THE IMPLIED VOLATILITY OF ETF AND INDEX OPTIONS

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "THE IMPLIED VOLATILITY OF ETF AND INDEX OPTIONS"

Transcription

1 The Internatonal Journal of Busness and Fnance Research Volume 5 Number THE IMPLIED VOLATILITY OF ETF AND INDEX OPTIONS Stoyu I. Ivanov, San Jose State Unversty Jeff Whtworth, Unversty of Houston-Clear Lake Y Zhang, Prare Vew A&M Unversty ABSTRACT We examne the opton-mpled volatlty of the three most lqud ETFs (Damonds, Spders, and Cubes) and ther respectve trackng ndces (Dow 30, S&P 500, and NASDAQ 100). We fnd that volatlty smles for ETF optons are more pronounced than for ndex optons, prmarly because deep-n-themoney ETF optons have consderably hgher mpled volatlty than deep-n-the-money ndex optons. The observed dfference n mpled volatlty s not due to a dfference between the realzed return dstrbutons of the underlyng ETFs and ndces. Dfferences n mpled volatlty for ETF and ndex optons also do not appear to be explaned by dscrepances n net buyng pressure, as theorzed by Bollen and Whaley (2004). JEL: G11; G12 KEYWORDS: exchange-traded funds, ndex optons, mpled volatlty, open nterest INTRODUCTION I n ths paper, we study the opton-mpled volatlty of exchange-traded funds (ETFs) and ther trackng ndces. ETFs are relatvely cheap nstruments for dversfcaton n terms of drect costs. They have greatly ncreased n popularty and have become mportant nvestment vehcles for both professonal and ndvdual nvestors. The Investment Company Insttute (ICI) reports that there were 80 ETFs n 2000 and 359 ETFs n 2006, a 350 percent ncrease. ETF assets also ncreased sgnfcantly from $65.59 bllon n 2000 to $ bllon n 2006, an ncrease of 544 percent for the perod. Most of the lterature on ETFs focuses on ther trackng errors relatve to ther respectve ndces. However, very lttle research has been conducted on ETF volatlty. The paper contrbutes to the lterature n several ways. Frst, the paper flls the vod n the exstng lterature on the volatlty of ETFs, whch play an mportant role n rsk dversfcaton. We examne both the realzed return volatlty and the opton-mpled volatlty a commonly used estmate of future volatlty. We fnd that the mpled volatlty of ETF optons s dfferent from that of ndex optons, especally for deep-n-the-money optons. However, we fnd no sgnfcant dfference n the realzed return volatltes of ETFs versus ther ndces. Second, the paper adopts a unque sample to reexamne the net buyng pressure theory of Bollen and Whaley (2004). The advantage of usng par samples of ETF optons and ndex optons s that return dstrbutons of ETFs are nsgnfcantly dfferent from those of ther trackng ndces. Our results are nconsstent wth the argument of Bollen and Whaley (2004) that an opton s mpled volatlty functon should be postvely related to ts net buyng pressure. Therefore, the dfference n mpled volatlty functons between ETFs and ndces can be attrbuted to other factors. The remander of ths paper s organzed as follows: The next secton examnes the related lterature and develops the scope of ths research study. We then descrbe our data and methodology and dscuss the results of our emprcal tests. The fnal secton concludes. 35

2 S. I. Ivanov et al IJBFR Vol. 5 No LITERATURE REVIEW AND RESEARCH DEVELOPMENT In recent years, ETFs have exploded n popularty as nvestment tools. However, most of the extant lterature on ETFs focuses on ther trackng error (e.g., Poterba and Shoven, 2002; Engle and Sarkar, 2006). To our knowledge there are no studes on the volatlty of ETFs. In a study of the excess volatlty characterstcs of closed-end funds, Pontff (1997) fnds that closed-end funds are more volatle than ther underlyng securtes. Closed-end funds are smlar to ETFs n that both are traded on a stock exchange throughout the tradng day, but ETFs are structured dfferently from closed-end funds. For example, ETFs legally resemble open end funds n the sense that new ETF securtes can be ssued. ETFs are passve nvestment vehcles. The ETF manager closely tracks the yeld and prce of the underlyng ndex by acqurng the stocks n the ndex. Nevertheless, ETFs do not necessarly mmc ther underlyng ndces perfectly for several reasons. Frst, the proportons and exact composton of the ETF portfolo mght dffer slghtly from that of the underlyng ndex as the portfolo manager seeks to mnmze costs. Second, ETFs accumulate dvdends n a non-nterest bearng account and dstrbute accumulated dvdends n a lump sum perodcally. (Spders and Cubes dstrbute dvdends quarterly, whle Damonds pay dvdends monthly.) Thrd, ETFs contnue tradng after hours untl 4:15 p.m., whle ndces are reported at 4:00 p.m. These dfferences may cause the return of an ETF to devate from that of ts underlyng ndex. Chrstensen and Prabhala (1998) show that a stock s future volatlty s predcted more relably by the opton-mpled volatlty than by the stock s past realzed volatlty. Therefore, we also nvestgate the mpled volatlty of the three best-known and most lqud ETFs: Damonds, Spders, and Cubes. These ETFs track the yeld and prce of the Dow Jones Industral Average (.e., Dow 30), S&P 500, and NASDAQ 100, respectvely. ETFs are traded lke stocks, so ETF optons can be consdered stock optons. The exstng lterature shows that the mpled volatlty functon of stock optons s dfferent from that of ndex optons. Baksh, Kapada, and Madan (2003) study S&P 100 ndex optons and the 30 largest stocks n the ndex and fnd that the ndex volatlty smle (the varaton of the mpled volatlty across strke prces) s more negatvely sloped than ndvdual stock volatlty smles. They show that ths dfference comes from the more skewed return dstrbuton of ndvdual stocks. Hence, we frst test whether the mpled volatlty functons of ETF and ndex optons dffer. We fnd that ETF optons commonly have hgher mpled volatltes than ther ndces, especally for deep-n-the-money optons. In vew of ths result, we examne not only the mean return of ETFs versus ther trackng ndces, but also the entre return dstrbuton. We focus on the hgher moments of return volatlty, skewness, and kurtoss. Usng realzed daly returns over a perod spannng more than sx years, we fnd no sgnfcant dfference between the return dstrbutons of ETFs and ndces. Snce ETFs track ther underlyng ndces closely and are not sgnfcantly dfferent from ther underlyng ndex n the return dstrbutons, ths produces a unque sample to explore the mpled volatlty of stock optons versus ndex optons. Bollen and Whaley (2004) argue that opton prces and mpled volatltes are affected by the demand for optons. When arbtrage s lmted, the opton supply curve s upward slopng so that mpled volatlty s related to the net buyng pressure from orders submtted by nvestors. Bollen and Whaley study both ndex optons and stock optons and fnd that changes n the mpled volatlty of S&P 500 ndex optons (ndvdual stock optons) are most affected by demand for ndex puts (ndvdual stock calls). They suggest that net buyng pressure s related to nvestor speculatve or hedgng demand for optons. Put optons are wdely used for downsde protecton especally out-of-the-money puts, whch are low-cost hedgng nstruments. Call optons provde upsde potental; thus, out-of-the-money calls are more lkely used for speculaton. Therefore, we attempt to determne whether ETF optons are used more often for speculatve or hedgng purposes a queston that, to the best of our knowledge, has not been explored n pror studes. Index optons are wdely used for hedgng purposes (Evnne and Rudd, 1985). Whle Moran 36

3 The Internatonal Journal of Busness and Fnance Research Volume 5 Number (2003) suggests that ETFs are also wdely used for hedgng, the speculatve motve for tradng cannot be ruled out. We nvestgate ths queston by examnng the behavor of both calls and puts of varyng levels of moneyness. The level of open nterest provdes some ndcaton of the demand for an opton. Therefore, we perform multvarate regressons to determne whether the mpled volatlty of ETF and ndex optons s related to open nterest n the manner suggested by the net buyng pressure hypothess. Our paper s related to studes by Chan, Cheng, and Lung (2004) and Kang and Park (2008). Chan, Cheng, and Lung use the Bollen and Whaley (2004) net buyng pressure metrc to examne the mpled volatltes, premums, and profts of Hong Kong Hang Seng ndex optons. Kang and Park use the net buyng pressure metrc to examne the mpled volatltes of KOSPI 200 optons. In another work, Chan, Cheng, and Lung (2006) study Hang Seng ndex optons durng the Asan Fnancal Crss and fnd evdence n support of the net buyng pressure hypothess. We also account for transacton costs. Peña, Rubo, and Serna (1999) use the bd-ask spread as a proxy for transacton costs and fnd that the spread nfluences the curvature of the volatlty smle. DATA AND METHODOLOGY We study three ETFs: Spders (SPY), Damonds (DIA), and Cubes (QQQQ after the swtch from AMEX to NASDAQ on 12/1/2004, and QQQ before the swtch). Data for these ETFs and for the S&P 500 ndex are obtaned from the Center for Research n Securty Prces (CRSP). Data for the Dow Jones Industral Average are obtaned from Dow Jones Indces webste ( and data for the NASDAQ 100 ndex are obtaned from the NASDAQ Indces webste ( One potental problem wth our data s that closng ndex levels are reported as of 4:00 p.m., whle ETFs contnue tradng untl 4:15 p.m. Thus, to algn the tradng perods, we use the NYSE Trade and Quote (TAQ) database to obtan the last prce for each of our ETFs wthn one second of 4:00 p.m. each day. Our sample perod s from 3/10/1999 to 12/29/2006. We use optons data from the Chcago Board Optons Exchange (CBOE) from 2003 to 2006 provded by DeltaNeutral.com. Index optons for the Dow 30, S&P 500, and NASDAQ 100 are all European and expre on the Saturday followng the thrd Frday of the expraton month. However, the ETF optons are Amercan. The data from DeltaNeutral.com ncludes mpled volatltes based on the Black-Scholes opton prcng model. Whle ths s correct for ndex optons, whch are European, t s ncorrect for ETF optons, whch are Amercan. Thus, we compute a new set of mpled volatltes for ETF optons based on a 100-step bnomal tree model. The optons dataset s fltered based on the crtera suggested by Day and Lews (1988) and Xu and Taylor (1994). The optons used to form the sample are requred to meet the followng crtera: a) The tme to expraton must be greater than 7 days and less than 30 days. δt rt b) The opton must satsfy the European opton boundary condtons, c < Se Xe and rt δt p < Xe Se. c) The opton must also satsfy the Amercan opton boundary condtons, C < S X and P < X S. d) The opton must not be so deep out of or n the money that exercse s ether mpossble or absolutely certan;.e., the absolute value of the opton s hedgng delta s between 0.02 and After applyng these flters, we sort the remanng optons n the sample by mpled volatlty and remove those observatons n the top and bottom 1 percent, resultng n a fnal sample of 87,588 ETF optons and 105,679 ndex optons. These crtera ensure that the opton prces used n ths study are reasonable and help to avod the problems of thn tradng and excessve volatlty, whch mght endanger the soundness of our conclusons. We determne each opton s moneyness usng the Bollen and Whaley (2004) method based on the optons delta, as shown n Table 1. 37

4 S. I. Ivanov et al IJBFR Vol. 5 No Table 1: Bollen and Whaley Classfcatons of Moneyness Based on Opton s Delta Category Labels Range 1 Deep-n-the-money (DITM) call < C 0.98 Deep-out-of-the-money (DOTM) put < P In-the-money (ITM) call < C Out-of-the-money (OTM) put < P At-the-money (ATM) call < C At-the-money (ATM) put < P Out-of-the-money (OTM) call < C In-the-money (ITM) put < P Deep-out-of-the-money (DOTM) call 0.02 < C Deep-n-the-money (DITM) put 0.98 < P Ths table shows Bollen and Whaley s fve moneyness categores. To nvestgate the potental dfference n the mpled volatlty functons of ndex optons versus stock optons, we consder several possble explanatons: 1. ETFs and ndces have dfferent return dstrbutons as argued by Baksh et al. (2003). 2. Demand, as measured by open nterest, s dfferent for ETF optons and ndex optons. 3. Transacton costs, as measured by the bd-ask spread, are larger for ndex optons than for ETF optons. To determne whch of these explanatons are best supported by the data, we perform unvarate and multvarate tests on the mpled volatlty functon. EMPIRICAL RESULTS Frst we examne the mpled volatlty levels of ETF and ndex optons. Because ETF optons are Amercan, we compute ther mpled volatltes by usng a 100-step bnomal tree model n leu of the Black-Scholes formula. Fgure 1 presents the mean mpled volatlty for the ETF and ndex optons n each of the fve moneyness categores descrbed prevously. Results are presented separately for calls and puts. In each case, ETF optons have slghtly more pronounced volatlty smles than ther correspondng ndex optons. We also note that mpled volatltes for Damond and Cube optons are hgher n each moneyness category than for Dow 30 and NASDAQ 100 optons. For Spder/S&P 500 opton pars, the relatve levels of mpled volatlty depend on the level of moneyness. In every case, DITM ETF optons (.e., Category 1 calls and Category 5 puts) exhbt consderably hgher mpled volatlty than DITM ndex optons, even after usng a bnomal model to account for potental early exercse of ETF optons. For other categores, mpled volatlty s usually farly close for ETF vs. ndex optons, but DOTM Cube optons do have consderably hgher mpled volatlty than DOTM NASDAQ 100 optons. The documented dfference n the mpled volatltes of ETF and ndex optons mght be due to ETFs and ndces havng dfferent return dstrbutons, as suggested by Baksh et al. (2003). Alternatvely, the dfference may be explaned by transacton costs (e.g., bd-ask spreads may be larger for ndex optons compared to ETF optons) or by the demand for dfferent types of optons, as measured by ther open nterest. To address the frst of these possble explanatons, we begn by examnng hstorcal realzed daly returns of ETFs and ndces. Because Spders (Cubes) [Damonds] are desgned to be prced at 1/10 (1/40) [1/100] the level of ther trackng ndex, we scale down each ndex by ts approprate factor to facltate comparson. Table 2 shows summary statstcs usng closng prces. The average prce level and return of ETFs and ndces are very close. The volatlty, skewness, and kurtoss are smlar as well, whch suggests no sgnfcant dfference n the dstrbutons of ETFs and ndces. 38

5 The Internatonal Journal of Busness and Fnance Research Volume 5 Number In Table 3, we present the same summary statstcs usng synchronzed prces and ndex levels. Synchronzaton s performed by obtanng ntraday stock prce data from the NYSE TAQ database. Fgure 1: Mean Impled Volatltes for ETF and Index Optons Spder/S&P 500 calls Spder/S&P 500 puts Mean Impled Volatlty (s) Spders S&P 500 Mean Impled Volatlty (s) S&P 500 Spders Cube/NASDAQ 100 calls Cube/NASDAQ 100 puts Mean Impled Volatlty (s) NASDAQ 100 Cubes Mean Impled Volatlty (s) Cubes NASDAQ 100 Damond/Dow 30 calls Damond/Dow 30 puts 0.35 Mean Impled Volatlty (s) Damonds Dow 30 Mean Impled Volatlty (s) Damonds Dow 30 Ths fgure shows the Impled Volatlty Smles of the three ETFs versus ther trackng ndces. 39

6 S. I. Ivanov et al IJBFR Vol. 5 No Table 2: Summary Statstcs for ETFs and Indces Usng Closng Prces Spder (SPY) prce 1/10 S&P 500 ndex level Spder (SPY) return S&P 500 ndex return Mean Medan devaton Skewness Kurtoss N Cube (QQQQ) prce 1/40 NASDAQ 100 ndex level Cube (QQQQ) return NASDAQ 100 ndex return Mean Medan devaton Skewness Kurtoss N Damond (DIA) prce 1/100 Dow 30 ndex level Damond (DIA) return Dow 30 ndex return Mean Medan devaton Skewness Kurtoss N Ths table shows the closng prces, volatlty, and returns of ETFs and ndces. Data are for the perod 3/10/1999 untl 12/29/2006. Table 3: Summary Statstcs for ETFs and Indces Usng Synchronzed Prces Spder (SPY) prce 1/10 S&P 500 ndex level Spder (SPY) return S&P 500 ndex return Mean Medan devaton Skewness Kurtoss N Cube (QQQQ) prce 1/40 NASDAQ 100 ndex level Cube (QQQQ) return NASDAQ 100 ndex return Mean Medan devaton Skewness Kurtoss N Damond (DIA) prce 1/100 Dow 30 ndex level Damond (DIA) return Dow 30 ndex return Mean Medan devaton Skewness Kurtoss N Ths table shows the synchronzed prces, volatlty, and returns of ETFs and ndces. Data are for the perod 3/10/1999 untl 12/29/2006. For each of the three ETFs, the last tradng prce wthn one second of 4:00 p.m. s extracted and matched wth ts closng ndex level. In rare cases where no ETF prce s quoted wthn one second of 4:00 p.m., that day s observaton s deleted from the synchronzed dataset. The results n Table 3 are smlar to those n Table 2 n that there s no sgnfcant dfference n volatlty, skewness, and kurtoss between ETFs and ndces, whch agan shows smlarty n the return dstrbutons of ETFs and ndces. 40

7 The Internatonal Journal of Busness and Fnance Research Volume 5 Number We now test formally for equalty between the return dstrbutons of ETFs and ther correspondng ndces. Table 4 presents Kolmogorov-Smrnov values and sgnfcance levels for each of the ETF-ndex pars examned n ths study. The Kolmogorov-Smrnov test s a non-parametrc test based on the maxmum dstance between the cumulatve dstrbuton functons of two random varables. Our results show no sgnfcant dfference n the dstrbutons of ETF returns and that of ther underlyng ndces, regardless of whether we use closng or synchronzed ETF prces. We perform addtonal tests on the smlarty between dstrbutons of ETFs and underlyng ndces returns wth quantle-quantle (Q-Q) plots. In dong so, we observe that ETF and ndex returns quantles plot on a straght lne aganst each other, ndcatng that the two dstrbutons are the same. These plots are not shown but are avalable upon request. These fndngs further confrm that our observed dfferences n mpled volatltes are not due to dfferences between the underlyng ETF and ndex return dstrbutons. Table 4: Kolmogorov-Smrnov Test for Equalty of Dstrbuton Functons Usng Closng ETF Prces Usng Synchronzed ETF Prces KS value p-value Decson KS value p-value Decson Spders vs. S&P Fal to reject Fal to reject Cubes vs. NASDAQ Fal to reject Fal to reject Damonds vs. Dow Fal to reject Fal to reject Ths table shows the Kolmogorov-Smrnov (KS) test results of closng and synchronzed ETF and ndex returns. The test has a null hypothess of equalty between the dstrbuton functons of the two seres beng compared. Data are for the perod 3/10/1999 untl 12/29/2006. We now explore alternatve explanatons for dfferences n the mpled volatlty functon. When there are lmts to arbtrage, t s possble that mpled volatlty may be related to the demand for an opton, as suggested by Bollen and Whaley s (2004) net buyng pressure argument. It s also possble that dfferences n bd-ask spreads may be partly responsble for dfferent mpled volatlty levels. To test these two predctons, we estmate the multvarate regresson model: ˆ σ = β + β OpInt 0 + β OpInt * DummyIndex β BdAsk + β ExpratonTme + β DummyIndex ε, 4 (1) where σˆ s the opton s mpled volatlty, OpInt s open nterest dvded by 1,000,000, BdAsk s the percentage bd-ask spread (calculated as the dollar spread dvded by the ask prce), ExpratonTme s the amount of tme untl opton expraton, and DummyIndex s equal to 1 for ndex optons and 0 for ETF optons. Table 5 presents regresson results for opton categores 1 and 5 (DITM and DOTM optons). Results for other categores are not reported but are avalable from the authors upon request. Gven that open nterest s a proxy for demand, we would expect ths varable to be postvely related to an opton s prce (and therefore, ts mpled volatlty). However, ths s not the case; we document a consstently negatve and sgnfcant relaton between open nterest and mpled volatlty. Therefore, our regresson results do not appear to provde evdence for the net buyng pressure theory of Bollen and Whaley (2004). The sgnfcant relaton between the bd-ask spread and mpled volatlty supports the argument that the volatlty smle s related to transacton costs. However, the drecton of ths relatonshp s not the same n each case. In general, mpled volatlty s negatvely related to percentage spreads for DITM optons, but postvely related for DOTM optons. The sgns and sgnfcance levels of the DummyIndex coeffcents confrm that on average, ndex optons have lower mpled volatltes than ETF optons. Ths s especally true for DITM optons. Although we dd not obtan the expected sgn for the open nterest regresson varable n Table 5, t s worth notng that open nterest s an mperfect proxy for net buyng pressure snce the level of open nterest s affected by both buyer- and seller-ntated trades. Therefore, t s stll possble that the demand 41

8 S. I. Ivanov et al IJBFR Vol. 5 No for dfferent opton types may have a nontrval mpact on mpled volatltes. It s not surprsng that the greatest mpled volatlty dfferences are noted for DITM and DOTM optons. DOTM optons are especally useful for speculators due to the hgh elastcty of ther premum wth respect to the underlyng asset prce or ndex level. However, DOTM puts and calls can also be useful for hedgng by establshng floors on long postons and caps on short postons, respectvely. In addton, DITM optons can be very useful for establshng delta-neutral hedges; because ther gammas are near zero, t s not necessary to adjust the hedge rato as often when the value of the underlyng asset changes. The dfference n the mpled volatlty functons noted n Fgure 1 shows that ETF and ndex optons are not perfect substtutes for each other. Although more research s needed n ths area, ther overall hgher mpled volatltes suggests that ETF optons may be more attractve nstruments to hedgers and speculators. Table 5: Regresson Results on the Impled Volatlty Panel A: Category 1 (DITM call, DOTM put) Spder/S&P 500 calls Cube/NASDAQ 100 calls Damond/Dow 30 calls Coeffcent p-value Coeffcent p-value Coeffcent p-value Intercept *** < *** < *** <.0001 OpInt *** < *** < *** <.0001 BdAsk *** < *** < *** <.0001 ExpratonTme 73*** < *** < *** <.0001 DummyIndex *** < *** < *** <.0001 OpInt*DummyIndex *** < *** < *** <.0001 Adjusted R Observatons Spder/S&P 500 puts Cube/NASDAQ 100 puts Damond/Dow 30 puts Coeffcent p-value Coeffcent p-value Coeffcent p-value Intercept *** < *** < *** <.0001 OpInt *** < *** < *** <.0001 BdAsk *** < *** < *** <.0001 ExpratonTme 02*** 15 29*** < *** 07 DummyIndex *** < *** < *** <.0001 OpInt*DummyIndex *** < *** < *** <.0001 Adjusted R Observatons Panel B: Category 5 (DOTM call, DITM put) Spder/S&P 500 calls Cube/NASDAQ 100 calls Damond/Dow 30 calls Coeffcent p-value Coeffcent p-value Coeffcent p-value Intercept *** < *** < *** <.0001 OpInt *** < *** < *** <.0001 BdAsk 47** *** < *** <.0001 ExpratonTme 18*** < *** < *** <.0001 DummyIndex 58*** *** < *** <.0001 OpInt*DummyIndex *** < *** <.0001 Adjusted R Observatons Spder/S&P 500 puts Cube/NASDAQ 100 puts Damond/Dow 30 puts Coeffcent p-value Coeffcent p-value Coeffcent p-value Intercept *** < *** < *** <.0001 OpInt *** < *** < *** <.0001 BdAsk *** < *** < *** <.0001 ExpratonTme 79*** < *** < *** <.0001 DummyIndex *** < *** < *** <.0001 OpInt*DummyIndex *** < *** < *** <.0001 Adjusted R Observatons Ths table shows regresson results based on equaton (1). The tme perod s from January 2003 to December OpInt s open nterest dvded by 1,000,000; BdAsk s the dollar bd-ask spread dvded by the ask prce; ExpratonTme s the amount of tme to opton expraton; DummyIndex s 1 for ndex optons and 0 for ETF optons. *, **, *** ndcate sgnfcance at the 10, 5, and 1 percent levels respectvely. 42

9 The Internatonal Journal of Busness and Fnance Research Volume 5 Number CONCLUSION In ths paper we study a popular nvestment vehcle that has recently grown n promnence, the exchangetraded fund. Optons on ETFs are a recent development and as such have not been extensvely studed. We document that there s a dfference between the mpled volatltes of ETF and ndex optons. However, we fnd no evdence of a dfference n the return dstrbutons of ETFs versus ther trackng ndces, contrary to the predctons of Baksh et al. (2003). Because the underlyng return dstrbutons are the same, we nvestgate other possble explanatons for dfferences n the mpled volatlty functons. We fnd that mpled volatlty s related to the percentage bd-ask spread, although the drecton of ths relatonshp vares dependng on the opton s moneyness. We also nvestgate Bollen and Whaley s (2004) net buyng pressure argument. We fnd that mpled volatlty s related to open nterest (our proxy for opton demand), but not n the expected drecton. However, gven that open nterest s an mperfect measure of net buyng pressure, we cannot rule out Bollen and Whaley s explanaton altogether. Our fndngs do ndcate that ETF optons have more pronounced volatlty smles than ther equvalent ndex optons. Ths s drven prmarly by the fact that DITM (and, to a lesser extent, DOTM) ETF optons have hgher mpled volatltes. Although the precse reasons for ths are stll unknown, t s plausble that n some cases ETF optons may be more attractve nstruments for hedgng and speculaton. In any event, t s clear that they are not perfect substtutes for ndex optons. The paper has a natural lmtaton n the selecton of open nterest as a proxy for opton demand. Opton demand could be better measured wth the exact net buyng pressure varable computed usng the Bollen and Whaley procedure, whch utlzes ntraday opton data. However, gven that our dataset provdes only end-of-day opton prces, the computaton of the exact net buyng pressure metrc s not possble at ths stage. In a future study, we plan to address ths ssue after acqurng the ntraday opton data. Another nterestng extenson of ths paper would be a more detaled examnaton of Cube optons. Cubes swtched tradng from AMEX to NASDAQ on 12/1/2004, and n a future paper we plan to examne how ths change affected the mpled volatlty of the correspondng ETF and ndex optons. REFERENCES Baksh, G., Kapada, N., and Madan, D. (2003), Stock Return Characterstcs, Skew Laws, and the Dfferental Prcng of Indvdual Equty Optons. Revew of Fnancal Studes, 16(1), Bollen, N.P.B. and Whaley, R.E. (2004), Does Net Buyng Pressure Affect the Shape of Impled Volatlty Functons? Journal of Fnance, 59(2), Chan, K.C., Cheng, L.T.W., and Lung, P.P. (2004), Net Buyng Pressure, Volatlty Smle, and Abnormal Proft of Hang Seng Index Optons. Journal of Futures Markets, 24(12), Chan, K.C., Cheng, L.T.W., and Lung, P.P. (2006), Testng the Net Buyng Pressure Hypothess Durng the Asan Fnancal Crss: Evdence From Hang Seng Index Optons. Journal of Fnancal Research, 29(1), Chrstensen, B.J. and Prabhala, N.R. (1998), The Relaton Between Impled and Realzed Volatlty. Journal of Fnancal Economcs, 50(2), Day, T.E. and Lews, C.M. (1988), The Behavor of the Volatlty Implct n the Prces of Stock Index Optons. Journal of Fnancal Economcs, 22(1),

10 S. I. Ivanov et al IJBFR Vol. 5 No Engle, R. and Sarkar, D. (2006), Premums-Dscounts and Exchange Traded Funds. Journal of Dervatves, 13(4), Evnne, J. and Rudd, A. (1985), Index Optons: The Early Evdence. Journal of Fnance, 40(3), Kang, J., and Park, H. (2008), The Informaton Content of Net Buyng Pressure: Evdence from the KOSPI 200 Index Opton Market. Journal of Fnancal Markets, 11(1), Moran, M.T. (2003), Managng Costs and Rsks wth ETF Tools. Investment Gude Seres: Gude to Exchange-Traded Funds II, Peña, I., Rubo, G., and Serna, G. (1999), Why Do We Smle? On the Determnants of the Impled Volatlty Functon. Journal of Bankng and Fnance, 23(8), Pontff, J. (1997), Excess Volatlty and Closed-End Funds. Amercan Economc Revew, 87(1), Poterba, J.M. and Shoven, J.B. (2002), Exchange-Traded Funds: A New Investment Opton for Taxable Investors. Amercan Economc Revew, 92(2), Xu, X. and Taylor, S.J. (1994), The Term Structure of Volatlty Impled by Foregn Exchange Optons. Journal of Fnancal and Quanttatve Analyss, 29(1), ACKNOWLEDGEMENT We would lke to thank the journal edtors, Terrance Jalbert and Mercedes Jalbert, two anonymous referees, and partcpants at the Southwestern Fnance Assocaton and Mdwest Fnance Assocaton meetngs for ther nsghtful comments. Any errors are our own. BIOGRAPHY Dr. Stoyu Ivanov s an Assstant Professor of Fnance at San Jose State Unversty. He can be contacted at: Accountng and Fnance, BT 958, One Washngton Square, San Jose, CA Phone: Emal: Dr. Jeff Whtworth s an Assstant Professor of Fnance at the Unversty of Houston-Clear Lake. He can be contacted at: 2700 Bay Area Blvd., Houston, TX Phone: Emal: Dr. Y Zhang s an Assstant Professor of Fnance at Prare Vew A&M Unversty. She can be contacted at: Department of Accountng, Fnance & MIS, College of Busness, Mal Stop 2310, T.R. Solomon St. Hobart Taylor Bldg, Rm1B118, Prare Vew, TX Phone: E-mal: 44

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Can Auto Liability Insurance Purchases Signal Risk Attitude? Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

More information

The impact of hard discount control mechanism on the discount volatility of UK closed-end funds

The impact of hard discount control mechanism on the discount volatility of UK closed-end funds Investment Management and Fnancal Innovatons, Volume 10, Issue 3, 2013 Ahmed F. Salhn (Egypt) The mpact of hard dscount control mechansm on the dscount volatlty of UK closed-end funds Abstract The mpact

More information

Scale Dependence of Overconfidence in Stock Market Volatility Forecasts

Scale Dependence of Overconfidence in Stock Market Volatility Forecasts Scale Dependence of Overconfdence n Stoc Maret Volatlty Forecasts Marus Glaser, Thomas Langer, Jens Reynders, Martn Weber* June 7, 007 Abstract In ths study, we analyze whether volatlty forecasts (judgmental

More information

EXCHANGE TRADED FUNDS AND INDEX ANALYSIS: VOLATILITY AND OPTIONS

EXCHANGE TRADED FUNDS AND INDEX ANALYSIS: VOLATILITY AND OPTIONS EXCHANGE TRADED FUNDS AND INDEX ANALYSIS: VOLATILITY AND OPTIONS Stoyu I. Ivanov, University of Nebraska - Lincoln Yi Zhang 1, Prairie View A&M University Abstract Exchange Traded Funds (ETFs) track their

More information

On the pricing of illiquid options with Black-Scholes formula

On the pricing of illiquid options with Black-Scholes formula 7 th InternatonalScentfcConferenceManagngandModellngofFnancalRsks Ostrava VŠB-TU Ostrava, Faculty of Economcs, Department of Fnance 8 th 9 th September2014 On the prcng of llqud optons wth Black-Scholes

More information

An Alternative Way to Measure Private Equity Performance

An Alternative Way to Measure Private Equity Performance An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

More information

Macro Factors and Volatility of Treasury Bond Returns

Macro Factors and Volatility of Treasury Bond Returns Macro Factors and Volatlty of Treasury Bond Returns Jngzh Huang Department of Fnance Smeal Colleage of Busness Pennsylvana State Unversty Unversty Park, PA 16802, U.S.A. Le Lu School of Fnance Shangha

More information

Two Faces of Intra-Industry Information Transfers: Evidence from Management Earnings and Revenue Forecasts

Two Faces of Intra-Industry Information Transfers: Evidence from Management Earnings and Revenue Forecasts Two Faces of Intra-Industry Informaton Transfers: Evdence from Management Earnngs and Revenue Forecasts Yongtae Km Leavey School of Busness Santa Clara Unversty Santa Clara, CA 95053-0380 TEL: (408) 554-4667,

More information

World currency options market efficiency

World currency options market efficiency Arful Hoque (Australa) World optons market effcency Abstract The World Currency Optons (WCO) maket began tradng n July 2007 on the Phladelpha Stock Exchange (PHLX) wth the new features. These optons are

More information

DO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS?

DO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS? DO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS? Fernando Comran, Unversty of San Francsco, School of Management, 2130 Fulton Street, CA 94117, Unted States, fcomran@usfca.edu Tatana Fedyk,

More information

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy Fnancal Tme Seres Analyss Patrck McSharry patrck@mcsharry.net www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton

More information

Informational Content of Option Trading on Acquirer Announcement Return * National Chengchi University. The University of Hong Kong.

Informational Content of Option Trading on Acquirer Announcement Return * National Chengchi University. The University of Hong Kong. Informatonal Content of Opton Tradng on Acqurer Announcement Return * Konan Chan a, b,, L Ge b,, and Tse-Chun Ln b, a Natonal Chengch Unversty b The Unversty of Hong Kong Aprl, 2012 Abstract Ths paper

More information

The Investor Recognition Hypothesis:

The Investor Recognition Hypothesis: The Investor Recognton Hypothess: the New Zealand Penny Stocks Danel JP Cha, Department of Accountng and Fnance, onash Unversty, Clayton 3168, elbourne, Australa, and Danel FS Cho, Department of Fnance,

More information

Online Appendix Supplemental Material for Market Microstructure Invariance: Empirical Hypotheses

Online Appendix Supplemental Material for Market Microstructure Invariance: Empirical Hypotheses Onlne Appendx Supplemental Materal for Market Mcrostructure Invarance: Emprcal Hypotheses Albert S. Kyle Unversty of Maryland akyle@rhsmth.umd.edu Anna A. Obzhaeva New Economc School aobzhaeva@nes.ru Table

More information

Day-of-the-Week Trading Patterns of Individual and Institutional Investors

Day-of-the-Week Trading Patterns of Individual and Institutional Investors Day-of-the-Week Tradng Patterns of Indvdual and Instutonal Investors Joel N. Morse, Hoang Nguyen, and Hao M. Quach Ths study examnes the day-of-the-week tradng patterns of ndvdual and nstutonal nvestors.

More information

Simon Acomb NAG Financial Mathematics Day

Simon Acomb NAG Financial Mathematics Day 1 Why People Who Prce Dervatves Are Interested In Correlaton mon Acomb NAG Fnancal Mathematcs Day Correlaton Rsk What Is Correlaton No lnear relatonshp between ponts Co-movement between the ponts Postve

More information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12 14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed

More information

Management Quality, Financial and Investment Policies, and. Asymmetric Information

Management Quality, Financial and Investment Policies, and. Asymmetric Information Management Qualty, Fnancal and Investment Polces, and Asymmetrc Informaton Thomas J. Chemmanur * Imants Paegls ** and Karen Smonyan *** Current verson: December 2007 * Professor of Fnance, Carroll School

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

Bond futures. Bond futures contracts are futures contracts that allow investor to buy in the

Bond futures. Bond futures contracts are futures contracts that allow investor to buy in the Bond futures INRODUCION Bond futures contracts are futures contracts that allow nvestor to buy n the future a theoretcal government notonal bond at a gven prce at a specfc date n a gven quantty. Compared

More information

The Application of Fractional Brownian Motion in Option Pricing

The Application of Fractional Brownian Motion in Option Pricing Vol. 0, No. (05), pp. 73-8 http://dx.do.org/0.457/jmue.05.0..6 The Applcaton of Fractonal Brownan Moton n Opton Prcng Qng-xn Zhou School of Basc Scence,arbn Unversty of Commerce,arbn zhouqngxn98@6.com

More information

Forecasting the Direction and Strength of Stock Market Movement

Forecasting the Direction and Strength of Stock Market Movement Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract - Stock market s one of the most complcated systems

More information

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt. Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces

More information

Inequality and The Accounting Period. Quentin Wodon and Shlomo Yitzhaki. World Bank and Hebrew University. September 2001.

Inequality and The Accounting Period. Quentin Wodon and Shlomo Yitzhaki. World Bank and Hebrew University. September 2001. Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.

More information

Price Risk and Bid-Ask Spreads of Currency Options

Price Risk and Bid-Ask Spreads of Currency Options Prce Rsk and Bd-Ask Spreads of Currency Optons By Mara E. de Boyre New Mexco State Unversty Department of Fnance, MSC 3FIN P.O. Box 30001 Las Cruces, NM 88003-8001 Phone: (505)646-3252; Fax: (505)646-2820

More information

IN THE UNITED STATES THIS REPORT IS AVAILABLE ONLY TO PERSONS WHO HAVE RECEIVED THE PROPER OPTION RISK DISCLOSURE DOCUMENTS.

IN THE UNITED STATES THIS REPORT IS AVAILABLE ONLY TO PERSONS WHO HAVE RECEIVED THE PROPER OPTION RISK DISCLOSURE DOCUMENTS. http://mm.pmorgan.com European Equty Dervatves Strategy 4 May 005 N THE UNTED STATES THS REPORT S AVALABLE ONLY TO PERSONS WHO HAVE RECEVED THE PROPER OPTON RS DSCLOSURE DOCUMENTS. Correlaton Vehcles Technques

More information

CHAPTER 14 MORE ABOUT REGRESSION

CHAPTER 14 MORE ABOUT REGRESSION CHAPTER 14 MORE ABOUT REGRESSION We learned n Chapter 5 that often a straght lne descrbes the pattern of a relatonshp between two quanttatve varables. For nstance, n Example 5.1 we explored the relatonshp

More information

The Cox-Ross-Rubinstein Option Pricing Model

The Cox-Ross-Rubinstein Option Pricing Model Fnance 400 A. Penat - G. Pennacc Te Cox-Ross-Rubnsten Opton Prcng Model Te prevous notes sowed tat te absence o arbtrage restrcts te prce o an opton n terms o ts underlyng asset. However, te no-arbtrage

More information

Construction Rules for Morningstar Canada Target Dividend Index SM

Construction Rules for Morningstar Canada Target Dividend Index SM Constructon Rules for Mornngstar Canada Target Dvdend Index SM Mornngstar Methodology Paper October 2014 Verson 1.2 2014 Mornngstar, Inc. All rghts reserved. The nformaton n ths document s the property

More information

The DAX and the Dollar: The Economic Exchange Rate Exposure of German Corporations

The DAX and the Dollar: The Economic Exchange Rate Exposure of German Corporations The DAX and the Dollar: The Economc Exchange Rate Exposure of German Corporatons Martn Glaum *, Marko Brunner **, Holger Hmmel *** Ths paper examnes the economc exposure of German corporatons to changes

More information

The Impact of Stock Index Futures Trading on Daily Returns Seasonality: A Multicountry Study

The Impact of Stock Index Futures Trading on Daily Returns Seasonality: A Multicountry Study The Impact of Stock Index Futures Tradng on Daly Returns Seasonalty: A Multcountry Study Robert W. Faff a * and Mchael D. McKenze a Abstract In ths paper we nvestgate the potental mpact of the ntroducton

More information

The OC Curve of Attribute Acceptance Plans

The OC Curve of Attribute Acceptance Plans The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4

More information

Gender differences in revealed risk taking: evidence from mutual fund investors

Gender differences in revealed risk taking: evidence from mutual fund investors Economcs Letters 76 (2002) 151 158 www.elsever.com/ locate/ econbase Gender dfferences n revealed rsk takng: evdence from mutual fund nvestors a b c, * Peggy D. Dwyer, James H. Glkeson, John A. Lst a Unversty

More information

Staff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall

Staff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall SP 2005-02 August 2005 Staff Paper Department of Appled Economcs and Management Cornell Unversty, Ithaca, New York 14853-7801 USA Farm Savngs Accounts: Examnng Income Varablty, Elgblty, and Benefts Brent

More information

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

More information

STAMP DUTY ON SHARES AND ITS EFFECT ON SHARE PRICES

STAMP DUTY ON SHARES AND ITS EFFECT ON SHARE PRICES STAMP UTY ON SHARES AN ITS EFFECT ON SHARE PRICES Steve Bond Mke Hawkns Alexander Klemm THE INSTITUTE FOR FISCAL STUIES WP04/11 STAMP UTY ON SHARES AN ITS EFFECT ON SHARE PRICES Steve Bond (IFS and Unversty

More information

A Simplified Framework for Return Accountability

A Simplified Framework for Return Accountability Reprnted wth permsson from Fnancal Analysts Journal, May/June 1991. Copyrght 1991. Assocaton for Investment Management and Research, Charlottesvlle, VA. All rghts reserved. by Gary P. Brnson, Bran D. Snger

More information

Multiple-Period Attribution: Residuals and Compounding

Multiple-Period Attribution: Residuals and Compounding Multple-Perod Attrbuton: Resduals and Compoundng Our revewer gave these authors full marks for dealng wth an ssue that performance measurers and vendors often regard as propretary nformaton. In 1994, Dens

More information

The timing ability of hybrid funds of funds

The timing ability of hybrid funds of funds The tmng ablty of hybrd funds of funds Javer Rodríguez* Graduate School of Busness Admnstraton Unversty of Puerto Rco PO 23332 San Juan, PR 00931 Abstract Hybrd mutual funds are funds that nvest n a combnaton

More information

Momentum Trading, Mean Reversal and Overreaction in Chinese Stock Market *

Momentum Trading, Mean Reversal and Overreaction in Chinese Stock Market * Momentum Tradng, Mean Reversal and Overreacton n Chnese Stock Market * YANGRU WU Rutgers Unversty and Hong Kong Insttute for Monetary Research December 2003 (Prelmnary, Comments Welcome) ABSTRACT Whle

More information

Using Series to Analyze Financial Situations: Present Value

Using Series to Analyze Financial Situations: Present Value 2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated

More information

This study examines whether the framing mode (narrow versus broad) influences the stock investment decisions

This study examines whether the framing mode (narrow versus broad) influences the stock investment decisions MANAGEMENT SCIENCE Vol. 54, No. 6, June 2008, pp. 1052 1064 ssn 0025-1909 essn 1526-5501 08 5406 1052 nforms do 10.1287/mnsc.1070.0845 2008 INFORMS How Do Decson Frames Influence the Stock Investment Choces

More information

Fixed income risk attribution

Fixed income risk attribution 5 Fxed ncome rsk attrbuton Chthra Krshnamurth RskMetrcs Group chthra.krshnamurth@rskmetrcs.com We compare the rsk of the actve portfolo wth that of the benchmark and segment the dfference between the two

More information

Stress test for measuring insurance risks in non-life insurance

Stress test for measuring insurance risks in non-life insurance PROMEMORIA Datum June 01 Fnansnspektonen Författare Bengt von Bahr, Younes Elonq and Erk Elvers Stress test for measurng nsurance rsks n non-lfe nsurance Summary Ths memo descrbes stress testng of nsurance

More information

International Commodity Prices and the Australian Stock Market

International Commodity Prices and the Australian Stock Market Internatonal Commodty Prces and the Australan Stock Market Chrs Heaton, George Mlunovch and Anthony Passé-de Slva Abstract We propose a method for estmatng the earlest tme durng the tradng day when overnght

More information

The Analysis of Outliers in Statistical Data

The Analysis of Outliers in Statistical Data THALES Project No. xxxx The Analyss of Outlers n Statstcal Data Research Team Chrysses Caron, Assocate Professor (P.I.) Vaslk Karot, Doctoral canddate Polychrons Economou, Chrstna Perrakou, Postgraduate

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

More information

Beating the Odds: Arbitrage and Wining Strategies in the Football Betting Market

Beating the Odds: Arbitrage and Wining Strategies in the Football Betting Market Beatng the Odds: Arbtrage and Wnng Strateges n the Football Bettng Market NIKOLAOS VLASTAKIS, GEORGE DOTSIS and RAPHAEL N. MARKELLOS* ABSTRACT We examne the potental for generatng postve returns from wagerng

More information

1. Measuring association using correlation and regression

1. Measuring association using correlation and regression How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a

More information

Analysis of Premium Liabilities for Australian Lines of Business

Analysis of Premium Liabilities for Australian Lines of Business Summary of Analyss of Premum Labltes for Australan Lnes of Busness Emly Tao Honours Research Paper, The Unversty of Melbourne Emly Tao Acknowledgements I am grateful to the Australan Prudental Regulaton

More information

Student Performance in Online Quizzes as a Function of Time in Undergraduate Financial Management Courses

Student Performance in Online Quizzes as a Function of Time in Undergraduate Financial Management Courses Student Performance n Onlne Quzzes as a Functon of Tme n Undergraduate Fnancal Management Courses Olver Schnusenberg The Unversty of North Florda ABSTRACT An nterestng research queston n lght of recent

More information

THE DETERMINANTS OF THE TUNISIAN BANKING INDUSTRY PROFITABILITY: PANEL EVIDENCE

THE DETERMINANTS OF THE TUNISIAN BANKING INDUSTRY PROFITABILITY: PANEL EVIDENCE THE DETERMINANTS OF THE TUNISIAN BANKING INDUSTRY PROFITABILITY: PANEL EVIDENCE Samy Ben Naceur ERF Research Fellow Department of Fnance Unversté Lbre de Tuns Avenue Khéreddne Pacha, 002 Tuns Emal : sbennaceur@eudoramal.com

More information

Volatility of Stock Return Variance and Capital Gains Tax

Volatility of Stock Return Variance and Capital Gains Tax Volatlty of Stock Return Varance and Captal Gans Tax Xa Meng, Junbo Wang, Zhpeng Yan, Yan Zhao 1 Ths Draft: Oct 17, 011 bstract In ths paper, we develop a two-perod portfolo selecton model wth dfferental

More information

Do stock prices underreact to SEO announcements? Evidence from SEO Valuation

Do stock prices underreact to SEO announcements? Evidence from SEO Valuation Do stock prces underreact to SEO announcements? Evdence from SEO Valuaton Amyatosh K. Purnanandam Bhaskaran Swamnathan * Frst Draft: December 2005 Comments Welcome * Purnanandam s an Assstant Professor

More information

Akira Yanagisawa Leader Energy Demand, Supply and Forecast Analysis Group Energy Data and Modelling Center

Akira Yanagisawa Leader Energy Demand, Supply and Forecast Analysis Group Energy Data and Modelling Center Background of Surgng Ol Prces and Market Expectaton Seen n Optons Akra Yanagsawa Leader Energy Demand, Supply and Forecast Analyss Group Energy Data and Modellng Center Summary The crude ol prces (WTI

More information

A Model of Private Equity Fund Compensation

A Model of Private Equity Fund Compensation A Model of Prvate Equty Fund Compensaton Wonho Wlson Cho Andrew Metrck Ayako Yasuda KAIST Yale School of Management Unversty of Calforna at Davs June 26, 2011 Abstract: Ths paper analyzes the economcs

More information

Question 2: What is the variance and standard deviation of a dataset?

Question 2: What is the variance and standard deviation of a dataset? Queston 2: What s the varance and standard devaton of a dataset? The varance of the data uses all of the data to compute a measure of the spread n the data. The varance may be computed for a sample of

More information

Transition Matrix Models of Consumer Credit Ratings

Transition Matrix Models of Consumer Credit Ratings Transton Matrx Models of Consumer Credt Ratngs Abstract Although the corporate credt rsk lterature has many studes modellng the change n the credt rsk of corporate bonds over tme, there s far less analyss

More information

Management Quality and Equity Issue Characteristics: A Comparison of SEOs and IPOs

Management Quality and Equity Issue Characteristics: A Comparison of SEOs and IPOs Management Qualty and Equty Issue Characterstcs: A Comparson of SEOs and IPOs Thomas J. Chemmanur * Imants Paegls ** and Karen Smonyan *** Current verson: November 2009 (Accepted, Fnancal Management, February

More information

HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA*

HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA* HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA* Luísa Farnha** 1. INTRODUCTION The rapd growth n Portuguese households ndebtedness n the past few years ncreased the concerns that debt

More information

DEFINING %COMPLETE IN MICROSOFT PROJECT

DEFINING %COMPLETE IN MICROSOFT PROJECT CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,

More information

EUROPEAN. ThePriceandRiskEfects ofoptionintroductionsonthenordicmarkets. EconomicPapers434 December2010. StafanLindén EUROPEANCOMMISSION

EUROPEAN. ThePriceandRiskEfects ofoptionintroductionsonthenordicmarkets. EconomicPapers434 December2010. StafanLindén EUROPEANCOMMISSION EUROPEAN ECONOMY EconomcPapers434 December heprceandrskefects ofoptonintroductonsonthenordcmarkets StafanLndén EUROPEANCOMMISSION Economc Papers are wrtten by the Staff of the Drectorate-General for Economc

More information

SIMPLE LINEAR CORRELATION

SIMPLE LINEAR CORRELATION SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.

More information

Transaction Costs and Strategic Trading of German Investment Management Firms: Empirical Evidence from European Stock Markets

Transaction Costs and Strategic Trading of German Investment Management Firms: Empirical Evidence from European Stock Markets Transacton Costs and Strategc Tradng of German Investment Management Frms: Emprcal Evdence from European Stock Markets Lutz Johannng* Endowed Char for Asset Management European Busness School Schloß Rechartshausen

More information

Understanding the Impact of Marketing Actions in Traditional Channels on the Internet: Evidence from a Large Scale Field Experiment

Understanding the Impact of Marketing Actions in Traditional Channels on the Internet: Evidence from a Large Scale Field Experiment A research and educaton ntatve at the MT Sloan School of Management Understandng the mpact of Marketng Actons n Tradtonal Channels on the nternet: Evdence from a Large Scale Feld Experment Paper 216 Erc

More information

The Probability of Informed Trading and the Performance of Stock in an Order-Driven Market

The Probability of Informed Trading and the Performance of Stock in an Order-Driven Market Asa-Pacfc Journal of Fnancal Studes (2007) v36 n6 pp871-896 The Probablty of Informed Tradng and the Performance of Stock n an Order-Drven Market Ta Ma * Natonal Sun Yat-Sen Unversty, Tawan Mng-hua Hseh

More information

THE EFFECT OF PREPAYMENT PENALTIES ON THE PRICING OF SUBPRIME MORTGAGES

THE EFFECT OF PREPAYMENT PENALTIES ON THE PRICING OF SUBPRIME MORTGAGES THE EFFECT OF PREPAYMENT PENALTIES ON THE PRICING OF SUBPRIME MORTGAGES Gregory Ellehausen, Fnancal Servces Research Program George Washngton Unversty Mchael E. Staten, Fnancal Servces Research Program

More information

Corporate Real Estate Sales and Agency Costs of Managerial Discretion

Corporate Real Estate Sales and Agency Costs of Managerial Discretion Corporate Real Estate Sales and Agency Costs of Manageral Dscreton Mng-Long Lee * Department of Fnance Natonal Yunln Unversty of Scence & Technology Yunln, Tawan Mng-Te Lee Department of Accountng Tamkang

More information

The Analysis of Covariance. ERSH 8310 Keppel and Wickens Chapter 15

The Analysis of Covariance. ERSH 8310 Keppel and Wickens Chapter 15 The Analyss of Covarance ERSH 830 Keppel and Wckens Chapter 5 Today s Class Intal Consderatons Covarance and Lnear Regresson The Lnear Regresson Equaton TheAnalyss of Covarance Assumptons Underlyng the

More information

Interest Rate Futures

Interest Rate Futures Interest Rate Futures Chapter 6 6.1 Day Count Conventons n the U.S. (Page 129) Treasury Bonds: Corporate Bonds: Money Market Instruments: Actual/Actual (n perod) 30/360 Actual/360 The day count conventon

More information

Accounting Discretion of Banks During a Financial Crisis

Accounting Discretion of Banks During a Financial Crisis WP/09/207 Accountng Dscreton of Banks Durng a Fnancal Crss Harry Huznga and Luc Laeven 2009 Internatonal Monetary Fund WP/09/207 IMF Workng Paper Research Department Accountng dscreton of banks durng a

More information

Section 5.4 Annuities, Present Value, and Amortization

Section 5.4 Annuities, Present Value, and Amortization Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today

More information

Sulaiman Mouselli Damascus University, Damascus, Syria. and. Khaled Hussainey* Stirling University, Stirling, UK

Sulaiman Mouselli Damascus University, Damascus, Syria. and. Khaled Hussainey* Stirling University, Stirling, UK CORPORATE GOVERNANCE, ANALYST FOLLOWING AND FIRM VALUE Sulaman Mousell Damascus Unversty, Damascus, Syra and Khaled Hussaney* Strlng Unversty, Strlng, UK Ths paper s accepted for publcaton at: Corporate

More information

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET *

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET * ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET * Amy Fnkelsten Harvard Unversty and NBER James Poterba MIT and NBER * We are grateful to Jeffrey Brown, Perre-Andre

More information

Passive Filters. References: Barbow (pp 265-275), Hayes & Horowitz (pp 32-60), Rizzoni (Chap. 6)

Passive Filters. References: Barbow (pp 265-275), Hayes & Horowitz (pp 32-60), Rizzoni (Chap. 6) Passve Flters eferences: Barbow (pp 6575), Hayes & Horowtz (pp 360), zzon (Chap. 6) Frequencyselectve or flter crcuts pass to the output only those nput sgnals that are n a desred range of frequences (called

More information

Guide to the Volatility Indices of Deutsche Börse

Guide to the Volatility Indices of Deutsche Börse Volatlty Indces of Deutsche Börse Verson.4 Volatlty Indces of Deutschen Börse Page General Informaton In order to ensure the hghest qualty of each of ts ndces, Deutsche Börse AG exercses the greatest care

More information

The Short-term and Long-term Market

The Short-term and Long-term Market A Presentaton on Market Effcences to Northfeld Informaton Servces Annual Conference he Short-term and Long-term Market Effcences en Post Offce Square Boston, MA 0209 www.acadan-asset.com Charles H. Wang,

More information

HARVARD John M. Olin Center for Law, Economics, and Business

HARVARD John M. Olin Center for Law, Economics, and Business HARVARD John M. Oln Center for Law, Economcs, and Busness ISSN 1045-6333 ASYMMETRIC INFORMATION AND LEARNING IN THE AUTOMOBILE INSURANCE MARKET Alma Cohen Dscusson Paper No. 371 6/2002 Harvard Law School

More information

IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS

IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS Chrs Deeley* Last revsed: September 22, 200 * Chrs Deeley s a Senor Lecturer n the School of Accountng, Charles Sturt Unversty,

More information

An Empirical Study of Search Engine Advertising Effectiveness

An Empirical Study of Search Engine Advertising Effectiveness An Emprcal Study of Search Engne Advertsng Effectveness Sanjog Msra, Smon School of Busness Unversty of Rochester Edeal Pnker, Smon School of Busness Unversty of Rochester Alan Rmm-Kaufman, Rmm-Kaufman

More information

SPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME

SPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME August 7 - August 12, 2006 n Baden-Baden, Germany SPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME Vladmr Šmovć 1, and Vladmr Šmovć 2, PhD 1 Faculty of Electrcal Engneerng and Computng, Unska 3, 10000

More information

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET Amy Fnkelsten Harvard Unversty and NBER James Poterba MIT and NBER Revsed May 2003 ABSTRACT In ths paper, we nvestgate

More information

Exhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation

Exhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation Exhaustve Regresson An Exploraton of Regresson-Based Data Mnng Technques Usng Super Computaton Antony Daves, Ph.D. Assocate Professor of Economcs Duquesne Unversty Pttsburgh, PA 58 Research Fellow The

More information

CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol

CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL

More information

PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIGIOUS AFFILIATION AND PARTICIPATION

PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIGIOUS AFFILIATION AND PARTICIPATION PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIIOUS AFFILIATION AND PARTICIPATION Danny Cohen-Zada Department of Economcs, Ben-uron Unversty, Beer-Sheva 84105, Israel Wllam Sander Department of Economcs, DePaul

More information

Discount Rate for Workout Recoveries: An Empirical Study*

Discount Rate for Workout Recoveries: An Empirical Study* Dscount Rate for Workout Recoveres: An Emprcal Study* Brooks Brady Amercan Express Peter Chang Standard & Poor s Peter Mu** McMaster Unversty Boge Ozdemr Standard & Poor s Davd Schwartz Federal Reserve

More information

The Journal of Applied Business Research January/February 2010 Volume 26, Number 1

The Journal of Applied Business Research January/February 2010 Volume 26, Number 1 Product Dversfcaton In Compettve R&D-Intensve Frms: An Emprcal Study Of The Computer Software Industry C. Catherne Chang, Elon Unversty, USA ABSTRACT Ths paper studes the effect of dversfcaton nto dfferent

More information

CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES

CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES In ths chapter, we wll learn how to descrbe the relatonshp between two quanttatve varables. Remember (from Chapter 2) that the terms quanttatve varable

More information

THE efficient market hypothesis (EMH) asserts that financial. Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data

THE efficient market hypothesis (EMH) asserts that financial. Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data 1 Predctng Fnancal Markets: Comparng Survey, News, Twtter and Search Engne Data Huna Mao, Indana Unversty-Bloomngton, Scott Counts, Mcrosoft Research, and Johan Bollen, Indana Unversty-Bloomngton arxv:1112.1051v1

More information

Is the home bias in equities and bonds declining in Europe?

Is the home bias in equities and bonds declining in Europe? Drk Schoenmaker (Netherlands), Thjs Bosch (Netherlands) Is the home bas n equtes and bonds declnng n Europe? Abstract Fnance theory suggests that nvestors should hold an nternatonally dversfed portfolo.

More information

When Talk is Free : The Effect of Tariff Structure on Usage under Two- and Three-Part Tariffs

When Talk is Free : The Effect of Tariff Structure on Usage under Two- and Three-Part Tariffs 0 When Talk s Free : The Effect of Tarff Structure on Usage under Two- and Three-Part Tarffs Eva Ascarza Ana Lambrecht Naufel Vlcassm July 2012 (Forthcomng at Journal of Marketng Research) Eva Ascarza

More information

Rise of Cross-Asset Correlations

Rise of Cross-Asset Correlations Global Equty Dervatves & Delta One Strategy Rse of Cross-Asset Correlatons Asset Class Roadmap for Equty Investors Summary Cross-Asset Correlatons: Over the past ten years, cross-asset correlatons roughly

More information

New evidence of the impact of dividend taxation and on the identity of the marginal investor

New evidence of the impact of dividend taxation and on the identity of the marginal investor New evdence of the mpact of dvdend taxaton and on the dentty of the margnal nvestor LEONIE BELL AND TIM JENKINSON * * Economcs Department, Oxford Unversty and Saïd Busness School, Oxford Unversty and CEPR

More information

Pricing Multi-Asset Cross Currency Options

Pricing Multi-Asset Cross Currency Options CIRJE-F-844 Prcng Mult-Asset Cross Currency Optons Kenchro Shraya Graduate School of Economcs, Unversty of Tokyo Akhko Takahash Unversty of Tokyo March 212; Revsed n September, October and November 212

More information

Cautiousness and Measuring An Investor s Tendency to Buy Options

Cautiousness and Measuring An Investor s Tendency to Buy Options Cautousness and Measurng An Investor s Tendency to Buy Optons James Huang October 18, 2005 Abstract As s well known, Arrow-Pratt measure of rsk averson explans a ratonal nvestor s behavor n stock markets

More information

Trade Adjustment and Productivity in Large Crises. Online Appendix May 2013. Appendix A: Derivation of Equations for Productivity

Trade Adjustment and Productivity in Large Crises. Online Appendix May 2013. Appendix A: Derivation of Equations for Productivity Trade Adjustment Productvty n Large Crses Gta Gopnath Department of Economcs Harvard Unversty NBER Brent Neman Booth School of Busness Unversty of Chcago NBER Onlne Appendx May 2013 Appendx A: Dervaton

More information

10.2 Future Value and Present Value of an Ordinary Simple Annuity

10.2 Future Value and Present Value of an Ordinary Simple Annuity 348 Chapter 10 Annutes 10.2 Future Value and Present Value of an Ordnary Smple Annuty In compound nterest, 'n' s the number of compoundng perods durng the term. In an ordnary smple annuty, payments are

More information

ECONOMICS OF PLANT ENERGY SAVINGS PROJECTS IN A CHANGING MARKET Douglas C White Emerson Process Management

ECONOMICS OF PLANT ENERGY SAVINGS PROJECTS IN A CHANGING MARKET Douglas C White Emerson Process Management ECONOMICS OF PLANT ENERGY SAVINGS PROJECTS IN A CHANGING MARKET Douglas C Whte Emerson Process Management Abstract Energy prces have exhbted sgnfcant volatlty n recent years. For example, natural gas prces

More information

Are Women Better Loan Officers?

Are Women Better Loan Officers? Are Women Better Loan Offcers? Ths verson: February 2009 Thorsten Beck * CentER, Dept. of Economcs, Tlburg Unversty and CEPR Patrck Behr Goethe Unversty Frankfurt André Güttler European Busness School

More information

7.5. Present Value of an Annuity. Investigate

7.5. Present Value of an Annuity. Investigate 7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on

More information