Table 2 Hedonic regression of natural log of hammer price on the natural log of scrap value and other characteristics. Variable Predicted sign Full sample Coeff. (1) Bonhams Coeff. (3) 0.485 -0.239 0.317** -0.100 0.187 In (scrap) Spread In (age) Order QI 02 prov assay off Constant Observations Description dummies Place dummies Year dummies Quarter dummies Adj. R-squared See Table 1 for variable ove v eve 7 eve eve eve NA Table 3 Identifying arbitrage opportunities Arbitrage category P.Draper et al/Economics Letters 162 (2018) 45-48 Price Scrap Price Scrap Price & buyer's premium Scrap No lots sold 9924 690 56 0.457** -0.248- 0.411*** 0.129** 0.240 0.056*** definitions.p<0.01,"p<0.05,"p<0.1. 0.602*** 0.050*** 05 M -0.071 1.850 10,614 (11) (3) (5) (3) 0.547 regression: In(Price),= d+a, In(Scrap),+Characteristics, Std. err (2) (0.008) (0.046) (0.018) (0.033) (0.029) (0.016) (0.061) (0.014) (0.015) (0.115) Total weight (oz.) 245,311 29,877 1.608 + Controls + e, (1) Where In Price), is the natural log of the auction lot's hammer price. and In(Scrap), is our main variable of interest, the natural log of the lot's scrap value, for which we expect a significant positive coefficient if buyers' consider the scrap value of a lot an important feature when bidding Characteristics, include characteristics found in the auction catalogues that may explain the auction price. Specifically, we control for uncertainty as the spread between the high and low pre-sale estimate, age as the natural log of the lot age, the lot position in the auction, two binary quality variables to indicate if the lot was made by a notable silversmith working in the twentieth century (Q1) or earlier (Q2), and three binary variables to indicate provenance, the presence of an armorial or other inscription, and whether the assay office was other than London. Finally, we include binary variables (Controls) to control for the lot description (e.g. flatware), place of sale, year of sale, and quarter within the year in which the lot was sold. 4. Empirical analysis and results Table 2 reports the hedonic regression results of Eq. (1) for the full sample, lots sold by Bonhams, and lots sold by Woolley and Wallis. In all three regressions, we use unreported dummy variables to control for the lot description, place of sale, year of sale and quarter within the year in which the lot was sold. We report robust standard errors using the White heteroskedasticity-robust procedure. For all three regressions, the scrap value of silver has a 2 The lot's scrap value is calculated as the product of the lot's weight in ounces and the corresponding non-trade scrap price of silver on the auction day from Cookson Precious Metals Ltd at http://www.cooksongold.com/. This represents the price at which they are prepared to trade on that day. Over our sample period the non-trade scrap price of silver per ounce ranges from £5.26 on 15 January 2009 to (21.38 on 28 April 2011, has a mean (standard deviation) of £11.03 (C353), and an average (standard deviation) daily change of 0.002 (0.295) 0.058*** 0.268** 0.047** 0.018 2.255*** 5.179 (11) (2) (5) (3) 0.638 Total price (K) 8.976,321 371,875 14,490 Std. err (4) (0.010) (0.055) (0.022) (0.043) (0034) (0.020) (0.092) (0018) (0.019) (0.134) Woolley and Wallis Coeff (5) 0.399 -0.061 0512** 0.421*** 0.240** 0.052 0772 0.061*** -0.104 LIO 5.435 (11) NA (5) (3) Total price-bp (0) 10,900,000 454229 17,679 47 Std. err. (6) (0.011) (0.074) (0.029) (0.052) (0.046) (0.025) (0.074) (0.021) (0.021) (0.180) Total scrap ( 2,734,583 398,286 20,792 significant positive effect on the hammer price and the remaining variables, where significant, are all of the predicted sign. In an auction environment, this implies that bidders are partly setting their bids based on the lot's scrap value. The evidence does not, however, confirm whether profitable arbitrage opportunities exist between the silver auctions and scrap value of the lots sold and purchased. If arbitrage opportunities exist, then there is prima facie ev- idence of market inefficiency. While this is a weak-form test of market efficiency, since it only uses price information, the absence of arbitrage is a very important characteristic of financial markets, which we test for in the physical silver market. Purchasers of silver goods are faced with a variety of costs, notably the buyer's premium. Therefore, we expect profitable arbitrage may be pos- sible before allowing for the buyer's premium but is most likely impossible once it is added into the price of the good. To test this, we identify and report summary statistics in Table 3 of all lots sold above and below their scrap value based on the hammer price paid to the seller, and all lots sold below the scrap value based on the hammer price and buyer's premium paid by the bidder. Our findings in Table 3 show that arbitrage opportunities are available if the only cost imposed on buyers is the hammer price. However, once the buyer's premium is included, arbitrage oppor- tunities almost completely disappear. Specifically, less than one percent of our sample would yield a profit of, on average, less than £60 per transaction, ignoring any physical costs of collection and resale. 5. Conclusions The absence of arbitrage opportunities after costs, particularly the buyer's premium, suggests that bidders are sensitive to the total price. Moreover, although scrap silver can be sold for cash at 3 The buyer's premium is a fee set by the auction house, and paid to the auction- eer by the buyer. This is constant for each auction, but varies between houses and over the sample period from 19.5-25% of the hammer price. 4 Analysis of the 56 lots for which arbitrage is possible indicates that 91% were sold by Bonhams and more than half in the fourth quarter. 46 Table 1 Descriptive statistics and sample distribution Panel A: Pooled sample statistics Variable Price Price bp Scrap Weight Spread Age Order Q1 02 prov arm assay off 2009 2010 2011 2012 2013 Mean 880.74 1073.17 295.16 25.93 0.39 166.06 2014 Quarter 1st 2nd 3rd 0.67 0.08 0.19 0.02 0.40 0.62 Std.dev. 1906.84 231157 441.80 37.03 0.13 Pape 77.75 0.25 0.26 0.39 0.15 0.49 0.48 Knightsbridge Obs. Price 551 667 Scrap 708.85 220.07 696.21 278.90 899.24 538.90 539 1089.38 645.23 447 1062.86 41761 332 1386.42 485.82 671 ART P. Draper et al/Economics Letters 162 (2018) 45-48 892.68 391.53 1134 928.36 424.30 393.99 895.67 959.65 452.56 424 1160 Min 20.00 2440 0.89 0.10 0.05 1.00 0.00 Panel B: Sample distribution of lots sold, average lot hammer price (C), and average lot scrap value (E) Year Bonhams Max. 85,00000 101,5790 6.211.80 553.00 1.00 $10.00 1.00 1.00 1.00 1.00 1.00 1.00 Knowle Obs. Price Scrap 402 382.44 179.87 298.34 476 458.25 472 596.60 459.47 291 465.10 275.94 394 488.48 324.64 296 487.53 306.71 369 358.25 49145 the bullion dealer at the prices quoted and with speedy or even immediate payment. The possibility, therefore, of selling for scrap provides a minimum guaranteed price, allowing sellers to realise cash with a minimum of restrictions. Both sales for scrap, and to antique dealers provide immediacy to the seller. They also provide certainty and low transaction costs. They do not however provide exposure to a wide range of buyers. The significant transactions costs associated with selling by auction suggests that sellers of silver goods that are of low quality and plentiful supply should sell direct to bullion or antique dealers to maximise their receipts from sale. Despite this, low quality silver is sold at auction. Selling for scrap removes any chance of receiving a premium for artistic merit or scarcity and there may be some sellers of low quality items who prefer, perhaps through ignorance. to sell by auction. High quality items should not be scrapped but sold to dealers or by auction. Many silver goods have some worth relating to their scarcity. usefulness, artistic merit and quality of workmanship. A number of items, however, have no such merit. They may have been mass produced and of poor quality originally, or may be battered and broken. The demand for such items is slight. They are of limited interest to collectors or dealers. They do however retain value, a value that depends on their silver content. They may be easily melted and reduced to pure silver. In an efficient market, we would expect any silver item to have a minimum value directly related to its scrap value. Description Hammer price of lot (C) Hammer price of lot including the buyer's premium (E) Scrap value of lot(C) Weight of lot (ounces) 2. Sample and data To examine the role that the scrap value plays in determining the price of silver items sold at auction, along with any possible opportunities for riskless arbitrage, we construct a unique database from the catalogues of 88 silver auctions from two major En- glish auction houses, Bonhams, and Woolley and Wallis, between Auctioneer's spread (High estimate-Low estimate)Low estimate) Age of lot (years) Order of lot in the auction Dummy (1 if notable 20th century maker: 0 otherwise) Dummy (= 1 if notable pre-20th century makers: O otherwise) Dummy (1if provenance: 0 otherwise) Dummy (1 if armorial; 0 otherwise) Dummy (1if London assay office: 0 otherwise) New Bond Street Obs. Price Scrap 148 3245.54 281.95 377.16 132 361477 146 3987.33 741.78 61 3179.48 67479 407473 798.47 5035.71 685.61 93 274 3748.17 635.91 72 2750.69 285.49 276 398184 541.78 Woolley and Wallis Salisbury Obs Price 567 828 478.66 920.42 986 684.69 1018 580.38 1080 559.84 956 506.69 1180.00 549.56 1376.00 691.92 1415.00 60724 1464.00 634.33 Scrap 81.93 192.16 288.25 211.68 162.78 116.80 170.31 186.19 186.37 185.68 January 2009 and December 2014. Although no statistics are available for the proportion of antique silver sold by dealers in the UK, informed sources have suggested that, while dealers are responsible for about half of annual sales, auction houses have become more dominant in the market. Bonhams is the third major international UK auction house and the largest seller of UK antique silver by volume over our sample period, and Woolley and Wallis is the UK's largest regional auction house and the second largest seller of UK antique silver by volume. Since it is common for auctioneers to exclude some of the available pre-sale information from the online data (Campos and Barbosa, 2009), copies of the physical catalogues are used. Whilst attempting to be as comprehensive as possible we exclude unsold lots, mixed collections (since it is impossible to assign detailed information to the individual pieces), and lots without a pre- sale estimate. Our final sample contains information on 10,614 sold lots spread across three Bonhams locations, Knightsbridge (30%), Knowle (13%), New Bond Street (6%), and Woolley and Wallis house in Salisbury (51%). In terms of the timing of the silver auctions, Woolley and Wallis hold auctions quarterly, while Bonhams have departed from a fixed schedule in recent years, and closed their knowle salesroom in 2011. Table 1 reports the main descriptive statistics and sample distribution of the dataset described above. 3. A hedonic price model for silver goods To examine the relation between the hammer price and the scrap value of the auction lot, we use the following hedonic pricing 1 Unlike other studies, we do not concentrate on the international auction houses, Christies and Sotheby's, since both rarely hold specialist silver auctions in London over the sample period.
Table 2 Hedonic regression of natural log of hammer price on the natural log of scrap value and other characteristics. Variable Predicted sign Full sample Coeff. (1) Bonhams Coeff. (3) 0.485 -0.239 0.317** -0.100 0.187 In (scrap) Spread In (age) Order QI 02 prov assay off Constant Observations Description dummies Place dummies Year dummies Quarter dummies Adj. R-squared See Table 1 for variable ove v eve 7 eve eve eve NA Table 3 Identifying arbitrage opportunities Arbitrage category P.Draper et al/Economics Letters 162 (2018) 45-48 Price Scrap Price Scrap Price & buyer's premium Scrap No lots sold 9924 690 56 0.457** -0.248- 0.411*** 0.129** 0.240 0.056*** definitions.p<0.01,"p<0.05,"p<0.1. 0.602*** 0.050*** 05 M -0.071 1.850 10,614 (11) (3) (5) (3) 0.547 regression: In(Price),= d+a, In(Scrap),+Characteristics, Std. err (2) (0.008) (0.046) (0.018) (0.033) (0.029) (0.016) (0.061) (0.014) (0.015) (0.115) Total weight (oz.) 245,311 29,877 1.608 + Controls + e, (1) Where In Price), is the natural log of the auction lot's hammer price. and In(Scrap), is our main variable of interest, the natural log of the lot's scrap value, for which we expect a significant positive coefficient if buyers' consider the scrap value of a lot an important feature when bidding Characteristics, include characteristics found in the auction catalogues that may explain the auction price. Specifically, we control for uncertainty as the spread between the high and low pre-sale estimate, age as the natural log of the lot age, the lot position in the auction, two binary quality variables to indicate if the lot was made by a notable silversmith working in the twentieth century (Q1) or earlier (Q2), and three binary variables to indicate provenance, the presence of an armorial or other inscription, and whether the assay office was other than London. Finally, we include binary variables (Controls) to control for the lot description (e.g. flatware), place of sale, year of sale, and quarter within the year in which the lot was sold. 4. Empirical analysis and results Table 2 reports the hedonic regression results of Eq. (1) for the full sample, lots sold by Bonhams, and lots sold by Woolley and Wallis. In all three regressions, we use unreported dummy variables to control for the lot description, place of sale, year of sale and quarter within the year in which the lot was sold. We report robust standard errors using the White heteroskedasticity-robust procedure. For all three regressions, the scrap value of silver has a 2 The lot's scrap value is calculated as the product of the lot's weight in ounces and the corresponding non-trade scrap price of silver on the auction day from Cookson Precious Metals Ltd at http://www.cooksongold.com/. This represents the price at which they are prepared to trade on that day. Over our sample period the non-trade scrap price of silver per ounce ranges from £5.26 on 15 January 2009 to (21.38 on 28 April 2011, has a mean (standard deviation) of £11.03 (C353), and an average (standard deviation) daily change of 0.002 (0.295) 0.058*** 0.268** 0.047** 0.018 2.255*** 5.179 (11) (2) (5) (3) 0.638 Total price (K) 8.976,321 371,875 14,490 Std. err (4) (0.010) (0.055) (0.022) (0.043) (0034) (0.020) (0.092) (0018) (0.019) (0.134) Woolley and Wallis Coeff (5) 0.399 -0.061 0512** 0.421*** 0.240** 0.052 0772 0.061*** -0.104 LIO 5.435 (11) NA (5) (3) Total price-bp (0) 10,900,000 454229 17,679 47 Std. err. (6) (0.011) (0.074) (0.029) (0.052) (0.046) (0.025) (0.074) (0.021) (0.021) (0.180) Total scrap ( 2,734,583 398,286 20,792 significant positive effect on the hammer price and the remaining variables, where significant, are all of the predicted sign. In an auction environment, this implies that bidders are partly setting their bids based on the lot's scrap value. The evidence does not, however, confirm whether profitable arbitrage opportunities exist between the silver auctions and scrap value of the lots sold and purchased. If arbitrage opportunities exist, then there is prima facie ev- idence of market inefficiency. While this is a weak-form test of market efficiency, since it only uses price information, the absence of arbitrage is a very important characteristic of financial markets, which we test for in the physical silver market. Purchasers of silver goods are faced with a variety of costs, notably the buyer's premium. Therefore, we expect profitable arbitrage may be pos- sible before allowing for the buyer's premium but is most likely impossible once it is added into the price of the good. To test this, we identify and report summary statistics in Table 3 of all lots sold above and below their scrap value based on the hammer price paid to the seller, and all lots sold below the scrap value based on the hammer price and buyer's premium paid by the bidder. Our findings in Table 3 show that arbitrage opportunities are available if the only cost imposed on buyers is the hammer price. However, once the buyer's premium is included, arbitrage oppor- tunities almost completely disappear. Specifically, less than one percent of our sample would yield a profit of, on average, less than £60 per transaction, ignoring any physical costs of collection and resale. 5. Conclusions The absence of arbitrage opportunities after costs, particularly the buyer's premium, suggests that bidders are sensitive to the total price. Moreover, although scrap silver can be sold for cash at 3 The buyer's premium is a fee set by the auction house, and paid to the auction- eer by the buyer. This is constant for each auction, but varies between houses and over the sample period from 19.5-25% of the hammer price. 4 Analysis of the 56 lots for which arbitrage is possible indicates that 91% were sold by Bonhams and more than half in the fourth quarter. 46 Table 1 Descriptive statistics and sample distribution Panel A: Pooled sample statistics Variable Price Price bp Scrap Weight Spread Age Order Q1 02 prov arm assay off 2009 2010 2011 2012 2013 Mean 880.74 1073.17 295.16 25.93 0.39 166.06 2014 Quarter 1st 2nd 3rd 0.67 0.08 0.19 0.02 0.40 0.62 Std.dev. 1906.84 231157 441.80 37.03 0.13 Pape 77.75 0.25 0.26 0.39 0.15 0.49 0.48 Knightsbridge Obs. Price 551 667 Scrap 708.85 220.07 696.21 278.90 899.24 538.90 539 1089.38 645.23 447 1062.86 41761 332 1386.42 485.82 671 ART P. Draper et al/Economics Letters 162 (2018) 45-48 892.68 391.53 1134 928.36 424.30 393.99 895.67 959.65 452.56 424 1160 Min 20.00 2440 0.89 0.10 0.05 1.00 0.00 Panel B: Sample distribution of lots sold, average lot hammer price (C), and average lot scrap value (E) Year Bonhams Max. 85,00000 101,5790 6.211.80 553.00 1.00 $10.00 1.00 1.00 1.00 1.00 1.00 1.00 Knowle Obs. Price Scrap 402 382.44 179.87 298.34 476 458.25 472 596.60 459.47 291 465.10 275.94 394 488.48 324.64 296 487.53 306.71 369 358.25 49145 the bullion dealer at the prices quoted and with speedy or even immediate payment. The possibility, therefore, of selling for scrap provides a minimum guaranteed price, allowing sellers to realise cash with a minimum of restrictions. Both sales for scrap, and to antique dealers provide immediacy to the seller. They also provide certainty and low transaction costs. They do not however provide exposure to a wide range of buyers. The significant transactions costs associated with selling by auction suggests that sellers of silver goods that are of low quality and plentiful supply should sell direct to bullion or antique dealers to maximise their receipts from sale. Despite this, low quality silver is sold at auction. Selling for scrap removes any chance of receiving a premium for artistic merit or scarcity and there may be some sellers of low quality items who prefer, perhaps through ignorance. to sell by auction. High quality items should not be scrapped but sold to dealers or by auction. Many silver goods have some worth relating to their scarcity. usefulness, artistic merit and quality of workmanship. A number of items, however, have no such merit. They may have been mass produced and of poor quality originally, or may be battered and broken. The demand for such items is slight. They are of limited interest to collectors or dealers. They do however retain value, a value that depends on their silver content. They may be easily melted and reduced to pure silver. In an efficient market, we would expect any silver item to have a minimum value directly related to its scrap value. Description Hammer price of lot (C) Hammer price of lot including the buyer's premium (E) Scrap value of lot(C) Weight of lot (ounces) 2. Sample and data To examine the role that the scrap value plays in determining the price of silver items sold at auction, along with any possible opportunities for riskless arbitrage, we construct a unique database from the catalogues of 88 silver auctions from two major En- glish auction houses, Bonhams, and Woolley and Wallis, between Auctioneer's spread (High estimate-Low estimate)Low estimate) Age of lot (years) Order of lot in the auction Dummy (1 if notable 20th century maker: 0 otherwise) Dummy (= 1 if notable pre-20th century makers: O otherwise) Dummy (1if provenance: 0 otherwise) Dummy (1 if armorial; 0 otherwise) Dummy (1if London assay office: 0 otherwise) New Bond Street Obs. Price Scrap 148 3245.54 281.95 377.16 132 361477 146 3987.33 741.78 61 3179.48 67479 407473 798.47 5035.71 685.61 93 274 3748.17 635.91 72 2750.69 285.49 276 398184 541.78 Woolley and Wallis Salisbury Obs Price 567 828 478.66 920.42 986 684.69 1018 580.38 1080 559.84 956 506.69 1180.00 549.56 1376.00 691.92 1415.00 60724 1464.00 634.33 Scrap 81.93 192.16 288.25 211.68 162.78 116.80 170.31 186.19 186.37 185.68 January 2009 and December 2014. Although no statistics are available for the proportion of antique silver sold by dealers in the UK, informed sources have suggested that, while dealers are responsible for about half of annual sales, auction houses have become more dominant in the market. Bonhams is the third major international UK auction house and the largest seller of UK antique silver by volume over our sample period, and Woolley and Wallis is the UK's largest regional auction house and the second largest seller of UK antique silver by volume. Since it is common for auctioneers to exclude some of the available pre-sale information from the online data (Campos and Barbosa, 2009), copies of the physical catalogues are used. Whilst attempting to be as comprehensive as possible we exclude unsold lots, mixed collections (since it is impossible to assign detailed information to the individual pieces), and lots without a pre- sale estimate. Our final sample contains information on 10,614 sold lots spread across three Bonhams locations, Knightsbridge (30%), Knowle (13%), New Bond Street (6%), and Woolley and Wallis house in Salisbury (51%). In terms of the timing of the silver auctions, Woolley and Wallis hold auctions quarterly, while Bonhams have departed from a fixed schedule in recent years, and closed their knowle salesroom in 2011. Table 1 reports the main descriptive statistics and sample distribution of the dataset described above. 3. A hedonic price model for silver goods To examine the relation between the hammer price and the scrap value of the auction lot, we use the following hedonic pricing 1 Unlike other studies, we do not concentrate on the international auction houses, Christies and Sotheby's, since both rarely hold specialist silver auctions in London over the sample period.
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
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Transcribed Image Text:Table 2
Hedonic regression of natural log of hammer price on the natural log of scrap value and other characteristics.
Variable
Predicted sign
Full sample
Coeff.
(1)
Bonhams
Coeff.
(3)
0.485***
-0.239***
0.317***
100
0.187***
0.058***
0.268***
0.047***
In(scrap)
Spread
In (age)
Order
Q1
02
prov
arm
assay off
Constant
Observations
Description dummies
ove
ove
2
ove
*ve
eve
ve
-ve
ΝΑ
Table 3
Identifying arbitrage opportunities.
Arbitrage category
Price > Scrap
Price < Scrap
Price & buyer's premium Scrap
P.Draper et al. /Economics Letters 162 (2018) 45-48
No. lots sold
9924
690
56
0.457***
-0.248***
0.411***
0.129***
0.240***
0.056***
0.602***
0.050***
Place dummies
Year dummies
Quarter dummies
Adj. R-squared
See Table 1 for variable definitions. *** p<0.01, ** p < 0.05,"p<0.1.
-0.071***
1.850***
10,614
(11)
(3)
(5)
(3)
0.547
Std.err.
(2)
(0.008)
(0.046)
(0.018)
(0.033)
(0.029)
(0.016)
(0.061)
(0.014)
(0.015)
(0.115)
Total weight (oz.)
245,311
29,877
1,608
regression:
In(Price), = do + a,In(Scrap); +[Characteristics,
+ Σ Controls + e,
(1)
Where In(Price), is the natural log of the auction lot's hammer price,
and In(Scrap), is our main variable of interest, the natural log of
the
the lot's scrap value, for which we expect a significant positive
coefficient if buyers' consider the scrap value of a lot an important
feature when bidding Characteristics, include characteristics
found in the auction catalogues that may explain the auction price.
Specifically, we control for uncertainty as the spread between the
high and low pre-sale estimate, age as the natural log of the lot
age, the lot position in the auction, two binary quality variables
to indicate if the lot was made by a notable silversmith working
in the twentieth century (Q1) or earlier (Q2), and three binary
variables to indicate provenance, the presence of an armorial or
other inscription, and whether the assay office was other than
London. Finally, we include binary variables ( Controls) to control
for the lot description (e.g. flatware), place of sale, year of sale, and
quarter within the year in which the lot was sold.
4. Empirical analysis and results
Table 2 reports the hedonic regression results of Eq. (1) for
the full sample, lots sold by Bonhams, and lots sold by Woolley
and Wallis. In all three regressions, we use unreported dummy
variables to control for the lot description, place of sale, year of sale
and quarter within the year in which the lot was sold. We report
robust standard errors using the White heteroskedasticity-robust
procedure. For all three regressions, the scrap value of silver has a
2 The lot's scrap value is calculated as the product of the lot's weight in ounces
and the corresponding non-trade scrap price of silver on the auction day from
Cookson Precious Metals Ltd at http://www.cooksongold.com/. This represents the
price at which they are prepared to trade on that day. Over our sample period the
non-trade scrap price of silver per ounce ranges from £5.26 on 15 January 2009 to
£21.38 on 28 April 2011, has a mean (standard deviation) of £11.03 (£3.53), and an
average (standard deviation) daily change of £0.002 (€0.295).
0.018
2.255***
5,179
(11)
(2)
(5)
(3)
0.638
Total price (E)
8,976,321
371,875
14,490
Std.err.
(4)
(0.010)
(0.055)
(0.022)
(0.043)
(0.034)
(0.020)
(0.092)
(0.018)
(0.019)
(0.134)
Woolley and Wallis
Coeff.
(5)
0.399***
-0.061
0512***
0.421***
0.240***
0.052"
0.772***
0.061***
-0.104***
1.147***
5.435
(11)
NA
(5)
(3)
0.389
Total price+bp (E)
10,900,000
454,229
17,679
47
Std.err.
(6)
(0.011)
(0.074)
(0.029)
(0.052)
(0.046)
(0.025)
(0.074)
(0.021)
(0.021)
(0.180)
Total scrap (E)
2,734,583
398,286
20,792
significant positive effect on the hammer price and the remaining
variables, where significant, are all of the predicted sign. In an
auction environment, this implies that bidders are partly setting
their bids based on the lot's scrap value. The evidence does not,
however, confirm whether profitable arbitrage opportunities exist
between the silver auctions and scrap value of the lots sold and
purchased.
If arbitrage opportunities exist, then there is prima facie ev-
idence of market inefficiency. While this is a weak-form test of
market efficiency, since it only uses price information, the absence
of arbitrage is a very important characteristic of financial markets,
which we test for in the physical silver market. Purchasers of
silver goods are faced with a variety of costs, notably the buyer's
premium. Therefore, we expect profitable arbitrage may be pos-
sible before allowing for the buyer's premium but is most likely
impossible once it is added into the price of the good. To test this,
we identify and report summary statistics in Table 3 of all lots sold
above and below their scrap value based on the hammer price paid
to the seller, and all lots sold below the scrap value based on the
hammer price and buyer's premium paid by the bidder.
Our findings in Table 3 show that arbitrage opportunities are
available if the only cost imposed on buyers is the hammer price.
However, once the buyer's premium is included, arbitrage oppor-
tunities almost completely disappear. Specifically, less than one
percent of our sample would yield a profit of, on average, less than
£60 per transaction, ignoring any physical costs of collection and
resale.
5. Conclusions
The absence of arbitrage opportunities after costs, particularly
the buyer's premium, suggests that bidders are sensitive to the
total price. Moreover, although scrap silver can be sold for cash at
3 The buyer's premium is a fee set by the auction house, and paid to the auction-
the
over the sample period stant for each auction, but varies between houses and
from 19.5-25% of the hammer price.
4 Analysis of the 56 lots for which arbitrage is possible indicates that 91% were
sold by Bonhams and more than half in the fourth quarter.
46
Table 1
Descriptive statistics and sample distribution
Panel A: Pooled sample statistics
Variable
Price
Price+bp
Scrap
Weight
Spread
Age
Order
Q1
Q2
prov
arm
assay off
2009
2010
Mean
880.74
1073.17
295.16
25.93
166.06
0.67
2011
2012
2013
2014
Quarter
1st
2nd
3rd
4th
0.39
0.08
0.19
603
0.02
0.40
0.62
Std. dev.
1906.84
2311.57
441.80
37.03
0.13
77.75
0.25
0.26
0.39
0.15
0.49
0.48
Price
Scrap
708.85 220.07
698.21 278.90
899.24 538.90
645.23
417.61
485.82
P. Draper et al./Economics Letters 162 (2018) 45-48
489
1134
892.68 391.53
928.36 424.30
424 895.67 393.99
959.65 452.56
1160
Min.
20.00
24.40
0.89
0.10
0.05
1.00
0.00
-
-
Panel B: Sample distribution of lots sold, average lot hammer price (E), and average lot scrap value (E)
Year
Bonhams
Knightsbridge
Obs.
551
667
671
539 1089.38
447
1062.86
1386.42
Knowle
Obs. Price
402 382.44
476
458.25
472 596.60
Max.
85,00000
101,5780
6,211.80
553.00
291
465.10
394 488.48
296 487.53
369 491.45
1.00
510.00
1.00
1.00
1.00
1.00
Scrap
179.87
298.34
459.47
-
1.00
1.00
275.94
324.64
306.71
358.25
the bullion dealer at the prices quoted and with speedy or even
immediate payment. The possibility, therefore, of selling for scrap
provides a minimum guaranteed price, allowing sellers to realise
cash with a minimum of restrictions.
Both sales for scrap, and to antique dealers provide immediacy
to the seller. They also provide certainty and low transaction costs.
They do not however provide exposure to a wide range of buyers.
The significant transactions costs associated with selling by auction
suggests that sellers of silver goods that are of low quality and
plentiful supply should sell direct to bullion or antique dealers to
maximise their receipts from sale. Despite this, low quality silver
is sold at auction. Selling for scrap removes any chance of receiving
a premium for artistic merit or scarcity and there may be some
sellers of low quality items who prefer, perhaps through ignorance,
to sell by auction. High quality items should not be scrapped but
sold to dealers or by auction.
Many silver goods have some worth relating to their scarcity.
usefulness, artistic merit and quality of workmanship. A number
of items, however, have no such merit. They may have been mass
produced and of poor quality originally, or may be battered and
broken. The demand for such items is slight. They are of limited
interest to collectors or dealers. They do however retain value,
a value that depends on their silver content. They may be easily
melted and reduced to pure silver. In an efficient market, we would
expect any silver item to have a minimum value directly related to
its scrap value.
2. Sample and data
To examine the role that the scrap value plays in determining
the price of silver items sold at auction, along with any possible
opportunities for riskless arbitrage, we construct a unique database
from the catalogues of 88 silver auctions from two major En-
glish auction houses, Bonhams, and Woolley and Wallis, between
Description
Hammer price of lot (E)
Hammer price of lot including the buyer's premium (E)
Scrap value of lot (E)
Weight of lot (ounces)
Auctioneer's spread (High estimate-Low estimate) Low estimate)
Age of lot(years)
Order of lot in the auction
Dummy (= 1 if notable 20th century maker: 0 otherwise)
Dummy (= 1 if notable pre-20th century makers: 0 otherwise)
Dummy (= 1 if provenance: 0 otherwise)
Dummy (= 1 if armorial: 0 otherwise)
Dummy (= 1 if London assay office: 0 otherwise)
New Bond Street
Obs. Price
Scrap
283.95
148 3245.54
133
132 3614.77 377.16
146 3987.33 741.78
61 3179.48 674.79
93 4074.73 798.47
42 5035.71 685.61
274 3748.17 635.91
72 2750.69 285.49
276 3981,84 541.78
Woolley and Wallis
Salisbury
Obs.
Price
567 478.66
828 920.42
986 684.69
580.38
1018
1080 559.84
956 506.69
Scrap
81.93
192.16
288.25
211.68
162.78
116.80
1180.00 549.56 170.31
1376.00 691.92 186.19
1415.00 607.24 186.37
1464.00
185.68
634.33
January 2009 and December 2014. Although no statistics are
available for the proportion of antique silver sold by dealers in
the UK, informed sources have suggested that, while dealers are
responsible for about half of annual sales, auction houses have
become more dominant in the market. Bonhams is the third major
international UK auction house and the largest seller of UK antique
silver by volume over our sample period, and Woolley and Wallis
is the UK's largest regional auction house and the second largest
seller of UK antique silver by volume.
Since it is common for auctioneers to exclude some of the
available pre-sale information from the online data (Campos and
Barbosa, 2009), copies of the physical catalogues are used. Whilst
attempting to be as comprehensive as possible we exclude unsold
lots, mixed collections (since it is impossible to assign detailed
information to the individual pieces), and lots without a pre-
sale estimate. Our final sample contains information on 10,614
sold lots spread across three Bonhams locations, Knightsbridge
(30%), Knowle (13%), New Bond Street (6%), and Woolley and
Wallis' house in Salisbury (51%). In terms of the timing of the
silver auctions, Woolley and Wallis hold auctions quarterly, while
Bonhams have departed from a fixed schedule in recent years,
and closed their Knowle salesroom in 2011. Table 1 reports the
main descriptive statistics and sample distribution of the dataset
described above.
3. A hedonic price model for silver goods
To examine the relation between the hammer price and the
scrap value of the auction lot, we use the following hedonic pricing
1 Unlike other studies, we do not concentrate on the international auction
houses, Christies and Sotheby's, since both rarely hold specialist silver auctions in
London over the sample period.
Transcribed Image Text:ELSEVIER
HIGHLIGHTS
Auctions, market efficiency, and the trade in second-hand and antique
silver
ARTICLE INFO
Article history:
Received 7 June 2017
Received in revised form 28 September
2017
Paul Draper, Alan Duboisée de Ricquebourg*, Iain Clacher
University of Leeds, United Kingdom
Accepted 26 October 2017
Available online 9 November 2017
JEL classification:
G14
• Scrap value of silver plays a significant role in determining realised auction prices.
Riskless arbitrage opportunities are limited via selling items for their scrap value.
• Silver auctions exhibit a high level of market efficiency.
Z1
D44
Economics Letters 162 (2018) 45-48
Keywords:
Art market
Market efficiency
Auction
Silver
Contents lists available at ScienceDirect
Economics Letters
journal homepage: www.elsevier.com/locate/ecolet
https://doi.org/10.1016/j.econlet.2017.10.022
0165-1765/2017 Elsevier B.V. All rights reserved.
E-mail address: a.j.duboiseedericquebourg@leeds.ac.uk
(A. Duboisée de Ricquebourg).
1. Introduction
Ashenfelter and Graddy (2003) cite evidence of long periods
of divergence in prices between auction houses and geographical
locations from studies of wine, prints, and paintings (e.g. Ashen-
felter, 1989; Pesando, 1993; Mei and Moses, 2002). Despite this
interest in pricing anomalies, few studies have addressed in depth
the issue of market efficiency and opportunities for arbitrage in
the market for collectibles. In many areas, such as paintings, the
conditions for riskless arbitrage are impossible to fulfil. Even for
wine and prints, whilst it is possible to identify opportunities for
profitable arbitrage over time, by purchasing and selling identical
bottles or prints in different markets, the risks and uncertainties
involved make riskless arbitrage almost impossible.
* Correspondence to: University of Leeds, Leeds University Business School,
Leeds, LS2 9JT, United Kingdom.
ABSTRACT
Using a new, large, unique database relating to silver goods sold at two major UK auction houses we show
that the scrap value of silver plays a significant role in determining the realised prices of items sold at silver
auctions. However, although scrap silver can be sold for cash at guaranteed prices almost immediately.
arbitrage opportunities are extremely limited. Just over 6% of silver goods sell below scrap value but once
the buyer's premium is allowed for the opportunity for profit is tiny.
© 2017 Elsevier B.V. All rights reserved.
economics
letters
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Motivated by an observed apparent discrepancy between the
value of the silver in some goods and their auction prices we
investigate systematically the opportunities for riskless arbitrage
in the market for secondhand and antique silver. Studies of English
silver goods (or indeed any other type of silver good) are limited.
Bauwens and Ginsburgh (2000) use a sample based on sales of
English teapots, but their primary focus is on the accuracy the
pre-sale estimate. Our focus is rather different, as our concern is
with market efficiency, and specifically the possibility of riskless
arbitrage from buying silver items with little artistic merit at
auction, and then selling these for scrap within the same day at
a guaranteed price.
teed price
A thriving market exists in silver scrap. Recycling scrap silver
is straightforward. English silver has a long established history
of hallmarking (almost) guaranteeing a fixed proportion of silver
and copper. The application of heat and flux enables the silver to
be easily separated from the copper. Prices paid for hall-marked
scrap silver by bullion dealers are available online and in specialist
publications. Prices are firm and it is entirely possible to sell to
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