Daiki Maki

578 total citations · 1 hit paper
25 papers, 420 citations indexed

About

Daiki Maki is a scholar working on Economics and Econometrics, General Economics, Econometrics and Finance and Finance. According to data from OpenAlex, Daiki Maki has authored 25 papers receiving a total of 420 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Economics and Econometrics, 19 papers in General Economics, Econometrics and Finance and 13 papers in Finance. Recurrent topics in Daiki Maki's work include Monetary Policy and Economic Impact (19 papers), Market Dynamics and Volatility (10 papers) and Financial Risk and Volatility Modeling (9 papers). Daiki Maki is often cited by papers focused on Monetary Policy and Economic Impact (19 papers), Market Dynamics and Volatility (10 papers) and Financial Risk and Volatility Modeling (9 papers). Daiki Maki collaborates with scholars based in Japan and China. Daiki Maki's co-authors include Yu Jiang and has published in prestigious journals such as Economics Letters, Economic Modelling and Mathematics and Computers in Simulation.

In The Last Decade

Daiki Maki

20 papers receiving 397 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Tests for cointegration allowing for an unknown number of breaks

2012 314 citations Daiki Maki Economic Modelling profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daiki Maki Japan	8	354	186	124	78	36	25	420
Shabir Mohsin Hashmi China	12	481 1.4×	117 0.6×	183 1.5×	77 1.0×	40 1.1×	22	546
Vladimir Arčabić Croatia	9	382 1.1×	185 1.0×	107 0.9×	99 1.3×	22 0.6×	29	465
Gawon Yoon South Korea	8	375 1.1×	226 1.2×	85 0.7×	109 1.4×	20 0.6×	37	444
Hüseyin Kaya Türkiye	8	338 1.0×	182 1.0×	121 1.0×	58 0.7×	28 0.8×	25	421
Natalya Ketenci Türkiye	11	346 1.0×	163 0.9×	125 1.0×	87 1.1×	46 1.3×	32	428
Selim Kayhan Türkiye	10	349 1.0×	130 0.7×	159 1.3×	70 0.9×	96 2.7×	34	414
Mark A. Thoma United States	8	381 1.1×	199 1.1×	131 1.1×	99 1.3×	47 1.3×	19	440
Christian Gengenbach Netherlands	6	306 0.9×	156 0.8×	53 0.4×	56 0.7×	16 0.4×	7	339
Issouf Samaké United States	12	237 0.7×	127 0.7×	68 0.5×	46 0.6×	24 0.7×	27	304
Artūras Juodis Netherlands	9	392 1.1×	94 0.5×	94 0.8×	30 0.4×	45 1.3×	16	475

Countries citing papers authored by Daiki Maki

Since Specialization

Citations

This map shows the geographic impact of Daiki Maki's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daiki Maki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daiki Maki more than expected).

Fields of papers citing papers by Daiki Maki

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daiki Maki. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daiki Maki. The network helps show where Daiki Maki may publish in the future.

Co-authorship network of co-authors of Daiki Maki

This figure shows the co-authorship network connecting the top 25 collaborators of Daiki Maki. A scholar is included among the top collaborators of Daiki Maki based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daiki Maki. Daiki Maki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Maki, Daiki. (2024). Forecasting downside and upside realized volatility: The role of asymmetric information. The Journal of Economic Asymmetries. 29. e00357–e00357.

2.

Maki, Daiki. (2024). Asymmetric effect of trading volume on realized volatility. International Review of Economics & Finance. 94. 103388–103388.

3.

Maki, Daiki. (2023). Asymmetric Effect of Trading Volume on Realized Volatility. SSRN Electronic Journal. 1 indexed citations

4.

Maki, Daiki, et al.. (2021). Impacts of asymmetry on forecasting realized volatility in Japanese stock markets. Economic Modelling. 101. 105533–105533. 6 indexed citations

5.

Maki, Daiki. (2015). Wild bootstrap testing for cointegration in an ESTAR error correction model. Economic Modelling. 47. 292–298. 2 indexed citations

6.

Maki, Daiki. (2014). Wild bootstrap tests for unit root in ESTAR models. Statistical Methods & Applications. 24(3). 475–490. 2 indexed citations

7.

Maki, Daiki, et al.. (2014). Residual-based tests for cointegration with three-regime TAR adjustment. Empirical Economics. 48(3). 1013–1054. 9 indexed citations

8.

Maki, Daiki. (2012). Detecting cointegration relationships under nonlinear models: Monte Carlo analysis and some applications. Empirical Economics. 45(1). 605–625. 5 indexed citations

9.

Maki, Daiki. (2011). Pitfalls in Estimating Cointegrating Vector when Cointegration Relationship has Nonlinear Adjustment. Communications in Statistics - Simulation and Computation. 40(8). 1111–1121. 2 indexed citations

10.

Maki, Daiki. (2011). The influence of heteroskedastic variances on cointegration tests: A comparison using Monte Carlo simulations. Computational Statistics. 28(1). 179–198. 2 indexed citations

11.

Maki, Daiki. (2009). Tests for a Unit Root Using Three-Regime TAR Models: Power Comparison and Some Applications. Econometric Reviews. 28(4). 335–363. 7 indexed citations

12.

Maki, Daiki. (2009). An alternative procedure to test for cointegration in STAR models. Mathematics and Computers in Simulation. 80(5). 999–1006. 9 indexed citations

13.

Maki, Daiki, et al.. (2009). Limit Cycles in Japanese Macroeconomic Data: Policy Implications from he View of Business Cycles. 4(1). 37–54.

14.

Maki, Daiki. (2007). The size performance of a nonparametric unit root test under a variance shift. Statistics & Probability Letters. 78(6). 743–748. 2 indexed citations

15.

Maki, Daiki. (2007). The Performance of Variance Ratio Unit Root Tests Under Nonlinear Stationary TAR and STAR Processes: Evidence from Monte Carlo Simulations and Applications. Computational Economics. 31(1). 77–94. 1 indexed citations

16.

Maki, Daiki. (2006). Non-linear adjustment in the term structure of interest rates: a cointegration analysis in the non-linear STAR framework. Applied Financial Economics. 16(17). 1301–1307. 10 indexed citations

17.

Maki, Daiki, et al.. (2006). The equilibrium relationship among money, income, prices, and interest rates: evidence from a threshold cointegration test. Applied Economics. 38(13). 1585–1592. 19 indexed citations

18.

Maki, Daiki. (2005). The term structure of interest rates with nonlinear adjustment: Evidence from a unit root test in the nonlinear STAR framework. Economics bulletin. 3(6). 1–7. 3 indexed citations

19.

Maki, Daiki. (2005). Asymmetric adjustment of the equilibrium relationship between the nominal interest rate and inflation rate. Economics bulletin. 3(9). 1–8. 9 indexed citations

20.

Maki, Daiki. (2003). Nonparametric cointegration analysis of the nominal interest rate and expected inflation rate. Economics Letters. 81(3). 349–354. 15 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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