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An Empirical Investigation of Volatility Clustering, Volatility Spillover and Persistence from USA to Two Emerging Economies India and China

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Abstract

The issues of volatility and risk in recent times have gained importance for financial practitioners, market participants, regulators and researchers. Volatility is the most basic statistical risk measure instrument. This chapter empirically investigates the pattern of volatility in the Indian and Chinese stock markets during 2006–2011 with reference to its time varying nature, presence of certain characteristics such as volatility clustering and existence of ‘spillover effect’ in the domestic and the US stock markets. This chapter will also try to estimate the persistence of shock in terms of half-life in each sub-period of study. It contributes to the body of knowledge by providing a holistic outlook to the subject of stock market volatility in India and provides evidence on its main features with the help of econometric techniques employing GARCH models. A comparative analysis is made with the Chinese stock market taking Shanghai Composite Index (SCI).

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References

  • Aggarwal R, Tandon K (1994) Anomalies or illusions? Evidence from stock markets in eighteen countries. J Int Money Finan 13(1):83–106

    Article  Google Scholar 

  • Bollerslev T (1986) Generalised autoregressive conditional heteroskedasticity. J Econom 31(3):307–327

    Article  Google Scholar 

  • Booth GG, Martikainen T, Tse Y (1997) Price and volatility spillovers in Scandinavian stock markets. J Bank Finan 21(6):811–823

    Article  Google Scholar 

  • Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control, revised edn. Holden-Day, California

    Google Scholar 

  • Box G, Pierce D (1970) Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J Am Stat Assoc 65:1509–1526

    Article  Google Scholar 

  • Chaudhury SK (1991) Seasonality in share returns: preliminary evidence on day of the week effect. Chartered Accountant (India) 40(5):107–109

    Google Scholar 

  • Fama EF (1965) The Behaviour of Stock Market Prices. J Bus 38(1):34–105  

    Google Scholar 

  • Garman M, Klass M (1980) On the estimation of security price volatilities from historical data. J Bus 53:67–78

    Article  Google Scholar 

  • Hansda SK, Ray P (2002) BSE and Nasdaq: Globalisation, Information Technology and Stock Prices. Economic and Political Weekly, February 2, 459–468

    Google Scholar 

  • Hansda SK, Ray P (2003) Stock Market Integration and Dually Listed Stocks: Indian ADR and Domestic Stock Prices. Economic and Political Weekly, February 22, 741–753

    Google Scholar 

  • Kumar KK, Mukhopadhyay C (2002) A Case of US and India. Paper published as part of the NSE Research Initiative. http//:www.nseindia.com

  • Lamoureux Christopher G, Lastrapes William D (1990) Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects. J Financ 45(1):221–229

    Google Scholar 

  • Ljung G, Box G (1978) On a measure of lack of fit in time series models. Biometrika 66:67–72

    Google Scholar 

  • Lo AW, MacKinlay AC (1988) Stock market prices do not follow random walks: evidence from a simple specification test. Rev Financ Stud 1:41–66

    Google Scholar 

  • Mandelbrot B (1963) The Variation of Certain Speculative Prices. J Bus 36(4):394–419

    Google Scholar 

  • Miller MH (1991) Financial innovations and market volatility. Blackwell, pp 1–288

    Google Scholar 

  • Parkinson M (1980) The extreme value method for estimating the variance of the rate of return. J Bus 53:61–65

    Article  Google Scholar 

  • Poterba James M, Summers LH (1986) The Persistence of Volatility and Stock Market Fluctuations. Am Econ Rev 1142–1151

    Google Scholar 

  • Rao BSR, Naik U (1990) Inter-Relatedness of Stock Markets: Spectral investigation of USA, Japanese and Indian Markets – A Note. Arth Vignana 32(3&4):309–321

    Google Scholar 

  • Sharma JL, Kennedy RE (1977) A Comparative Analysis of Stock Price behavior on Bombay, London and New York Stock Exchange. J Financ Quant Anal 31(3):391–413

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Business Administration, Pailan College of Management & Technology, Kolkata, India

    Ayanangshu Sarkar

  2. Department of Economics, Jadavpur University, Kolkata, India

    Malabika Roy

Authors
  1. Ayanangshu Sarkar
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  2. Malabika Roy
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Corresponding author

Correspondence to Ayanangshu Sarkar .

Editor information

Editors and Affiliations

  1. Department of Economics, Jadavpur University, Jadavpur, Kolkata, India

    Malabika Roy

  2. Department of Economics, Jadavpur University, Jadavpur, Kolkata, West Bengal, India

    Saikat Sinha Roy

Appendices

Appendix 1

GARCH estimate for clustering and half-life calculation

1st period India

Dependent variable: RTINDIA

Sample: 4/03/2006–12/14/2007

Included observations: 422

GARCH = C(2) + C(3) * RESID(−1)2 + C(4) * GARCH(−1)

 

Coefficient

Std. error

z-Statistic

Prob.

C

0.098926

0.029144

3.394374

0.0007

Variance equation

C

0.026160

0.007093

3.688360

0.0002

RESID(−1)2

0.180966

0.040497

4.468580

0.0000

GARCH(−1)

0.775294

0.042797

18.11582

0.0000

R-squared

−0.003247

Mean dependent var

0.057862

Adjusted R-squared

−0.010447

S.D. dependent var

0.721531

GARCH 1st period China

Dependent variable: RTCHINA

Sample: 4/03/2006–12/14/2007

Included observations: 422

GARCH = C(2) + C(3) * RESID(−1)2 + C(4) * GARCH(−1)

 

Coefficient

Std. error

z-Statistic

Prob.

C

0.128874

0.036255

3.554664

0.0004

Variance equation

C

0.013697

0.004443

3.082556

0.0021

RESID(−1)2

0.061899

0.013156

4.704891

0.0000

GARCH(−1)

0.924676

0.012104

76.39616

0.0000

R-squared

−0.000406

Mean dependent var

0.145264

Adjusted R-squared

−0.007586

S.D. dependent var

0.814575

GARCH 2nd period India

Dependent variable: RTINDIA

Sample: 12/17/2007–4/01/2009

Included observations: 317

GARCH = C(2) + C(3) * RESID(−1)2 + C(4) * GARCH(−1)

 

Coefficient

Std. error

z-Statistic

Prob.

C

−0.058058

0.059031

−0.983521

0.3254

Variance equation

C

0.075185

0.039518

1.902566

0.0571

RESID(−1)2

0.131614

0.049000

2.686020

0.0072

GARCH(−1)

0.820384

0.060157

13.63744

0.0000

R-squared

−0.000696

Mean dependent var

−0.089802

Adjusted R-squared

−0.010288

S.D. dependent var

1.204988

GARCH 2nd period China

Dependent variable: RTCHINA

Sample: 12/17/2007–4/01/2009

Included observations: 317

GARCH = C(2) + C(3) * RESID(–1)2 + C(4) * GARCH(–1)

 

Coefficient

Std. error

z-Statistic

Prob.

C

−0.162750

0.028802

−5.650552

0.0000

Variance equation

C

0.035248

0.003274

10.76595

0.0000

RESID(−1)2

−0.040575

0.003209

−12.64421

0.0000

GARCH(−1)

1.018870

4.06E-05

25064.94

0.0000

R-squared

−0.001140

Mean dependent var

−0.123300

Adjusted R-squared

−0.010736

S.D. dependent var

1.170177

GARCH 3rd period India

Dependent variable: RTINDIA

Sample: 4/02/2009–3/31/2011

Included observations: 498

GARCH = C(2) + C(3) * RESID(−1)2 + C(4) * GARCH(−1)

 

Coefficient

Std. error

z-Statistic

Prob.

C

0.050708

0.022293

2.274645

0.0229

Variance equation

C

0.005644

0.002633

2.143564

0.0321

RESID(−1)2

0.118561

0.021685

5.467410

0.0000

GARCH(−1)

0.875519

0.022526

38.86757

0.0000

R-squared

−0.000017

Mean dependent var

0.053451

Adjusted R-squared

−0.006090

S.D. dependent var

0.656688

GARCH 3rd period China

Dependent variable: RTCHINA

Sample: 4/02/2009–3/31/2011

Included observations: 498

GARCH = C(2) + C(3) * RESID(−1)2 + C(4) * GARCH(−1)

 

Coefficient

Std. error

z-Statistic

Prob.

C

0.029746

0.031494

0.944487

0.3449

Variance equation

C

0.023166

0.011189

2.070427

0.0384

RESID(−1)2

0.042569

0.014948

2.847911

0.0044

GARCH(−1)

0.905456

0.035288

25.65900

0.0000

R-squared

−0.000043

Mean dependent var

0.025190

Adjusted R-squared

−0.006116

S.D. dependent var

0.699381

Appendix 2

EGARCH estimate For clustering and leverage effect

1st period

Dependent variable: RTINDIA

Sample: 4/03/2006–12/14/2007

Included observations: 422

LOG(GARCH) = C(2) + C(3) * ABS(RESID(−1)/@SQRT(GARCH(−1))) + C(4) * RESID(−1)/@SQRT(GARCH(−1)) + C(5) * LOG(GARCH(−1))

 

Coefficient

Std. error

z-Statistic

Prob.

C

0.069703

0.026230

2.657399

0.0079

Variance equation

  

C(2)

−0.373576

0.063735

−5.861376

0.0000

C(3)

0.322985

0.069004

4.680653

0.0000

C(4)

−0.262083

0.040088

−6.537610

0.0000

C(5)

0.864514

0.022603

38.24711

0.0000

R-squared

−0.000270

Mean dependent var

0.057862

Adjusted R-squared

−0.009865

S.D. dependent var

0.721531

Dependent variable: RTCHINA

Sample: 4/03/2006–12/14/2007

Included observations: 422

LOG(GARCH) = C(2) + C(3) * ABS(RESID(−1)/@SQRT(GARCH(-1))) + C(4) * RESID(−1)/@SQRT(GARCH(−1)) + C(5) * LOG(GARCH(−1))

 

Coefficient

Std. error

z-Statistic

Prob.

C

0.146602

0.030127

4.866224

0.0000

Variance equation

  

C(2)

−0.100532

0.018940

−5.308044

0.0000

C(3)

0.134752

0.027813

4.844927

0.0000

C(4)

0.056456

0.023697

2.382382

0.0172

C(5)

0.981920

0.006318

155.4087

0.0000

R-squared

−0.000003

Mean dependent var

0.145264

Adjusted R-squared

−0.009595

S.D. dependent var

0.814575

2nd period

Dependent variable: RTINDIA

Sample: 12/17/2007–4/01/2009

Included observations: 317

LOG(GARCH) = C(2) + C(3) * ABS(RESID(−1)/@SQRT(GARCH(−1))) + C(4)*RESID(−1)/@SQRT(GARCH(−1)) + C(5) * LOG(GARCH(−1))

 

Coefficient

Std. error

z-Statistic

Prob.

C

−0.102140

0.059328

−1.721614

0.0851

Variance equation

C(2)

−0.110166

0.060657

−1.816224

0.0693

C(3)

0.152771

0.075284

2.029272

0.0424

C(4)

−0.125305

0.028927

−4.331755

0.0000

C(5)

0.955006

0.020300

47.04521

0.0000

R-squared

−0.000105

Mean dependent var

−0.089802

Adjusted R-squared

−0.012927

S.D. dependent var

1.204988

Dependent variable: RTCHINA

Sample: 12/17/2007–4/01/2009

Included observations: 317

LOG(GARCH) = C(2) + C(3) * ABS(RESID(−1)/@SQRT(GARCH(−1))) + C(4) * RESID(−1)/@SQRT(GARCH(−1)) + C(5) * LOG(GARCH(−1))

 

Coefficient

Std. error

z-Statistic

Prob.

C

−0.136118

0.053393

−2.549352

0.0108

Variance equation

C(2)

0.067374

0.008209

8.207774

0.0000

C(3)

−0.073897

0.005684

−13.00042

0.0000

C(4)

−0.059393

0.021711

−2.735566

0.0062

C(5)

0.968566

0.005465

177.2196

0.0000

R-squared

−0.000120

Mean dependent var

−0.123300

Adjusted R-squared

−0.012942

S.D. dependent var

1.170177

3rd PERIOD

Dependent variable: RTINDIA

Sample: 4/02/2009–3/31/2011

Included observations: 498

LOG(GARCH) = C(2) + C(3) * ABS(RESID(−1)/@SQRT(GARCH(−1))) + C(4) * RESID(−1)/@SQRT(GARCH(−1)) + C(5) * LOG(GARCH(−1))

 

Coefficient

Std. error

z-Statistic

Prob.

C

0.035054

0.021637

1.620078

0.1052

Variance equation

C(2)

−0.212070

0.036652

−5.785965

0.0000

C(3)

0.257810

0.041170

6.262093

0.0000

C(4)

−0.102913

0.030323

−3.393880

0.0007

C(5)

0.987444

0.008039

122.8386

0.0000

R-squared

−0.000786

Mean dependent var

0.053451

Adjusted R-squared

−0.008906

S.D. dependent var

0.656688

Dependent variable: RTCHINA

Sample: 4/02/2009–3/31/2011

Included observations: 498

LOG(GARCH) = C(2) + C(3) * ABS(RESID(−1)/@SQRT(GARCH(−1))) + C(4) * RESID(−1)/@SQRT(GARCH(−1)) + C(5) * LOG(GARCH(−1))

 

Coefficient

Std. error

z-Statistic

Prob.

C

0.028783

0.031867

0.903230

0.3664

Variance equation

C(2)

−0.179784

0.069434

−2.589290

0.0096

C(3)

0.077045

0.048245

1.596959

0.1103

C(4)

−0.120139

0.040140

−2.993006

0.0028

C(5)

0.846203

0.059197

14.29467

0.0000

R-squared

−0.000026

Mean dependent var

0.025190

Adjusted R-squared

−0.008140

S.D. dependent var

0.699381

Appendix 3

EGARCH estimate for Spillover

1st sub-period

Dependent variable: RTINDIA

Sample: 4/03/2006–12/14/2007

Included observations: 422

LOG(GARCH) = C(3) + C(4) * ABS(RESID(−1)/@SQRT(GARCH(−1))) + C(5) * RESID(−1)/@SQRT(GARCH(−1)) + C(6) * LOG(GARCH(−1))

 

Coefficient

Std. error

z-Statistic

Prob.

C

0.078013

0.024172

3.227418

0.0012

RTUSA

0.160136

0.061943

2.585198

0.0097

Variance equation

C(3)

−0.352640

0.083664

−4.214963

0.0000

C(4)

0.314144

0.090026

3.489479

0.0005

C(5)

−0.264987

0.056315

−4.705423

0.0000

C(6)

0.892383

0.029893

29.85266

0.0000

GED parameter

1.341309

0.132189

10.14687

0.0000

R-squared

0.013994

Mean dependent var

0.057862

Adjusted R-squared

−0.000262

S.D. dependent var

0.721531

S.E. of regression

0.721626

Akaike info criterion

1.860797

Dependent variable: RTCHINA

Sample: 4/03/2006–12/14/2007

Included observations: 422

LOG(GARCH) = C(3) + C(4) * ABS(RESID(−1)/@SQRT(GARCH(−1))) + C(5) * RESID(−1)/@SQRT(GARCH(−1)) + C(6) * LOG(GARCH(−1))

 

Coefficient

Std. error

z-Statistic

Prob.

C

0.152740

0.026043

5.864823

0.0000

RTUSA

0.105099

0.073655

1.426903

0.1536

Variance equation

C(3)

−0.153487

0.051721

−2.967602

0.0030

C(4)

0.192909

0.071931

2.681846

0.0073

C(5)

0.009560

0.049847

0.191793

0.8479

C(6)

0.958284

0.023377

40.99332

0.0000

GED parameter

1.049438

0.095773

10.95762

0.0000

R-squared

0.001252

Mean dependent var

0.145264

Adjusted R-squared

−0.013188

S.D. dependent var

0.814575

S.E. of regression

0.819929

Akaike info criterion

2.221829

2nd sub-period

Dependent variable: RTINDIA

Sample: 12/17/2007–4/01/2009

Included observations: 317

LOG(GARCH) = C(3) + C(4)*ABS(RESID(−1)/@SQRT(GARCH(−1))) + C(5) * RESID(−1)/@SQRT(GARCH(−1)) + C(6) * LOG(GARCH(−1))

 

Coefficient

Std. error

z-Statistic

Prob.

C

−0.105695

0.060773

−1.739173

0.0820

RTUSA

−0.045623

0.063101

−0.723027

0.4697

Variance equation

C(3)

−0.106505

0.063488

−1.677548

0.0934

C(4)

0.147896

0.079577

1.858535

0.0631

C(5)

−0.129890

0.030174

−4.304656

0.0000

C(6)

0.954788

0.022615

42.21920

0.0000

GED parameter

1.944783

0.245918

7.908260

0.0000

R-squared

−0.004109

Mean dependent var

−0.089802

Adjusted R-squared

−0.023544

S.D. dependent var

1.204988

Dependent variable: RTCHINA

Sample: 12/17/2007–4/01/2009

Included observations: 317

LOG(GARCH) = C(3) + C(4) * ABS(RESID(−1)/@SQRT(GARCH(−1))) + C(5) * RESID(−1)/@SQRT(GARCH(−1)) + C(6) * LOG(GARCH(−1))

 

Coefficient

Std. error

z-Statistic

Prob.

C

−0.096887

0.046488

−2.084100

0.0372

RTUSA

−0.097534

0.065605

−1.486692

0.1371

Variance equation

C(3)

0.075368

5.38E−08

1400563.

0.0000

C(4)

−0.088226

0.004521

−19.51454

0.0000

C(5)

−0.066091

0.029370

−2.250329

0.0244

C(6)

0.969752

0.005868

165.2748

0.0000

GED parameter

1.462581

0.180404

8.107258

0.0000

R-squared

−0.001916

Mean dependent var

−0.123300

Adjusted R-squared

−0.021307

S.D. dependent var

1.170177

3rd Sub-period

Dependent variable: RTINDIA

Sample (adjusted): 4/02/2009–3/24/2011

Included observations: 493 after adjustments

LOG(GARCH) = C(3) + C(4) * ABS(RESID(−1)/@SQRT(GARCH(−1))) + C(5) * RESID(−1)/@SQRT(GARCH(−1)) + C(6) * LOG(GARCH(−1))

 

Coefficient

Std. error

z-Statistic

Prob.

C

0.040485

0.020647

1.960833

0.0499

RTUSA

−0.027055

0.040936

−0.660907

0.5087

Variance equation

C(3)

−0.198975

0.055644

−3.575880

0.0003

C(4)

0.222863

0.059845

3.723999

0.0002

C(5)

−0.115798

0.041855

−2.766615

0.0057

C(6)

0.977461

0.015055

64.92544

0.0000

GED parameter

1.362674

0.090024

15.13686

0.0000

R-squared

−0.003205

Mean dependent var

0.053993

Adjusted R-squared

−0.015590

S.D. dependent var

0.659994

Dependent variable: RTCHINA

Sample: 4/02/2009–3/31/2011

Included observations: 498

LOG(GARCH) = C(3) + C(4) * ABS(RESID(−1)/@SQRT(GARCH(−1))) + C(5) * RESID(−1)/@SQRT(GARCH(−1)) + C(6) * LOG(GARCH(−1))

 

Coefficient

Std. error

z-Statistic

Prob.

C

0.070085

0.026711

2.623857

0.0087

RTUSA

−0.091560

0.047075

−1.944979

0.0518

Variance equation

C(3)

−0.219788

0.110867

−1.982449

0.0474

C(4)

0.016437

0.086411

0.190224

0.8491

C(5)

−0.202779

0.077343

−2.621820

0.0087

C(6)

0.749673

0.109840

6.825106

0.0000

GED parameter

1.262590

0.114267

11.04950

0.0000

R-squared

−0.003006

Mean dependent var

0.025190

Adjusted R-squared

−0.015262

S.D. dependent var

0.699381

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Sarkar, A., Roy, M. (2016). An Empirical Investigation of Volatility Clustering, Volatility Spillover and Persistence from USA to Two Emerging Economies India and China. In: Roy, M., Sinha Roy, S. (eds) International Trade and International Finance. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2797-7_20

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