# Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds

## Abstract

**:**

## 1. Introduction

## 2. Relevant Literature

## 3. Methodology

#### 3.1. Identifying Intraday Jumps

_{t}is expressed as a jump-diffusion process given by

_{t}is the instantaneous drift with continuous and locally finite variation along the sample path; σ

_{t}> 0 is the spot volatility component that is assumed to be cádlág; ω

_{t}is a standard Brownian motion; and κ

_{t}dq

_{t}denotes the jump component where dq

_{t}= 1 if a jump is observed at time t and 0 otherwise. Jumps occur with time-varying intensity λ

_{t}and size κ

_{t}. Andersen et al. (2007b) consider whether a randomly selected intraday return realization is subject to a jump. They define intraday returns as

^{M}= 1 – α and α is the daily significance level of the test; $\sqrt{\frac{1}{M}B{V}_{t}}$ is the estimate of the instantaneous volatility for each intraday interval j = 1, …, M and $B{V}_{t}$ is the bipower variation at day t estimated following Barndorff-Nielsen and Shephard (2004, 2006) as

^{−5}.

#### 3.2. Mitigating Market Microstructure Noise

^{2}))

^{2}, β = 2E(ϵ

^{4}) − 3((ϵ

^{2}))

^{2}, γ = 4E(ϵ

^{2})(${\int}_{t-1}^{t}{\sigma}_{s}^{2}}ds$) − E(ϵ

^{4}) + 2(E(ϵ

^{2}))

^{2}.

#### 3.3. Variable Definitions

^{a}and v

^{b}are the volume at best ask and best bid, respectively. Width is measured using the relative quoted spread and the effective spread. The relative quoted spread is defined as

^{a}and p

^{b}are the best bid and ask quotes at the time of the trade, respectively, and $mid=\frac{{p}^{a}+{p}^{b}}{2}$ is the bid–ask midpoint price. If the price of market orders is improved to facilitate transactions, then the quoted spread would overestimate transaction costs. In this case, the effective spread is a better measure of actual transaction costs. The effective spread is constructed as

#### 3.4. Econometric Model

**x**on the probability of observing intraday jumps. The regressors in

**x**include liquidity and trading activity variables as well as macroeconomic news surprises calculated from survey results. j denotes the intraday time increment. Furthermore, the model is estimated for signed jumps by replacing the left-hand side variable with the probability of positive jumps P(J

^{+}= 1|

**x**) and the probability of negative jumps P(J

^{−}= 1|

**x**). All models are estimated with robust standard errors. The frequency of observing jumps is estimated at intraday interval j using lagged liquidity, volatility, and trading activity variables in addition to contemporaneous macroeconomic news surprises.

## 4. Data

## 5. Empirical Results

#### 5.1. Intraday Jumps and Liquidity Dynamics

#### 5.2. Intraday Jumps, Liquidity, and Macroeconomic Announcement Surprises

#### 5.3. Cojumps Analysis

^{−5}. Using Equation (10); there are 236 cojumps observed during the overlapping period in the sample from June 2007 to December 2010. About 12.29% of these cojumps occur within the 20-min interval that follows a macroeconomic announcement. This indicates that simultaneous jumps across SPDR Spiders and SPDR Gold are linked to changes to the fundamentals of the U.S. economy. Table 5 identifies the most important macroeconomic announcement surprises that are linked to cojumps using estimates from a probit model. The model is estimated using all macroeconomic announcements. For brevity, only the statistically significant announcements are reported.

#### 5.4. Post-Jump Price Discovery

_{1}captures the price impact of the order flow imbalance if no jump occurs, β

_{2}is the additional price impact of post-jump order flow, ϵ

_{j}

_{+1}is the error term.

_{2}is positive and statistically significant at the 10% level). Although the statistical evidence is weak, the realizations of jumps reveal information that impacts returns. In Model 2, estimates show that the order flow imbalance is negatively associated with SPDR Gold returns (β

_{1}is statistically significant at the 5% level); however, the effect size is small. Post-jump order flow is positively associated with returns in the SPDR Gold time series (β

_{2}is statistically significant at the 1% level). The size of the coefficient β

_{2}is significantly larger than β

_{1}suggesting that the occurrence of jumps play a significant informational role that affects prices. Results from Models 3 and 4 indicate that the post-jump order flow has a significant and positive effect on the prices in SPDR Spiders and SPDR Gold post the realization of jumps, which indicates that jump realizations contribute to price discovery. Models 5 and 6 reveal a significant informational role associated with post-positive jumps order flow in both ETFs. This role is diminished following the occurrence of a negative jump in the SPDR Gold price time series. Analysis extended to cojumps in Models 7 and 8 confirm the role of regressors shown in Models 5 and 6.

## 6. Conclusions

## Funding

## Conflicts of Interest

## References

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1 | Agrrawal et al. (2014) use a four-factor liquidity scoring algorithm to rank 462 ETFs, based on all four factors. They show that liquid ETFs are characterized by large market capitalization and lower bid–ask spreads. These ETFs attract a large trading volume and have a lower expense ratio. |

2 | Bandi et al. (2008) evaluate economic implications of the optimal sampling theory to investors. They show that the economic utility of a risk averse investor is improved when relying on variance forecasts constructed using the optimal sampling frequency rather than the 5 or 15-minute forecasts that are commonly used in the literature to mitigate market microstructure noise. |

3 | Balduzzi et al. (2001) find that the median is an efficient and unbiased predictor of macroeconomic announcement figures. |

4 | Occasionally, data are obtained for timestamps prior to 8:01 a.m. when the optimal sampling frequency is relatively low. This is necessary to match returns with macroeconomic announcements occurring at 8:30 a.m. |

5 | Occasionally, there are days where the optimal sampling interval exceeds 15 minutes. For these days, the sample is extended to include observations prior to 8:00 a.m. such that 2 observations are always included in the record before announcement times. |

6 | The results in the table are based on sampling all variables at the optimal frequency by starting at 8:01 a.m. on each day. |

7 | Estimate of probit models which include macroeconomic news surprises and liquidity variables show similar results to the ones reported in Table 5. Consumer confidence, initial claims, and leading indicators announcements remain significant. News variables do not subsume the information contained in liquidity variables. These results are not reported here for brevity. |

Indicator | Freq | Time | μ_{s} | σ_{s} | μ_{A} |
---|---|---|---|---|---|

Business inventories | M | 10:00 * | 0.028 | 1.582 | 27 |

Capacity utilization | M | 9:15 | −0.272 | 1.989 | 29 |

Construction spending | M | 10:00 | 0.290 | 2.369 | 25 |

Consumer confidence | M | 10:00 | −0.002 | 3.260 | 27 |

Consumer credit | M | 15:00 | −0.291 | 3.258 | 20 |

Consumer price index (CPI) | M | 8:30 | −0.106 | 1.358 | 30 |

Durable orders | M | 8:30 | −0.064 | 2.341 | 29 |

Factory orders | M | 10:00 | 0.300 | 1.629 | 22 |

GDP advance report | Q | 8:30 | −0.203 | 1.721 | 31 |

GDP second report | Q | 8:30 | 0.201 | 1.426 | 31 |

GDP third report | Q | 8:30 | −0.660 | 2.308 | 30 |

Housing starts | M | 8:30 | −0.180 | 2.703 | 29 |

Industrial production | M | 9:15 | −0.214 | 2.11 | 29 |

Initial jobless claims | W | 8:30 | 0.317 | 3.916 | 16 |

Leading indicators | M | 8:30 | −0.041 | 1.157 | 26 |

New home sales | M | 10:00 | −0.265 | 2.978 | 30 |

Personal consumption expenditures | M | 8:30 | 0.062 | 1.013 | 29 |

Personal income | M | 8:30 | 0.192 | 1.792 | 29 |

Producers price index (PPI) | M | 8:30 | 0.278 | 2.204 | 30 |

Private nonfarm payrolls | M | 8:30 | −0.609 | 2.201 | 30 |

Retail sales (ex-Auto) | M | 8:30 | 0.027 | 2.385 | 29 |

Retail sales | M | 8:30 | 0.006 | 2.299 | 29 |

Treasury budget | M | 14:00 | 0.059 | 1.538 | 15 |

Unemployment rate | M | 8:30 | −0.156 | 2.615 | 28 |

Institute for Supply Management survey (ISM) | M | 10:00 | 0.241 | 2.370 | 28 |

ISM non-manufacturing | M | 10:00 | 0.070 | 2.402 | 27 |

**Notes:**The table lists U.S. macroeconomic indicators, the frequency and time (U.S. E.T.) of each announcement per year, the mean and standard deviation of the announcement surprise over the sample period, as well as the average number of forecasts per indicator calculated as the ratio of survey count to the number of announcement releases calculated over the full sample period (January 2005–December 2010). The frequency of the announcements is denoted by Q = quarterly, M = monthly, and W = weekly. [*] Prior to 13 December 2005, the Business Inventories announcements were released at either 8:30 a.m. E.T. or 10:00 a.m. E.T.

Panel A | SPDR Spiders (SPY) | ||||||
---|---|---|---|---|---|---|---|

2005 | 2006 | 2007 | 2008 | 2009 | 2010 | All | |

Number of Jumps | 29 | 44 | 64 | 65 | 99 | 93 | 394 |

Number of Positive Jumps | 17 | 28 | 33 | 45 | 49 | 58 | 230 |

Number of Negative Jumps | 12 | 16 | 31 | 20 | 50 | 35 | 164 |

E (jump size | jumps) (%) | 0.48 | 0.41 | 0.61 | 1.66 | 1.1 | 0.84 | 0.93 |

SD (jump size | jump) (%) | 0.22 | 0.12 | 0.34 | 1.26 | 0.63 | 0.62 | 0.79 |

P (jumps | announcements) (%) | 3.58 | 6.8 | 5.55 | 4.32 | 6.63 | 5.43 | 5.39 |

P (news | jumps) (%) | 6.48 | 11.92 | 8.19 | 7.47 | 9.7 | 7.46 | 8.64 |

P (jumps, announcements) (%) | 0.05 | 0.09 | 0.04 | 0.04 | 0.07 | 0.05 | 0.06 |

Panel B | SPDR Gold (GLD) | ||||||

2007 | 2008 | 2009 | 2010 | All | |||

Number of Jumps | 68 | 118 | 130 | 116 | 432 | ||

Number of Positive Jumps | 45 | 66 | 76 | 71 | 258 | ||

Number of Negative Jumps | 23 | 52 | 54 | 45 | 174 | ||

E (jump size | jumps) (%) | 0.81 | 1.37 | 0.93 | 0.74 | 0.98 | ||

SD (jump size | jump) (%) | 0.44 | 0.62 | 0.39 | 0.32 | 0.52 | ||

P (jumps | announcements) (%) | 2.45 | 1.02 | 0.49 | 1.01 | 1.11 | ||

P (news | jumps) (%) | 3.44 | 1.33 | 0.63 | 0.01 | 1.48 | ||

P (jumps, announcements) (%) | 0.02 | 0.01 | 0.005 | 0.01 | 0.01 |

**Notes:**The table reports the number of jumps, number of positive jumps, number of negative jumps, the absolute mean size, and the standard deviation of jumps per year. The joint distribution of macroeconomic news announcements and jumps is calculated using the number of jumps observed at one (two) interval(s) following a macroeconomic announcement for SPDR Spiders (SPDR Gold).

Model | 1 | 2 | 3 | 4 | 5 | 6 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Dep. Variable | P(J_{Spiders} = 1|x) | $\mathit{P}({\mathit{J}}_{\mathit{S}\mathit{p}\mathit{i}\mathit{d}\mathit{e}\mathit{r}\mathit{s}}^{+}=1|\mathit{x})$ | $\mathit{P}({\mathit{J}}_{\mathit{S}\mathit{p}\mathit{i}\mathit{d}\mathit{e}\mathit{r}\mathit{s}}^{-}=1|\mathit{x})$ | P(J_{Gold} = 1|x) | $\mathit{P}({\mathit{J}}_{\mathit{G}\mathit{o}\mathit{l}\mathit{d}}^{+}=1|\mathit{x})$ | $\mathit{P}({\mathit{J}}_{\mathit{G}\mathit{o}\mathit{l}\mathit{d}}^{-}=1|\mathit{x})$ | ||||||

Coef | S.E. | Coef | S.E. | Coef | S.E. | Coef | S.E. | Coef | S.E. | Coef | S.E. | |

Constant | −2.879 *** | 0.032 | −3.028 *** | 0.029 | −3.152 *** | 0.032 | −2.459 *** | 0.039 | −2.563 *** | 0.043 | −2.762 *** | 0.055 |

Depth shocks | −0.041 *** | 0.015 | −0.041 ** | 0.018 | −0.035 | 0.023 | 0.053 *** | 0.015 | 0.038 *** | 0.018 | 0.059 *** | 0.021 |

ES shock | 0.048 *** | 0.016 | 0.045 *** | 0.019 | 0.039 * | 0.023 | 0.148 *** | 0.015 | 0.126 *** | 0.019 | 0.151 * | 0.021 |

RQS shocks | 0.078 *** | 0.020 | 0.036 * | 0.021 | 0.119 *** | 0.031 | 0.073 *** | 0.018 | 0.088 *** | 0.021 | 0.041 * | 0.025 |

NT | 0.142 *** | 0.011 | 0.121 ** | 0.024 | 0.136 *** | 0.013 | −0.037 * | 0.021 | −0.035 | 0.024 | −0.002 | 0.032 |

Resiliency | 0.640 * | 0.359 | 0.725 * | 0.374 | −0.022 | 0.063 | −0.233 | 0.358 | 0.054 | 0.081 | −0.976 *** | 0.34 |

OF Imbalance | 0.038 *** | 0.007 | 0.031 *** | 0.009 | 0.037 *** | 0.009 | 0.077 *** | 0.015 | 0.078 *** | 0.019 | 0.061 *** | 0.021 |

Volatility | −0.028 *** | 0.01 | −0.020 * | 0.011 | −0.034 *** | 0.010 | −0.078 *** | 0.025 | −0.125 *** | 0.03 | −0.025 ** | 0.029 |

McFadden R^{2} | 7.06 | 5.19 | 8.15 | 3.9 | 4.08 | 4.84 |

**Notes:**The table reports the coefficient estimates (Coef) of the probit model and their robust standard errors (S.E.). Models 1–3 report the results for SPDR Spiders. Models 4–6 report the results for SPDR Gold. ***, **, * denote the 1%, 5%, and 10% significance levels, respectively.

Model | 1 | 2 | 3 | 4 | 5 | 6 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Dep. Variable | P(J_{Spiders} = 1|x) | $\mathit{P}({\mathit{J}}_{\mathit{S}\mathit{p}\mathit{i}\mathit{d}\mathit{e}\mathit{r}\mathit{s}}^{+}=1|\mathit{x})$ | $\mathit{P}({\mathit{J}}_{\mathit{S}\mathit{p}\mathit{i}\mathit{d}\mathit{e}\mathit{r}\mathit{s}}^{-}=1|\mathit{x})$ | P(J_{Gold} = 1|x) | $\mathit{P}({\mathit{J}}_{\mathit{G}\mathit{o}\mathit{l}\mathit{d}}^{+}=1|\mathit{x})$ | $\mathit{P}({\mathit{J}}_{\mathit{G}\mathit{o}\mathit{l}\mathit{d}}^{-}=1|\mathit{x})$ | ||||||

Coef | SE | Coef | SE | Coef | SE | Coef | SE | Coef | SE | Coef | S.E. | |

Constant | −2.880 *** | 0.023 | −3.030 *** | 0.029 | −2.718 *** | 0.021 | −2.597 *** | 0.027 | −2.742 *** | 0.034 | −2.931 *** | 0.036 |

Depth shocks | −0.043 *** | 0.015 | −0.043 ** | 0.189 | −0.039 | 0.023 | 0.027 * | 0.014 | 0.025 | 0.018 | 0.024 | 0.023 |

ES shock | 0.048 *** | 0.016 | 0.045 ** | 0.199 | 0.039 * | 0.023 | 0.126 *** | 0.014 | 0.104 *** | 0.018 | 0.132 *** | 0.018 |

RQS shocks | 0.079 *** | 0.020 | 0.036 * | 0.021 | 0.119 *** | 0.029 | 0.098 *** | 0.016 | 0.068 *** | 0.020 | 0.122 *** | 0.023 |

NT | 0.142 *** | 0.011 | 0.121 *** | 0.014 | 0.137 *** | 0.013 | 0.074 *** | 0.014 | 0.073 *** | 0.015 | 0.064 *** | 0.019 |

Resiliency | 0.640 * | 0.359 | 0.725 ** | 0.370 | 0.022 | 0.063 | −0.007 | 0.013 | 0.001 | 0.007 | 0.279 *** | 0.003 |

OF Imbalance | 0.038 *** | 0.007 | 0.031 *** | 0.009 | 0.037 *** | 0.009 | 0.036 *** | 0.007 | 0.029 *** | 0.009 | 0.038 *** | 0.011 |

Volatility | −0.287 *** | 0.103 | −0.206 | 0.128 | −0.349 *** | 0.105 | −0.791 *** | 0.134 | −0.864 *** | 0.195 | −0.578 *** | 0.167 |

Consumer confidence | −0.060 *** | 0.014 | −0.055 *** | 0.013 | −0.054 *** | 0.013 | −0.049 *** | 0.014 | −0.045 | 0.013 | −0.044 *** | 0.012 |

Construction spending | −0.064 ** | 0.029 | −0.057 ** | 0.026 | −0.060 ** | 0.027 | −0.100 *** | 0.038 | −0.095 *** | 0.035 | −0.091 *** | 0.034 |

GDP second report | −0.097 * | 0.053 | −0.107 ** | 0.050 | ||||||||

GDP third report | 0.066 * | 0.037 | 0.059 * | 0.034 | 0.065 * | 0.036 | ||||||

Initial claims | 0.205 ** | 0.092 | 0.205 ** | 0.092 | ||||||||

Leading indicators | −0.177 * | 0.100 | ||||||||||

Nonfarm payroll | −0.286 ** | 0.145 | −0.332 ** | 0.147 | ||||||||

Personal income | −0.039 * | 0.019 | −0.032 * | 0.019 | 0.831 ** | 0.199 | 0.640 *** | 0.217 | 1.311 *** | 0.172 | ||

PPI | −0.872 *** | 0.196 | −0.686 *** | 0.213 | −1.335 *** | 0.171 | ||||||

Treasury Budget | −0.146 * | 0.085 | −0.167 * | 0.090 | ||||||||

Other announcements | YES | YES | YES | YES | YES | YES | YES | YES | YES | YES | YES | YES |

McFadden R^{2}(%) | 7.25 | 5.51 | 8.17 | 5.08 | 4.02 | 5.79 |

**Notes:**The table reports the coefficient estimates (Coef) of the probit model and their robust standard errors (S.E.). Models 1–3 report the results for SPDR Spiders. Models 4–6 report the results for SPDR Gold. ***, **, * denote the 1%, 5%, and 10% significance levels, respectively.

Model | 1 | 2 | 3 | |||
---|---|---|---|---|---|---|

Panel A | ||||||

Dep. Variable | P(cojump = 1|x) | P(cojump^{+} = 1|x) | P(cojump^{−} = 1|x) | |||

Coef | SE | Coef | SE | Coef | S.E. | |

Constant | −2.703 *** | 0.02 | −2.994 *** | 0.03 | −3.079 *** | 0.034 |

Consumer Confidence | −0.037 *** | 0.011 | −0.034 *** | 0.01 | −0.033 *** | 0.01 |

Initial Jobless Claims | 0.107 ** | 0.055 | 0.167 ** | 0.069 | ||

Leading Indicators | 0.574 * | 0.313 | 0.736 ** | 0.349 | ||

Other announcements | YES | YES | YES | YES | YES | YES |

McFadden R^{2}(%) | 1.00 | 0.09 | 4.00 | |||

Panel B | ||||||

Dep. variable | P(cojump = 1|x) | P(cojump^{+} = 1|x) | P(cojump^{−} = 1|x) | |||

Coef | SE | Coef | SE | Coef | S.E. | |

Constant | −3.193 *** | 0.167 | −3.793 *** | 0.266 | −3.105 *** | 0.184 |

Depth shocks _{Spiders} | −0.133 *** | 0.018 | −0.113 *** | 0.02 | −0.113 *** | 0.024 |

RQS shocks _{Spiders} | 0.022 *** | 0.005 | −0.007 | 0.023 | 0.025 *** | 0.006 |

NT _{Spiders} | 0.258 *** | 0.029 | 0.192 *** | 0.029 | 0.206 *** | 0.058 |

Resiliency _{Spiders} | −0.031 *** | 0.005 | −0.040 *** | 0.007 | −0.036 *** | 0.008 |

OF Imbalance _{Spiders} | 0 | 0.001 | −0.0007 | <0.0001 | 0.002 | 0.001 |

Volatility _{Spiders} | 0.019 *** | 0.123 | 0.075 | 0.161 | 0.032 | 0.03 |

Depth shocks _{Gold} | −0.493 *** | 0.106 | −0.687 *** | 0.155 | −0.229 * | 0.013 |

RQS shocks _{Gold} | 0.004 *** | 0.001 | 0.004 *** | 0.001 | 0.003 *** | 0.001 |

NT _{Gold} | −1.138 *** | 0.203 | −0.647 *** | 0.194 | −1.336 *** | 0.303 |

Resiliency _{Gold} | 0.038 ** | 0.018 | 0.026 | 0.023 | 0.058 *** | 0.04 |

OF Imbalance _{Gold} | −0.001 ** | 0 | −0.0005 | 0.001 | −0.001 ** | 0.0005 |

Volatility _{Gold} | −0.060 * | 0.034 | −0.032 | 0.033 | −0.107 | 0.126 |

McFadden R^{2}(%) | 37.98 | 37.02 | 31.67 |

Model | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|

Dep. Variable | ${\mathit{r}}_{\mathit{S}\mathit{P}\mathit{Y}}^{\mathit{m}\mathit{i}\mathit{d}}$ | ${\mathit{r}}_{\mathit{G}\mathit{L}\mathit{D}}^{\mathit{m}\mathit{i}\mathit{d}}$ | ${\mathit{r}}_{\mathit{S}\mathit{P}\mathit{Y}}^{\mathit{m}\mathit{i}\mathit{d}}$ | ${\mathit{r}}_{\mathit{G}\mathit{L}\mathit{D}}^{\mathit{m}\mathit{i}\mathit{d}}$ | ${\mathit{r}}_{\mathit{S}\mathit{P}\mathit{Y}}^{\mathit{m}\mathit{i}\mathit{d}}$ | ${\mathit{r}}_{\mathit{G}\mathit{L}\mathit{D}}^{\mathit{m}\mathit{i}\mathit{d}}$ | ${\mathit{r}}_{\mathit{S}\mathit{P}\mathit{Y}}^{\mathit{m}\mathit{i}\mathit{d}}$ | ${\mathit{r}}_{\mathit{G}\mathit{L}\mathit{D}}^{\mathit{m}\mathit{i}\mathit{d}}$ |

${\mathit{D}}_{\mathit{j}\mathit{u}\mathit{m}\mathit{p},\mathit{S}\mathit{P}\mathit{Y}}$ | −0.004 | −0.0006 | ||||||

0.256 | 0.254 | |||||||

${\mathit{D}}_{\mathit{j}\mathit{u}\mathit{m}\mathit{p},\mathit{G}\mathit{L}\mathit{D}}$ | −0.007 | −0.008 | ||||||

0.007 | 0.008 | |||||||

${\mathit{D}}_{\mathit{j}\mathit{u}\mathit{m}\mathit{p},\mathit{S}\mathit{P}\mathit{Y}}^{\mathbf{+}}$ | −0.365 | |||||||

0.353 | ||||||||

${\mathit{D}}_{\mathit{j}\mathit{u}\mathit{m}\mathit{p},\mathit{S}\mathit{P}\mathit{Y}}^{\mathbf{-}}$ | 0.377 | |||||||

0.371 | ||||||||

${\mathit{D}}_{\mathit{j}\mathit{u}\mathit{m}\mathit{p},\mathit{G}\mathit{L}\mathit{D}}^{\mathbf{+}}$ | −0.009 | |||||||

0.009 | ||||||||

${\mathit{D}}_{\mathit{j}\mathit{u}\mathit{m}\mathit{p},\mathit{G}\mathit{L}\mathit{D}}^{\mathbf{-}}$ | −0.003 | |||||||

0.015 | ||||||||

${\mathit{D}}_{\mathit{c}\mathit{o}\mathit{j}\mathit{u}\mathit{m}\mathit{p}}$ | 0.009 | 0.009 ** | ||||||

0.897 | 0.897 | |||||||

$\mathit{O}{\mathit{F}}_{\mathit{j}+\mathbf{1},\mathit{S}\mathit{P}\mathit{Y}}$ | −0.0001 | −1.9 × 10^{−5} | −0.0001 | −0.0001 | −0.00001 | −2.9 × 10^{−5} | ||

0.0001 | 0.0001 | 6.78 × 10^{−5} | 0.0001 | 0.0001 | 0.0001 | |||

$\mathit{O}{\mathit{F}}_{\mathit{j}+\mathbf{1},\mathit{G}\mathit{L}\mathit{D}}$ | −0.0009 ** | −0.003 | −0.0008 | −0.0009 ** | −0.003 | −0.004 | ||

0.0004 | 0.003 | 0.0005 | 0.0004 | 0.003 | 0.003 | |||

$\mathit{O}{\mathit{F}}_{\mathit{j}+\mathbf{1}}{\mathit{D}}_{\mathit{j}\mathit{u}\mathit{m}\mathit{p},\mathit{S}\mathit{P}\mathit{Y}}$ | 0.023 * | 0.023 * | 0.033 *** | 0.022 * | ||||

0.013 | 0.013 | 0.011 | 0.012 | |||||

$\mathit{O}{\mathit{F}}_{\mathit{j}+\mathbf{1}}{\mathit{D}}_{\mathit{j}\mathit{u}\mathit{m}\mathit{p},\mathit{G}\mathit{L}\mathit{D}}$ | 0.307 *** | 0.263 ** | 0.298 *** | 0.263 ** | ||||

0.071 | 0.107 | 0.089 | 0.107 | |||||

$\mathit{O}{\mathit{F}}_{\mathit{j}+\mathbf{1}}{\mathit{D}}_{\mathit{j}\mathit{u}\mathit{m}\mathit{p},\mathit{S}\mathit{P}\mathit{Y}}^{\mathbf{+}}$ | 0.036 ** | 0.037 *** | ||||||

0.014 | 0.014 | |||||||

$\mathit{O}{\mathit{F}}_{\mathit{j}+\mathbf{1}}{\mathit{D}}_{\mathit{j}\mathit{u}\mathit{m}\mathit{p},\mathit{S}\mathit{P}\mathit{Y}}^{\mathbf{-}}$ | −0.008 | 0.002 | ||||||

0.022 | 0.019 | |||||||

$\mathit{O}{\mathit{F}}_{\mathit{j}+\mathbf{1}}{\mathit{D}}_{\mathit{j}\mathit{u}\mathit{m}\mathit{p},\mathit{G}\mathit{L}\mathit{D}}^{\mathbf{+}}$ | 0.509 *** | 0.564 *** | ||||||

0.101 | 0.14 | |||||||

$\mathit{O}{\mathit{F}}_{\mathit{j}+\mathbf{1}}{\mathit{D}}_{\mathit{j}\mathit{u}\mathit{m}\mathit{p},\mathit{G}\mathit{L}\mathit{D}}^{\mathbf{-}}$ | −2.552 *** | −3.076 ** | ||||||

0.901 | 1.31 | |||||||

Constant | 0.0012 | 0.001 *** | 0.004 | 0.001 ** | 0.001 | 0.001 | 0.004 | 0.005 |

0.002 | 0.001 | 0.003 | 0.0007 | 0.002 | 0.0006 | 0.003 | 0.003 | |

R (%)^{2} | 0.01 | 0.3 | 0.3 | 3.7 | 1.8 | 2.01 | 0.3 | 1.8 |

**Notes:**The table reports the estimates of Equation (10). The top row for each variable reports the coefficient estimates (Coef), and the bottom row reports the corresponding robust standard errors (S.E.) below. ***, **, * denote the 1%, 5%, and 10% significance levels, respectively.

© 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Jurdi, D.J.
Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds. *J. Risk Financial Manag.* **2020**, *13*, 118.
https://doi.org/10.3390/jrfm13060118

**AMA Style**

Jurdi DJ.
Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds. *Journal of Risk and Financial Management*. 2020; 13(6):118.
https://doi.org/10.3390/jrfm13060118

**Chicago/Turabian Style**

Jurdi, Doureige J.
2020. "Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds" *Journal of Risk and Financial Management* 13, no. 6: 118.
https://doi.org/10.3390/jrfm13060118