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1. 5. Trading activity measured by the 2average lot size per trade7 is fairly constant across tenors, except for the extreme strike ranges, and similarly sized across nearthe -money puts and calls. A salient pattern is that lot sizes increase substantially the more the options are OTM: e.g., the lot size is about twice as large for far OTM puts 57 84 than near-the-money puts 33 45. 38 Compare Christoffersen et al. (2018, Table 2) or Muravyev and Pearson (2020, Table 2). The spreads are even smaller than the relative spreads of ATM options on the SPY ETF reported in Andersen et al.
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79. Log price 12 March 2020 Sources: Eurex. Notes: Bid and ask log prices for put options with a tenor of 1 day. The grey triangles denote the benchmark bid and ask log prices at 12:00 hrs on March 12. Upward facing triangles denote the bid log price and downward facing triangles denote the ask log price. The light grey prices are the observed prices and the dark grey prices are the interpolated prices on the moneyness gridpoints, e.g., 3 and 2.5. The red triangles denote the bid and ask log prices at 15:30 hrs. The light red prices are the observed prices and the dark red prices are the interpolated prices.
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89. Option pricing theory has substantiated this dependence. Medvedev and Scaillet (2007) and Durrleman (2008) prove that the IV of an option with strike equal to the underlying price converges to the spot volatility as the tenor converges to zero.36 Thus, order books and prices of short-dated ATM options provide significant information about the state of spot volatility. Additionally, research by Bollerslev and Todorov (2011, 2014) and Andersen et al. (2015, 2020) has shown that far OTM puts and calls with short tenors can reveal information about downside and upside jump risks.
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92. Relative to the shaded pre-event period, the spread did not change much in the shaded post-event period and the market depth increased. -0.2 -0.15 -0.1 -0.05 0 0.05 IV 18/19/20 March 2020 11:30 14:30 17:25 11:30 14:30 17:25 11:30 14:30 17:25 -0.04 -0.02 0 0.02 0.04 0.06 0.08 Underlying price -0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0
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95. Sandås, P. (2001). Adverse selection and competitive market making: Empirical evidence from a limit order market. The Review of Financial Studies, 14(3):705–734.
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97. Sources: Own illustration based on illustrations in Gould et al. (2013), Foucault et al. (2013, p. 195), Schnaubelt et al. (2019). Notes: The upper plot shows the non-cumulative order book. The stacked bars refer to multiple orders. E.g., qbid 5,t consists of two orders and qbid 4,t of one order. Note, however, that we do not observe the size of the single orders as could be implied by the illustration. The lower plot shows the cumulative order book, i.e., the bid and ask curve. Figure 4: Illustration of an order book snapshot at time t.
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98. Stoll, H. R. (1978). The Supply of Dealer Services in Securities Markets. The Journal of Finance, 33(4):1133–1151.
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99. Tange, O. (2011). GNU parallel – The Command-Line Power Tool. ;login: The USENIX Magazine, 36(1):42–47.
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100. The evidence presented in Section E.1 is consistent with key predictions of inventory risk models. If (far) OTM puts and calls are more exposed to non-hedgeable forms of risk, such as jump risk, they imply higher inventory risk. The market maker then bids less aggressively so that spreads are wider and market depth lower. The associated larger lot sizes, in which OTM options trade, further increase the inventory risk for market makers. In contrast, ITM options, whose main risk is the change in the underlying asset because their delta is close to one, can be hedged efficiently by offsetting positions in the EURO STOXX 50 future.51 In addition, ITM options require only infrequent adjustments of the replicating portfolio because they have a low gamma. Hence, market makers may incur smaller replication costs (Leland, 1985) and thus set small spreads and provide high market depth, as we document.
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101. The specification is exponentially affine in the state variables. Thus, options can be priced along the lines of Duffie et al. (2000). Table 5: Parameters of the option pricing model.
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102. Thus, the high spreads in March 2020 and the lower spreads and higher elasticities observed from April 2020 onwards do not seem to result from fixed transaction costs and changes in trading volumes but rather from other channels, such as asymmetric information and inventory risk.
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103. Wei, J. and Zheng, J. (2010). Trading Activity and Bid-Ask Spreads of Individual Equity Options. Journal of Banking & Finance, 34:2897–2916.

104. Wurgler, J. and Zhuravskaya, E. (2002). Does Arbitrage Flatten Demand Curves for Stocks? The Journal of Business, 75(4):583–608.

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16. Banking supervision, monetary policy and risk-taking: Big data evidence from 15 credit registers. (2020). Smets, Frank ; Peydro, Jose-Luis ; Boucinha, Miguel ; Altavilla, Carlo.
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17. ECB Monetary Policy and Bank Default Risk. (2020). Vander Vennet, Rudi ; Soenen, Nicolas.
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18. Monetary Policy Surprises and Exchange Rate Behavior. (2020). Lee, Sang Seok ; Kısacıkoğlu, Burçin ; Kara, Hakan ; Gürkaynak, Refet ; Gurkaynak, Refet S ; Kisacikolu, Burin.
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19. Household Balance Sheet Channels of Monetary Policy: A Back of the Envelope Calculation for the Euro Area. (2020). Violante, Giovanni ; Tristani, Oreste ; Slacalek, Jiri.
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20. Distributional consequences of conventional and unconventional monetary policy. (2020). Kolasa, Marcin ; Brzoza-Brzezina, Michal ; Bielecki, Marcin.
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21. Macroprudential Policy in the Euro Area. (2020). Paya, Ivan ; Fernandez-Gallardo, Alvaro.
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22. Household balance sheet channels of monetary policy: A back of the envelope calculation for the euro area. (2020). Tristani, Oreste ; Slacalek, Jiri ; Violante, Giovanni L.
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23. Central bank information effects and transatlantic spillovers. (2020). Jarociński, Marek ; Jarociski, Marek.
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24. Central banks in parliaments: a text analysis of the parliamentary hearings of the Bank of England, the European Central Bank and the Federal Reserve. (2020). Jamet, Jean-Francois ; Giovannini, Alessandro ; Fraccaroli, Nicolò.
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25. Heterogeneity in corporate debt structures and the transmission of monetary policy. (2020). Holm-Hadulla, Fédéric ; Thurwachter, Claire.
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26. Banking supervision, monetary policy and risk-taking: big data evidence from 15 credit registers. (2020). Smets, Frank ; Peydro, Jose-Luis ; Boucinha, Miguel ; Altavilla, Carlo.
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27. How do financial markets react to monetary policy signals?. (2020). Altavilla, Carlo ; Motto, Roberto.
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28. Monetary policy effects in times of negative interest rates: What do bank stock prices tell us?. (2020). Houben, Aerdt ; Giuliodori, Massimo ; Bats, Joost.
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29. A Structural Investigation of Quantitative Easing. (2020). Strobel, Felix ; Boehl, Gregor ; Goy, Gavin.
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30. Demand shocks for public debt in the Eurozone. (2020). Lengyel, Andras ; Giuliodori, Massimo.
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31. Banking Supervision, Monetary Policy and Risk-Taking: Big Data Evidence from 15 Credit Registers. (2020). Smets, Frank ; Peydro, Jose-Luis ; Boucinha, Miguel ; Altavilla, Carlo.
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32. Measuring Monetary Policy with Residual Sign Restrictions at Known Shock Dates. (2020). Schiman, Stefan ; Badinger, Harald.
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33. Monetary Policy Surprises and Exchange Rate Behavior. (2020). Kısacıkoğlu, Burçin ; Kara, Hakan ; Gürkaynak, Refet ; Gurkaynak, Refet S ; Kisacikoglu, Burcin .
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34. The effects of conventional and unconventional monetary policy : identification through the yield curve. (2020). Nelimarkka, Jaakko ; Kortela, Tomi.
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35. Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy. (2020). Pfarrhofer, Michael ; Hauzenberger, Niko.
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36. Information Effects of Euro Area Monetary Policy: New evidence from high-frequency futures data. (2019). Kerssenfischer, Mark.
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37. Information effects of euro area monetary policy: New evidence from high-frequency futures data. (2019). Kerssenfischer, Mark.
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38. Identification with External Instruments in Structural VARs under Partial Invertibility. (2019). Ricco, Giovanni ; Miranda-Agrippino, Silvia.
    In: The Warwick Economics Research Paper Series (TWERPS).
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39. Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy. (2019). Pfarrhofer, Michael ; Niko, Hauzenberger.
    In: Working Papers in Economics.
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40. Macroeconomic Effects of the ECBs Forward Guidance. (2019). Zlobins, Andrejs.
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41. Quantitative Easing and the Term Premium as a Monetary Policy Instrument. (2019). Vaccaro-Grange, Etienne.
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42. Delphic and Odyssean Monetary Policy Shocks: Evidence from the Euro Area. (2019). ferroni, filippo ; Andrade, Philippe.
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43. Monetary policy announcements and expectations: Evidence from german firms. (2019). Müller, Gernot ; Enders, Zeno ; Muller, Gernot J ; Hunnekes, Franziska.
    In: Journal of Monetary Economics.
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44. A tale of two decades: the ECB’s monetary policy at 20. (2019). Rostagno, Massimo ; Lemke, Wolfgang ; Altavilla, Carlo ; Motto, Roberto ; Yiangou, Jonathan ; Carboni, Giacomo ; Guilhem, Arthur Saint.
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45. Monetary policy shocks and the health of banks. (2019). Uhlig, Harald ; Jung, Alexander.
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46. Monetary Policy Announcements and Expectations: Evidence from German Firms. (2019). Müller, Gernot ; Enders, Zeno ; Muller, Gernot ; Hunnekes, Franziska.
    In: CEPR Discussion Papers.
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47. Identification with External Instruments in Structural VARs under Partial Invertibility. (2019). Ricco, Giovanni ; Miranda-Agrippino, Silvia.
    In: CEPR Discussion Papers.
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48. Systemic Bank Risk and Monetary Policy. (2019). Faia, Ester ; Karau, Soeren.
    In: CEPR Discussion Papers.
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49. Banking Supervision, Monetary Policy and Risk-Taking: Big Data Evidence from 15 Credit Registers. (2019). Smets, Frank ; Peydro, Jose-Luis ; Boucinha, Miguel ; Altavilla, Carlo.
    In: Working Papers.
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50. Quantitative Easing and the Term Premium as a Monetary Policy Instrument. (2019). Vaccaro-Grange, Etienne.
    In: AMSE Working Papers.
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