• No results found

Hypothese 2: Orkanen hebben een meer significant effect op de rendementen van de

7. Conclusie

7.1 Aanbevelingen voor verder onderzoek

In dit onderzoek werd een concreet beeld geschept van de effecten van extreme weersomstandigheden in de Verenigde Staten op de S&P 500 gedurende de periode 2010-2019. Door de keuze voor een event study werd de onderzochte hoeveelheid extreme weersomstandigheden beperkt tot rampen die gedurende een relatief korte periode

plaatsvonden. Derhalve kan het nuttig zijn om via een andere methodologie ook de effecten van langdurige weersgerelateerde rampen zoals hittegolven en bosbranden na te gaan. Verder kan het bestuderen van een andere periode een nuttige aanvulling vormen op dit onderzoek, om op deze manier in een vergelijkend onderzoek na te gaan hoe de impact van extreme weersomstandigheden varieert over de tijd heen. Tot slot kan dit onderzoek worden uitgebreid naar andere gebieden en aandelenindexen.

8.BIBLIOGRAFIE

Addoum, J. M., Ng, D. T., & Bobea-Ortiz, A. (2018). Temperature Shocks and Earnings News. New York : Cornell University.

Anttila-Hughes, J. (2016). Financial Market Response to Extreme Events Indicating Climatic Change. (E. Sciences, Red.) The European Physical Journal Special Topics, 527-538. doi:10.1140/epjst/e2015-50098-6

Apergis, N., & Gupta, R. (2017). Can (unusual) weather conditions in New York predict South African stock returns? Research in International Business and Finance, 377-386. doi:dx.doi.org/10.1016/j.ribaf.2017.04.052

Armitage, S. (1995, March). Event Study Methods and Evidence on their Performance. Journal of Economic Surveys(Volume 9, Issue 1 ), 25-52. doi: https://doi.org/10.1111/j.1467- 6419.1995.tb00109.x

BBC. (2020, January 7). How did Australia fires start and what is being done? A very simple guide. Opgehaald van BBC News: https://www.bbc.com/news/world-australia-50980386 Berz, G. (1993). Views of the insurance industry. Socio-Economic and Policy Aspects of Changes

in the Incidence and Intensity of Extreme Events. University of Vrije: Institute for Environmental Studies.

Binder, J. J. (1998). The Event Study Methodology Since 1969. Review of Quantitative Finance and Accounting(11), 111-137.

Blume, M. E., & Friend, I. (1973, March). A New Look at the Capital Asset Pricing Model. The Journal of Finance(Volume 28, Number 1), 19-33.

Bodie, Z., Kane, A., & Marcus, A. J. (2019). Essentials of Investments: Eleventh Edition. New York: McGraw-Hill Education.

Born, P., & Viscusi, W. (2006). The catastrophic effects of natural disasters on insurance market. J Risk Uncertainty(33), 55-72. doi:DOI 10.1007/s11166-006-0171-z

Botzen, W. W., Deschenes, O., & Sanders, M. (2019). The Economic Impacts of Natural Disasters: A Review of Models and Empirical Studies. Review of Environmental Economics and Policy(Volume 13, Issue 2 ), 167-188. doi:doi: 10.1093/reep/rez004 Botzen, W., van den Bergh, J., & Bouwer, L. (2010). Climate Change and increased risk for the

insurance sector: a global perspective and an assessment for the Netherlands. Natural Hazards, 577-598. doi: 10.1007/s11069-009-9404-1

Bouwer, L. M. (2013). Projections of Future Extreme Weather Losses Under Changes in Climate and Exposure. Risk Analysis, 915-930. doi:DOI: 10.1111/j.1539-6924.2012.01880.x Bowman, D. M., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M., Cochrane, M. A., & Et al.

(2009, April 24). Fire in the Earth System. Science, pp. 481-484.

Brooks, C. (2019). Introductory Econometrics for Finance. Cambridge: Cambridge University Press.

Brown, P. J., Bradley, R. S., & Keimig, T. F. (2010). Changes In Extreme Climate Indices for the Northeastern United States, 1870-2005. Journal of Climate, 6555-6572.

Brown, S., & Warner, J. (1980). Measuring security price performance. Journal of Financial Economics, 3-31.

Brown, S., & Warner, J. (1980). Measuring Security Price Performance. Journal of Financial Economics, 3-31.

Cambridge Centre for Risk Studies. (2018). Impacts of Severe Natural Catastrophes on Financial Markets. Cambridge, United Kingdom: Cambridge Centre for Risk Studies.

Cao, M., & Wei , J. (2004). Weather Derivatives Valuation and Market Price of Weather Risk. The Journal of Futures Markets(Volume 24, Number 11), 1065-1089. doi:DOI: 10.1002/fut.2012

Cao, M., & Wei , J. (2005). Stock market returns: A note on temperature anomaly. Journal of Banking and Finance, 1559-1573. doi:doi:10.1016/j.jbankfin.2004.06.028

Centre for Research on the Epidemiology of Disasters - CRED. (2009). EM-DAT: International Disaster Database. School of Public Health - Université catholique de Louvain, Brussels, Belgium.

Centre for Research on the Epidemiology of Disasters (CRED). (2006). EM-DAT. Université catholique de Louvain, Brussels, Belgium.

Changnon, S. A., Changnon, D., Fosse, E., Hoganson, D. C., Roth Sr., R. J., & Totsch, J. M. (1996). Effects of Recent Weather Extremes on the Insurance Industry: Major Implications for the Atmospheric Sciences. Bulletin of the American Meteorological Society, 425-435. Corrado, C. J. (2010). Event Studies: a Methodology Review. Accounting & Finance(Volume 51,

Issue 1), 207-234.

Coumou, D., & Rahmstorf, S. (2012). A decade of weather extremes. Nature Climate Change, 491-496.

Decleir, J., & Bleys, B. (2019). The link between carbon capture and storage (CSS) and stranded assets: an event study methodology. Universiteit Gent: Faculteit Economie en Bedrijfskunde. Opgehaald van https://lib.ugent.be/catalog/rug01:002790789

Dehullu, Y., Loosvelt, P., Stieperaere, H., & Disli, M. (2018). Impact van terrorisme op de financiële markten: hebben recente terroristische aanslagen een impact op de financiële markten? Gent: Universiteit Gent.

Desot, M., De Potter, K., Inghelbrecht , K., & Dierick, N. (2019). The Impact of Donald Trump on the Stock Market: an Event Study using Google Trends Search Values. Universiteit Gent. Gent: Universiteit Gent.

Dimson, E., & Mussavian, M. (1998). A brief history of market efficiency. European Financial Management(Vol. 4, No. 1 ), 91-103.

Europese Commissie. (z.d.). De gevolgen van de klimaatverandering. Opgehaald van ec.europa.eu: https://ec.europa.eu/clima/change/consequences_nl

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 383-417.

Fama, E. F., & French, K. R. (2004). The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives(Volume 18, Number 3), 25-46.

Feria-Domínguez, J. M., Paneque, P., & Gil-Hurtado, M. (2017). Risk Perceptions on Hurricanes: Evidence from the U.S. Stock Market. International Journal of Environmental Research and Public Health, 14, 600. doi:https://doi.org/10.3390/ijerph14060600

Flannery, M., & Protopapadakis, A. (2002). Macroeconomic factors do influence aggregate stock returns. The review of financial studies. Oxford: Oxford University Press, 751-782. Frühwirth, M., & Sögner, L. (2015). Weather and SAD related mood effects on the financial

market. The Quarterly Review of Economics and Finance, 11-31. doi:dx.doi.org/10.1016/j.qref.2015.02.003

Gabrielsen, A., Apergis, N., & Smales, L. A. (2016). (Unusual) weather and stock returns - I am not in the mood for mood: further evidence from international markets. Financial Markets and Portfolio Management, 63-94. doi:10.1007/s11408-016-0262-z

Haldar, I. (2010). Global Warming: The Causes and Consequences. In I. Haldar, Global Warming: The Causes and Consequences (p. 190). n.p.: Mind Melodies.

Hewitt, E. (2012). Examining Market Response Following Hurricane Landfall: Does the U.S. Stock Market React Efficiently to Hurricanes? . Economic Honors Thesis, Macalester College, St. Paul, MN, USA.

Higuera, P. E. (2015). Taking time to consider the causes and consequences of large wildfires. PNAS, 13137-13138.

Hirshleifer, D., & Shumway, T. (2003). Good Day Sunshine: Stock Returns and the Weather. The Journal of Finance, 1009-1032.

Intergovernemental Panel on Climate Change (IPCC). (n.d.). About the IPCC. Opgehaald van IPCC: https://www.ipcc.ch/about/

Intergovernemental Panel on Climate Change. (2018). Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and. n.p.: In Press.

Israel, D. C., & Briones, R. M. (2012). mpacts of Natural Disasters on Agriculture, Food Security, and Natural Resources and Environment in the Philippines, PIDS Discussion Paper Series, No. 2012-36. Makati City, Philippines: Philippine Institute for Development Studies (PIDS).

Jareño, F., & Negrut, L. (2016). US Stock Market and Macroeconomic Factors . The Journal of Applied Business Research, 325-340.

Kamara, S. (2012). The effects of antitakeover provisions on shareholders wealth. Tilbury University, Department of Finance: Tilburg University.

Karl, T., Knight, R., Easterling, D., & Quayle, R. (1996). Indices of Climate Change for the United States. Bulletin of the American Meteorological Society(77), 279-292.

King, B. (1966). Market and industry factors in stock price behaviour. Journal of business, 139. Kousky, C. (2014). Informing climate adaptation: a review of the economic costs of natural

disasters. Energy Economics(46), 576-592.

Lee, Y.-M., & Wang, K.-M. (2011). The effectiveness of the sunshine effect in Taiwan's stock market before and after the 1997 financial crisis. Economic Modelling, 710-727.

Lesk, C., Rowhani, P., & Ramankutty, N. (2016). Influence of extreme weather disasters on global crop production. Nature(Voume 529), 84-87. doi:doi:10.1038/nature16467

Lockamy, A. (2014). Assessing disaster risks in supply chains. Industrial Management & Data Systems(Volume 114, Number 5), 755-777. doi:DOI 10.1108/IMDS-11-2013-0477 MacKinlay, A. (1997). Event Studies in Economics and Finance. Journal of Economic Literature,

13-39.

Malkiel, B. G. (1989). Efficient Market Hypothesis. London: Palgrave Macmillan.

Malkiel, B. G. (2003). The Efficient Market Hypothesis and Its Critics. Journal of Economic Perspectives, 59-82.

Marlon, J. R., Bartlein, P. J., Gavin, G. D., Long, C. J., Anderson, R. S., Briles, C. E., . . . Walsh, M. K. (2012). Long-term perspective on wildfires in the western USA. PNAS PLUS, E535- E543.

McWilliams , A., & Siegel, D. (1997). Event studies in management research: Theoretical and empirical issues. Academy of management journal(Volume 40, Number 3), 626-657.

Morgan Stanley Capital International (MSCI). (2020, n.d. n.d.). MSCI World Index (USD). Opgehaald van msci.com: https://www.msci.com/documents/10199/178e6643-6ae6- 47b9-82be-e1fc565ededb

Morgan Stanley Capital International (MSCI). (n.d.). MSCI ACWI Index. Opgehaald van MSCI: https://www.msci.com/acwi

National Oceanic and Atmospheric Administration (NOAA). (2020, n.d. n.d.). U.S. Billion-Dollar Weather and Climate Disasters. Opgehaald van National Centers for Environmental Information (NCEI): https://www.ncdc.noaa.gov/billions/

National Oceanic and Atmospheric Administration (NOAA). (n.d.). Billion-Dollar Weather and

Climate Disasters: Time Series. Opgehaald van NOAA:

https://www.ncdc.noaa.gov/billions/time-series

National Oceanic and Atmospheric Administration. (2020, n.d. n.d.). U.S. Climate Extremes Index (CEI). Opgehaald van National Centers for Environmental Information: https://www.ncdc.noaa.gov/extremes/cei/definition

NOAA National Climatic Data Center (NCDC). (sd). Global Component of Climate at a Glance (GCAG).

OECD. (2020, february 10). Trade in Goods and Services. Opgehaald van OECD Data: https://data.oecd.org/trade/trade-in-goods-and-services.htm

Parmentier, N. (2018). De invloed van belastingontwijking op Europese aandelen: een studie in het kader van de luxleaks. Gent: Universiteit Gent.

Perold, F. A. (2004). The Capital Asset Pricing Model. Journal of Economic Perspectives(Volume 18, Number 3), 3-24.

Peterson, P. P. (1989). Event Studies: A review of Issues and Methodology. Quarterly Journal of Business and Economics(Volume 28, Number 3 ), 36-66.

Radinovic, D., & Curic, M. (2012). Criteria for heat and cold wave duration indexes. Theoretical and Applied Climatology, 505-510.

Refinitiv. (n.d.). TRBC Sector Classification. Opgehaald van Refinitiv: https://www.refinitiv.com/en/financial-data/indices/trbc-business-classification

Refinitiv. (n.d., n.d. n.d.). Why Choose Eikon? Opgehaald van Refinitiv: https://www.refinitiv.com/en/products/eikon-trading-software

Roberts, H. (1967). Statistical versus clinical prediction of the stock market. Centre for Research inSecurity Prices, University of Chicago: Unpublished manuscript.

Robinson, P. J. (2000). On the Definition of a Heatwave. Journal of Applied Meteorology, 762- 775.

Rosenberg, B. (1981). The capital asset pricing model and the market model. Journal of Portfolio Management, 5-16. doi: https://doi.org/10.3905/jpm.1981.408793

S&P . (2020, February 7). S&P500. Opgehaald van S&P Dow Jones Indices: https://us.spindices.com/indices/equity/sp-500

Sariannidis, N., Giannarakis, G., & Partalidou, X. (2016). The effect of weather on the European Stock Market. International Journal of Social Economics, 943-958. doi:dx.doi.org/10.1108/IJSE-04-2015-0079

Sasaki, H., Yamaguchi, S., & Hisada, T. (2000). The globalisation of financial markets and monetary policy. International financial markets and the implications for monetary and financial stability (pp. 57-78). Basel, Switzerland: Bank for international Settlements. Seneviratne, S. I., Nicholls, N., Easterling, D., Goodess, C. M., Kanae, S., Kossin, J., . . . Zhang,

X. (2012). Changes in Climate Extremes and their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Intergovernemental Panel on Climate Change. Cambridge, UK and New York, USA: Cambridge University Press.

Sharpe, W. F. (1977). Financial Decision Making under Uncertainty (Vol. The Capital Asset Pricing Model: a "Multi-Beta" Interpretation). Stanford University: Elsevier. doi:https://doi.org/10.1016/C2013-0-04991-2

Sirucek, M. (2012). Macroeconomic variables and stock market: US review. International Journal of Computer Science and Management Studies, MPRA Paper 39094.

Smith, A. B., & Katz, R. W. (2013). U.S. Billion-dollar Weather and Climate Disasters: Data Sources, Trends, Accuracy and Biases. Natural Hazards, 387–410.

United Nations. (2020, January 22). Ten impacts of the Australian bushfires. Opgehaald van UN Environment Programme : https://www.unenvironment.org/news-and-stories/story/ten- impacts-australian-bushfires

Van Veer, T., Inghelbrecht, K., & Meir, J. (2019). De impact van sociale media op aandelen. Gent, Oost-Vlaanderen, België: Universiteit Gent.

Wolfgang, K., Löw, P., & Kundzewics, Z. W. (2019). Changes in risk of extreme weather events in Europe. Environmental Science and Policy, 74-83.

9. APPENDIX

ROBUUSTHEIDSCONTROLE – CONSTANT MEAN MODEL

HYPOTHESE 1: EXTREME WEERSOMSTANDIGHEDEN LEIDEN TOT AFWIJKENDE RENDEMENTEN OP DE S&P 500 INDEX

Aantal dagen ten opzichte van het event

Gemiddelde Abnormal Return (AR)

T-statistiek gemiddelde Abnormal Return (AR)

-1 -0,0029% -0,0176 0 0,0191% 0,1155 1 0,0188% 0,1137 2 0,0391% 0,2366 3 0,0018% 0,0108 4 -0,0130% -0,0785 5 -0,1356% -0,8198 6 -0,2358% -1,4260 7 0,0788% 0,4763 8 -0,1851% -1,1192 9 0,0299% 0,1809 10 -0,2722% -1,6463

Tabel 29: Gemiddelde AR over alle events voor de S&P 500 – constant mean model

HYPOTHESE 2: ORKANEN HEBBEN EEN MEER SIGNIFICANT EFFECT OP DE RENDEMENTEN VAN DE S&P 500

Beschrijving event CAR T-waarde

Orkaan Michael

(10/10/2018 – 11/10/2018) 9,0478% -4,4095

Tabel 30: Significante events doorheen de tijd voor de S&P 500: focus op orkanen – constant mean model

Aantal dagen ten opzichte van het event

Gemiddelde Abnormal Return (AR)

T-statistiek gemiddelde Abnormal Return (AR)

-1 -0,1904% -0,6761

1 -0,2120% -0,7527 2 -0,0917% -0,3257 3 0,3840% 1,3638 4 0,1839% 0,6532 5 -0,7195% -2,5552 6 -0,2864% -1,0171 7 0,6229% 2,2122 8 0,0344% 0,1220 9 -0,2705% -0,9606 10 -0,0991% -0,3519

Tabel 31: Gemiddelde AR over alle events binnen de S&P 500: focus op orkanen – constant mean model

HYPOTHESE 3: EXTREME WEERSOMSTANDIGHEDEN HEBBEN EEN VERSCHILLEND EFFECT OP DE VERSCHILLENDE SECTOREN VAN DE S&P 500

DE VERZEKERINGSSECTOR

Aantal dagen ten opzichte van het event

Gemiddelde Abnormal Return (AR)

T-statistiek gemiddelde Abnormal Return (AR)

-1 -0,1169% -0,6005 0 -0,0184% -0,0944 1 -0,1715% -0,8811 2 0,0059% 0,0304 3 0,0477% 0,2449 4 -0,0470% -0,2414 5 -0,1041% -0,5351 6 -0,2232% -1,1468 7 0,1800% 0,9250 8 -0,5301% -2,7244 9 0,1432% 0,7358 10 -0,2630% -1,3514

DE ENERGIESECTOR

Aantal dagen ten opzichte van het event

Gemiddelde Abnormal Return (AR)

T-statistiek gemiddelde Abnormal Return (AR)

-1 0,5228% 1,7948 0 0,1501% 0,5152 1 0,1720% 0,5906 2 -0,1758% -0,6037 3 0,0022% 0,0076 4 0,2822% 0,9690 5 -0,3455% -1,1861 6 -0,3702% -1,2710 7 0,3047% 1,0460 8 -0,0225% -0,0774 9 -0,0756% -0,2596 10 -0,2616% -0,8982

Tabel 33: Gemiddelde AR over alle events binnen de energiesector – constant mean model DE INDUSTRIËLE SECTOR

Aantal dagen ten opzichte van het event

Gemiddelde Abnormal Return (AR)

T-statistiek gemiddelde Abnormal Return (AR)

-1 0,0058% 0,0286 0 -0,0613% -0,3030 1 0,0713% 0,3526 2 0,0136% 0,0675 3 0,0391% 0,1935 4 -0,1306% -0,6461 5 -0,2109% -1,0434 6 -0,3073% -1,5204 7 0,1171% 0,5795 8 -0,1173% -0,5801 9 0,0229% 0,1133 10 -0,3313% -1,6391

DE NUTSSECTOR

Aantal dagen ten opzichte van het event

Gemiddelde Abnormal Return (AR)

T-statistiek gemiddelde Abnormal Return (AR)

-1 0,0224% 0,1444 0 -0,0821% -0,5288 1 -0,1168% -0,7525 2 -0,2606% -1,6786 3 -0,0520% -0,3347 4 0,0045% 0,0287 5 -0,2651% -1,7078 6 -0,0555% -0,3578 7 -0,0118% -0,0761 8 -0,3198% -2,0600 9 -0,1249% -0,8042 10 -0,0598% -0,3851

Tabel 35: Gemiddelde AR over alle events binnen de nutssector – constant mean model

ROBUUSTHEIDSCONTROLE – ALTERNATIEVE WINDOWS

HYPOTHESE 1: EXTREME WEERSOMSTANDIGHEDEN LEIDEN TOT AFWIJKENDE RENDEMENTEN OP DE S&P 500 INDEX

Aantal dagen ten opzichte van het event

Gemiddelde Abnormal Return (AR)

T-statistiek gemiddelde Abnormal Return (AR)

-3 0,3703% 2,1255 -2 0,0226% 0,1295 -1 -0,0214% -0,1226 0 0,0355% 0,2036 1 0,0237% 0,1361 2 0,0372% 0,2135 3 -0,0090% -0,0517 4 -0,0162% -0,0932 5 -0,1311% -0,7527 6 -0,2327% -1,3356

7 0,0768% 0,4410 8 -0,1959% -1,1241 9 0,0444% 0,2546 10 -0,2641% -1,5160 11 -0,0461% -0,2645 12 0,2065% 1,1854 13 -0,1550% -0,8896 14 -0,0998% -0,5730 15 0,0417% 0,2392

Tabel 36: Gemiddelde AR over alle events voor de S&P 500 – marktmodel – alternatief event- en estimation window

Gemiddelde CAR 0,3127%

SE 0,0076

T-waarde gemiddelde CAR -0,4117

Tabel 37: Gemiddelde CAR over alle events en over de tijd voor de S&P 500 – marktmodel - alternatief event- en estimation window

HYPOTHESE 2: ORKANEN HEBBEN EEN MEER SIGNIFICANT EFFECT OP DE RENDEMENTEN VAN DE S&P 500

Aantal dagen ten opzichte van het event

Gemiddelde Abnormal Return (AR)

T-statistiek gemiddelde Abnormal Return (AR)

-3 0,7147% 1,8166 -2 0,0976% 0,2480 -1 -0,2157% -0,5483 0 -0,1593% -0,4048 1 -0,2352% -0,5979 2 -0,1242% -0,3158 3 0,3569% 0,9070 4 0,1585% 0,4030 5 -0,7399% -1,8806 6 -0,2978% -0,7569 7 0,5903% 1,5005

8 0,0072% 0,0183 9 -0,2712% -0,6893 10 -0,1084% -0,2756 11 0,1334% 0,3390 12 0,1949% 0,4953 13 -0,0770% -0,1956 14 -0,1744% -0,4432 15 0,0519% 0,1320

Tabel 38: Gemiddelde AR over alle events binnen de S&P 500: focus op orkanen – marktmodel - alternatief event- en estimation window

Gemiddelde CAR 0,0977%

SE 0,0171

T-waarde gemiddelde CAR -0,0570

Tabel 39: Gemiddelde CAR over alle events en over de tijd voor de S&P 500: focus op orkanen – marktmodel - alternatief event- en estimation window

HYPOTHESE 3: EXTREME WEERSOMSTANDIGHEDEN HEBBEN EEN VERSCHILLEND EFFECT OP DE VERSCHILLENDE SECTOREN VAN DE S&P 500

DE VERZEKERINGSSECTOR

Aantal dagen ten opzichte van het event

Gemiddelde Abnormal Return (AR)

T-statistiek gemiddelde Abnormal Return (AR)

-3 0,3042% 1,4830 -2 -0,0309% -0,1509 -1 -0,1348% -0,6572 0 -0,0384% -0,1874 1 -0,1108% -0,5400 2 0,0950% 0,4629 3 0,0446% 0,2175 4 -0,0703% -0,3426 5 -0,0973% -0,4742 6 -0,2427% -1,1835 7 0,1289% 0,6284

8 -0,5732% -2,7948 9 0,2169% 1,0573 10 -0,3220% -1,5701 11 -0,0638% -0,3111 12 0,2432% 1,1857 13 -0,0348% -0,1696 14 -0,0822% -0,4008 15 -0,0543% -0,2649

Tabel 40: Gemiddelde AR over alle events binnen de verzekeringssector – marktmodel - alternatief event- en estimation window

Gemiddelde CAR 0,8229%

SE 0,0089

T-waarde gemiddelde CAR -0,9204

Tabel 41: Gemiddelde CAR over alle events en over de tijd voor de verzekeringssector – marktmodel – alternatief event- en estimation window

DE ENERGIESECTOR

Beschrijving event CAR T-waarde

Orkaan Harvey (25/08/2017 – 28/08/2017) 10,5139% 2,2531 Orkaan Irma (06/09/2017 – 12/09/2017) 13,6844% 2,9236 Orkaan Michael (10/10/2018 – 11/10/2018) -14,6801% -2,5568

Tabel 42: Significante events doorheen de tijd voor de energiesector – marktmodel - alternatief event- en estimation window

Aantal dagen ten opzichte van het event

Gemiddelde Abnormal Return (AR)

T-statistiek gemiddelde Abnormal Return (AR)

-3 0,5430% 1,8323

-2 0,4687% 1,5816

-1 0,3899% 1,3158

1 0,3159% 1,0659 2 0,0227% 0,0765 3 0,0215% 0,0725 4 0,2567% 0,8664 5 -0,4672% -1,5764 6 -0,2786% -0,9403 7 0,3174% 1,0710 8 -0,1608% -0,5424 9 -0,0246% -0,0829 10 -0,3465% -1,1692 11 -0,1203% -0,4059 12 0,2341% 0,7900 13 -0,3157% -1,0654 14 -0,3995% -1,3481 15 -0,3028% -1,0217

Tabel 43: Gemiddelde AR over alle events binnen de energiesector – marktmodel - alternatief event- en estimation window

Gemiddelde CAR 0,4470%

SE 0,0129

T-waarde gemiddelde CAR 0,3460

Tabel 44: Gemiddelde CAR over alle events en over de tijd voor de energiesector – marktmodel – alternatief event- en estimation window

DE INDUSTRIËLE SECTOR

Beschrijving event CAR T-waarde

Orkaan Michael

(10/10/2018 – 11/10/2018) -10,4609% -2,4046

Tabel 45: Significante events doorheen de tijd voor de industriële sector – marktmodel - alternatief event- en estimation window

Aantal dagen ten opzichte van het event

Gemiddelde Abnormal Return (AR)

T-statistiek gemiddelde Abnormal Return (AR)

-3 0,2488% 1,2249 -2 0,1100% 0,5417 -1 0,0205% 0,1011 0 -0,0736% -0,3622 1 -0,0333% -0,1641 2 0,0850% 0,4184 3 -0,0018% -0,0091 4 -0,0274% -0,1350 5 -0,1071% -0,5273 6 -0,2605% -1,2827 7 -0,0296% -0,1459 8 -0,1371% -0,6750 9 0,0821% 0,4040 10 -0,4350% -2,1416 11 -0,0099% -0,0485 12 0,1701% 0,8378 13 -0,2078% -1,0231 14 -0,1241% -0,6113 15 0,0903% 0,4447

Tabel 46: Gemiddelde AR over alle events binnen de industriële sector – marktmodel - alternatief event- en estimation window

Gemiddelde CAR 0,6404%

SE 0,0086

T-waarde gemiddelde CAR -0,7234

Tabel 47: Gemiddelde CAR over alle events en over de tijd voor de industriële sector – marktmodel – alternatief event- en estimation window

DE NUTSSECTOR

Beschrijving event CAR T-waarde

Orkaan Sandy

(30/10/2012 – 31/10/2012) -7,3826% -2,9581 Noodweer in “The Great Plains”, het midwesten en

het oosten (18/05/2013 – 22/05/2013) -7,1085% -2,4498 Koudegolf in de VS

(14/02/2015 – 20/02/2015) -9,5366% -2,2255

Tabel 48: Significante events doorheen de tijd voor de nutssector – marktmodel - alternatief event- en estimation window

Aantal dagen ten opzichte van het event

Gemiddelde Abnormal Return (AR)

T-statistiek gemiddelde Abnormal Return (AR)

-3 0,1748% 1,0941 -2 0,1236% 0,7737 -1 0,0556% 0,3478 0 -0,1134% -0,7095 1 -0,1151% -0,7202 2 -0,2578% -1,6134 3 -0,0754% -0,4721 4 0,0897% 0,5615 5 -0,2355% -1,4742 6 -0,0751% -0,4702 7 -0,0431% -0,2696 8 -0,2739% -1,7145 9 -0,1084% -0,6785 10 -0,1208% -0,7564 11 -0,0821% -0,5139 12 0,1482% 0,9274 13 -0,0541% -0,3388 14 -0,1349% -0,8444 15 0,0531% 0,3321

Tabel 49: Gemiddelde AR over alle events binnen de nutssector – marktmodel - alternatief event- en estimation window