6\right)_{3}^{n}+j^{\prime}+k_{n},\ \omega^{\prime}\left(6\right)_{n}=\omega^{\prime}\left(6\right)_{2}^{n}+j_{1}+k_{n},\ \omega^{\prime\prime}\left(6\right)_{n}=\omega^{\prime\prime}\left(6\right)_{2}^{n}j_{1}^{\prime}+k_{n}, $$

The asymptotic variance estimator is then given by

$$ \hat{\sigma}\left(\boldsymbol{j},\boldsymbol{j}^{\prime}\right)_{t}^{n}=\frac{1}{n_{t}}\sum_{\ell=1}^{3}\hat{\sigma}_{\ell}\left(\boldsymbol{j},\boldsymbol{j}^{\prime}\right)_{t}^{n}, $$

where $$ \hat{\sigma}_{1}\left(\boldsymbol{j},\boldsymbol{j}^{\prime}\right)_{t}^{n}=U\left(0;\boldsymbol{j},\boldsymbol{j}^{\prime}\right)+\sum_{k=1}^{i_{n}}\left(U\left(k;\boldsymbol{j},\boldsymbol{j}^{\prime}\right)_{t}^{n}\right)+\left(2i_{n}+1\right)U\left(4;\boldsymbol{j},\boldsymbol{j}\right)_{t}^{n}, $$

$$ \hat{\sigma}_{2}\left(\boldsymbol{j},\boldsymbol{j}^{\prime}\right)_{t}^{n}=U\left(3;\boldsymbol{j},\boldsymbol{j}^{\prime}\right), $$ $$ \hat{\sigma}_{3}\left(\boldsymbol{j},\boldsymbol{j}^{\prime}\right)_{t}^{n}=\frac{1}{n_{t}^{2}}\textrm{ReMeDI}\left(Y,\boldsymbol{j}\right)_{t}^{n}\textrm{ReMeDI}\left(Y,\boldsymbol{j}^{\prime}\right)_{t}^{n}U\left(1\right)_{t}^{n}\\, $$ $$ -\frac{1}{n_{t}}\left(\textrm{ReMeDI}\left(Y,\boldsymbol{j}\right)_{t}^{n}U\left(2,\boldsymbol{j}^{\prime}\right)_{t}^{n}+\textrm{ReMeDI}\left(Y,\boldsymbol{j}^{\prime}\right)_{t}^{n}U\left(2,\boldsymbol{j}\right)_{t}^{n}\right), $$