Liv-ex Reports

Talking Trade 16th of January: Burgundy leads the market as UK trade hosts 2024 vintage tastings

Burgundy claimed 37.1% of traded value. Bordeaux followed in second place with 30.1%.

• Market Intelligence

• Burgundy claimed 37.1% of traded value. Bordeaux followed in second place with 30.1%.
• For the second week running, Haut Brion 1989 was the top-traded wine of the week by value. Roulot’s 2023 Meursault and DRC, La Tache 2022 came in second and third.
• UK buyers pulled ahead, claiming 43% of traded value.

With tastings of the 2024 vintage taking place in London this week, Burgundy led the market, accounting for 37.1% of traded value, over 70% of which was purchased by UK buyers. The 2022s were the top-traded by value, though the 2023s traded most frequently.

Bordeaux followed in second place. With the 1989 vintage again in the top spot, Haut-Brion was the region’s top-traded wine overall. Petrus followed, a single bottle of the 1966 changing hands alongside several recent vintages.

Champagne’s share fell from 12.2% to 8.9%. Higher value, rarer wines led the region, with Salon and Dom Perignon P2 amongst the top traded wines by value.

The Rhone had a decent week, holding a 5.1% share. Jean Louis Chave was the region’s top-traded producer by value, with the Hermitage Rouge and Blanc and Ermitage Cathelin all trading.

Italy pulled back this week, both Tuscany and Piedmont seeing their shares decline. Giacomo Conterno and Biondi-Santi were the region’s top traded producers, rising ahead of the Super Tuscans who typically claim the top spots.

Copyright © 2025 Liv-ex Ltd. All rights reserved.

Liv-ex Reports

Bid-to-Offer Ratio as a Leading Indicator

This analysis shows that the bid-to-offer (BO) ratio is a leading indicator of market returns, typically signalling turns 2–5 months in advance. When the value…

Abstract:

This analysis shows that the bid-to-offer (BO) ratio is a leading indicator of market returns, typically signalling turns 2–5 months in advance. When the value of bids (a proxy of demand) rises relative to value of offers (a proxy of supply), price increases often follow.

Using an ordinary least squares (OLS) linear regression, we find that the current BO ratio of a basket of wines predicts future price movements with high statistical significance. The ratio can be calculated by value (£GBP) or volume (Litres) and applied to all wines or a specific subset (e.g. Liv-ex index components). Price movements are most effectively measured using Liv-ex indices.

Results vary by subset, but the broadest measures – the Liv-ex Fine Wine 1000 (LX1000) and its respective BO ratio – provide the most widely applicable results. The model indicates the strongest association between the BO ratio and 3-month LX1000 returns. It predicts price stability over 3 months at a BO ratio of 0.96, a 1.6% rise when the ratio is 1.5, and a -1.0% fall at a ratio of 0.66 (the ratio at the time of writing). These outlooks reflect overall market trends, but individual wines can diverge significantly with there being winners and losers within each cohort. Average market returns will cluster around these outlooks, however.

In the model, the LX1000’s BO ratio explains 59% of the variation in LX1000 returns, leaving 41% attributable to other factors. This makes it a valuable but not exhaustive predictor of fine wine prices.

Introduction:

The fine wine market has been in decline since October 2022, with the LX1000 falling by 28.7% over this period. Liv-ex indices have recently found some stability, however, with the LX100 up a modest 1.3% on September 2025. Recovery is not expected to be uniform across the market, and finding reliable green shoots will be crucial as buyers return. In this context, the BO ratio serves as a crucial metric for pinpointing where renewed optimism may be most justified.

The BO ratio is an effective measure of market sentiment, summarising the supply and demand balance for both the overall market and subsets of wines. Should demand outweigh supply, it follows that there will be inflationary pressure on prices.

The BO ratio can be calculated by value or by volume. The assumption is that volume provides a cleaner measure of market sentiment, without price movements having a confounding effect on both the ratio and index movements. Further, it is possible to calculate the BO ratio for subsets of wines, allowing us to establish the exact supply and demand balance of different indices’ wines.

This analysis aims to establish:

• Does the BO ratio have a predictive effect on price performance?
• If so, at what timeframe (months following) does the BO ratio most reliably predict index returns?
• Is the BO ratio a more reliable indicator for some subsets of wines than others?
• At what BO ratio is price stability expected?

Using a statistical approach we aim to quantify this relationship, allowing more informed use of this datapoint.

Method:

The two variables used in this analysis were the BO ratio (a proxy of supply & demand balance) and the index levels of the Liv-ex indices (a measurement of fine wine prices). An ordinary least squares linear regression is a statistical method used to estimate the relationship between an independent variable (BO ratio) and a dependent variable (fine wine prices). It finds the best-fitting straight line between these variables by minimising the total squared errors (difference between the observed data and the modelled relationship). Using this approach, we can assess whether there is a relationship between the variables, and should one exist, the strength of the association.

This study used Liv-ex datasets covering 2015 to present. BO ratios of all Liv-ex major indices, the Liv-ex 1000 sub-indices, and the overall Liv-ex market were analysed. These are:

(i) The overall BO ratio for all wines on the Liv-ex exchange

(ii) The BO ratio for the components of the LX1000

(iii) The BO ratio for the components of the Liv-ex Fine Wine 100 (LX100)

(iv) The BO ratio for the components of the Liv-ex Fine Wine 50 (LX50)

(v) The BO ratio for components within the Liv-ex 1000’s sub-indices:

a. Bordeaux 500 (BDX500)

b. Burgundy 150 (Bgdy150)

c. Champagne 50 (Cham50)

d. Rhone 100 (Rho100)

e. Italy 100 (Ital100)

f. Rest of the World 60 (ROW60)

The BO ratio is defined as the total value or volume of bids divided by the total value or volume of offers.

Price performance was measured using Liv-ex indices. Each wine contributed monthly price observations and corresponding BO ratios, resulting in approximately 120 months of data for between 50 and 1,000 wines, depending on the index used to measure price performance.

The analysis tested the predictive relationship between the BO ratio and future returns. Price movements at horizons of 0–12 months were calculated from monthly index levels. Linear regressions were run for each horizon, with current BO ratio as the explanatory variable and forward returns as the dependent variable. For example, in the case of the 3-month horizon, what is the relationship between the current BO ratio and the change in the index in 3 months from the date the BO ratio was recorded. For each model values for the following were recorded:

• Coefficients: the effect a 1-unit change in BO ratio has on % returns of the index
• P-values/t-ratios: measurements of statistical significance, i.e. how likely is it that BO ratio has a non-zero effect on index performance.
• R2: the proportion of the index performance’s variance which can be explained by the BO ratio.
• Confidence Intervals: the range in which we are 80% confident that the true value lies.

Using these metrics, we can select the horizon we are most confident in a relationship existing for each model, providing us with a single most reliable relationship between the BO ratio by either value or volume and index performance. A scatterplot of our selected BO ratio versus forward returns is then produced with a fitted regression line to visualise the relationship.

Limitations:

Due to our usage of timeseries data, there are limitations to our methods in the form of autocorrelation and heteroskedasticity, both of which violate the necessary assumptions required for linear regressions.

Autocorrelation, the correlation between datapoints in close proximity to each other, is present throughout due to the overlapping nature of index returns. With index returns being calculated in 1-to-12 month windows, the 3-month forward returns in one month will share 2 months of index performance with the next month’s 3-month forward returns.

Heteroskedasticity, non-uniform standard errors across all levels of the independent variable (BO ratio), is present within our dataset, with periods of high market sentiment and BO ratios displaying higher volatility than those without. For the LX100 model, when the BO ratio is below 1 the typical error (difference between actual and predicted returns) is small—about 0.5%. For observations with higher BO ratios, the typical error is slightly larger—about 1.9%. Essentially, periods of low demand compared to supply almost always lead to weak index performance, and as a result, the model’s predictions closely match the actual outcomes. In contrast, when demand significantly exceeds supply, the index tends to experience more volatility, with a wider range of realised returns and less alignment with the model’s predictions.

To account for these limitations and improve the robustness of our confidence intervals, heteroskedasticity and autocorrelation consistent (HAC) standard errors were applied using the Newey–West estimator. This adjustment ensures that confidence intervals and significance tests remain valid even when residuals exhibit non-constant variance or serial correlation, which is common in time-series financial data.

Results:

The results provide us with two keys insights. First, at what timeframe does the BO ratio most effectively predict the movements of Liv-ex indices. Second, what is the relationship between the ratio and the index performance over these varying horizons. We can then assess where the model is most significant and whether the estimated confidence intervals were narrow enough to provide useful information on the relationship between these variables.

Looking initially at our broadest market measures, the LX1000 BO ratio by value and its effect on the LX1000, we see that the most statistically significant relationship exists at the 3-month horizon (Table 1). At this horizon, the model has the lowest p-value and highest t-ratio, indicating the significance of the relationship between BO ratio and the performance of the LX1000. Additionally, it has the highest R2 (59%), indicating 59% of the variation in percentage change in the LX1000 over 3 months can be explained by the current BO ratio. Given the many confounding factors that affect the price of fine wine, 59% is a considerable proportion, making the BO ratio in this case a valuable indicator.

Charting this relationship (Figure 1) allows easier interpretation of the results. By choosing a current overall BO ratio on the x-axis and finding where this intersects the line of best fit, we identify the expected % change in the LX1000 in 3 months’ time. This gives us valuable information on the following:

a) A BO ratio of 0.96 is where we would expect the LX1000 to remain flat over the next 3 months.

b) At a BO ratio of 1.5 we would expect 1.6% increase in the LX1000 over the next 3 months

c) At a BO ratio of 0.66, the ratio at the time of writing, a 1.0% decline is expected over the next 3 months.

For each index of interest these results were replicated for all time horizons and both the BO ratio by value and volume. By selecting the time horizon and BO ratio with the highest R2 and t-ratio, we have a set of models that most accurately show the relationship between the BO ratio and index performance.

The results (Table 2) allow an assessment of which indices’ BO ratios are valuable indicators of future prices. Across all models, the t-ratios and p-values are robust, strongly suggesting that the coefficient—which measures the relationship between the BO ratio and movements in the index—is unlikely to be zero. A positive, non-zero coefficient (the slope of the best-fit line) means that each unit increase in the BO ratio is associated with a corresponding rise in index returns over the specified period. In the case of the overall BO ratio and LX1000 performance, a t-ratio of 13 indicates that the 5.2% coefficient is 13 standard deviations above 0, a convincing margin. Even in the case of the Rhone 100, the model with the least convincing t-ratio, its t-ratio of 4.8 surpasses the threshold of 2 which is understood to represent statistically significant when working with financial data. Across all indices, we can say with a high degree of certainty that a higher BO ratio indicates inflationary pressures on prices over the 2-to-5 month period.

Although we are confident in a relationship existing, the R² is crucial in assessing whether the relationship is meaningful when it comes to making predictions. The coefficient of determination (R²) measures how much of the variation in prices (the dependent variable) can be explained by changes in the BO ratio (the independent variable), and this value varies across models. Notably, the models for the LX1000, Bgdy150, Cham50, and BDX500 all achieve R² values above 45%. In the fine wine market—where prices are influenced by many external factors—being able to attribute more than 45% of price movements to a single variable highlights the BO ratio’s strength as a predictive indicator.

Conversely, the models for the LX50, Rho100, Ital100, and ROW60 have smaller magnitude in the coefficient and lower R2, with the BO ratio explaining substantially less variation in the index’s returns. Although the p-value suggests a significant relationship existing for these models, the impact of change in the index due to a change in BO ratio was smaller, with the index’s returns subject to more noise and less dependent on the supply-demand balance alone. The distinction between a model with a high and low R2 is clear when plotted (Figure 2 and 3).

The distinction lies in how well the data points fit to the regression line. The datapoints in the LX1000 model cluster around the line of best fit, whereas in the plot for the LX50, many data points sit far from this line. Both models show a statistically significant slope, with a clear positive relationship. Based on the coefficient, however, only the LX1000 model provides enough precision to make predictions meaningful.

Discussion:

Our results demonstrate a significant association between current BO ratio and index returns over the 2-to-5 month horizon. When demand outpaces supply, inflationary pressures often follow.

The equations derived for each index allow us to make a forecast for each index given how this index has historically performed at that BO ratio level. The predictive power of these models varies by index, with confidence intervals and variance from the regression lines more reliable for the LX1000, LX100, Bgdy150, Cham50, and BDX500. Given the models generated for these indices, we can estimate:

Despite the likelihood of a strong association, and the relative tightness of our data to the line of best fit, it is important to note that even the strongest model (LX1000) has the BO ratio explaining only 59% of the index performance’s variance. While substantial, it is not an exhaustive indicator of price, and thus confidence intervals show the BO ratio at which we’d expect to see price stability sit in the range of 0.67 to 1.34, with a sizeable degree of price movement still subject to other factors.

Indices with higher variance, such as LX50, offer less predictive power but still provide context. The LX50, which tracks the 10 most recent physical vintages of first growth châteaux, shows that while supply-demand dynamics matter, a larger proportion of price variance is attributable to external market factors. For well understood wines such as the labels of the first growths, with a high degree of liquidity on the secondary market, this is plausible. Burgundy, with a lesser degree of price transparency and liquidity in the secondary market, it follows that supply and demand play a bigger part in price dynamics.

In July of this year BO ratios across the Liv-ex indices began to rise, with the LX100 for example climbing from an all-time low of 0.35 to 0.92 today, its highest level since late 2022. In September, two months after these ratios began to climb, we saw the LX100 rise by its largest margin in three years and continue to rise each month since. This pattern was reflected across the majority of Liv-ex indices, just as BO ratios flagged the turn of the market 3 months prior to the market peak in October 2022. Statistically and anecdotally, BO ratios remain a valuable indicator of market sentiment and, in some cases, a forecasting tool. With optimism that the market may be finding its floor, indices with BO ratios above historical stability thresholds are likely to lead any recovery. Notably, the Bgdy150 and LX100 have risen consistently in recent months, supported by the highest level of demand in over two years – potential green shoots in an enduring market.

Copyright © 2025 Liv-ex Ltd. All rights reserved

Liv-ex Reports

Talking Trade 9th January: Haut-Brion 1989 claims 1st place; US share of buying climbs to 30%

Bordeaux led the market this week, while Burgundy claimed the top spot over the festive period.

• Market Intelligence

• Bordeaux led the market this week, while Burgundy claimed the top spot over the festive period.
• Haut-Brion 1989, Unico 2015 and Petrus 2021 were the top-traded wines of the week by value.
• US buying has held steady at 30% over the past three weeks, though trade levels (as expected) have dipped overall.

Bordeaux led the market this week with a 29.8% share of traded value. The 2019 and 2021 vintages were the top-traded by value.

Burgundy followed with an 18.4% share, down on its strong performance over the Christmas period. Clos de Tart 2016 was the top-traded wine by value, coming in fifth place overall.

Champagne came in third place, claiming 12.2% of traded value. While the 2015 vintage was the top-traded by value, 2008s changed hands most frequently. Dom Perignon dominated, though Louis Roederer and Krug also took substantial shares.

The Rhone took a 4.7% share, up from December’s 3.4%. While Rayas has been leading the Rhone 100’s recent upward price movement, E. Guigal was the top-traded producer this week, accounting for a third of its trade.

Both Tuscany and Piedmont had strong weeks, though the former dominated with a 10.9% share of overall trade value, San Guido and Masseto claiming the top positions.

US wines performed relatively well, claiming a 6.4% market share. UK buyers led, taking over half of traded value.

Thanks to Vega Sicilia, making up 60% of trade, Spain’s share rose to 5.5%. Alongside trades of recent vintage Unico, Valbuena and Alion, a magnum of 1962 Unico – still within its drinking window — also changed hands.

Liv-ex Reports

Power 100

Liv-ex has published its annual Power 100 report, ranking the most influential fine wine brands in the secondary market. After a challenging 2024, the latest…

The topics covered in the Power 100 2025:

• Regional breakdown
• Bordeaux: a question of pricing
• Cheval Blanc moved up. Yquem provides a example of how fair pricing can positively impact a brand.
• Burgundy: mid-range prevails
• Given that most Burgundy brands produce multiple wines under both domaine and maison labels, the Power 100 is inherently skewed in their favour.
• Italy: Super Tuscans
• Having taken Bronze last year, San Guido claimed Silver this year, solidifying its reputation as not only one of the most important, but most reliable brands on the market.
• Champagne: Selosse and Salon
• The region nevertheless performed well in the Power 100
• The rest of the world

Liv-ex members received the Power 100 Report at the end of last year.

Liv-ex Reports

December Market Report

The Liv-ex December Market Report 2025 takes a look into which wines have proven most resilient to the market downturn?

The topics covered in the Liv-ex November Market Report 2025:

• Opportunity For Buyers to Capitalise on Lowest Prices Since 2020
• Fine wine prices are at their lowest in five years. Demand is rising, with bids on Liv-ex hitting £31M, the highest since April 2023, and UK and Asian buyers driving a 135% surge in activity.  
• Signs of Recovery: Market Indices Show Strongest Gains in 3 Years
• Liv-ex’s major indices, the Liv-ex 50, 1000 and Bordeaux 500, have posted their first gains since early 2023, marking the strongest rise in over three years. 
• Resilient Labels Shine Amid Market Correction
• Having experienced only limited price increases into the market peak, Sauternes has seen relatively modest declines during the downturn.

Liv-ex members receive comprehensive analysis of the market every month.

Liv-ex Reports

Talking Trade 12th December: Bordeaux continues to dominate, Cos d’Estournel 21 in the lead

Bordeaux took a strong lead of the market with 42.3% share of the market, up from 37.1% last week. 2019s, 2021s and 2020s proved the…

• Market Intelligence

• Bordeaux led the market with a 42.3% share of traded value. Burgundy followed with 21.5%.
• Cos d’Estournel 2021, Chateau Lafite Rothschild 2020 and Le Pin 2015 were the top-traded wines by value.
• Asian buyers pulled ahead of US buyers with a 19.7% and 14.5% share of purchasing respectively.

Bordeaux took a strong lead of the market with 42.3% share of the market, up from 37.1% last week. 2019s, 2021s and 2020s proved the top-traded vintages by value. Chateau Lafite Rothschild, Cos d’Estournel and Le Pin were the region’s top-traded producers.

Burgundy’s share of trade also rose, up from 16.8% last week to 21.5% this week, the 2022 and 2019 vintages changing hands most frequently.

Champagne’s share fell to 9.2%. Cristal and Dom Perignon dominated, together accounting for c.30% of the region’s trade.

Tuscany and Piedmont both increased their share of the market slightly, the latter region taking the edge over the former (7.4% vs. 7.5%). Giacomo Conterno and San Guido were Italy’s top-traded producers by value.

Following a strong close last week, the US’s share of trade fell substantially to 3.8%. Opus One continued to trade actively, accounting for 30% of the region’s trade, purchasing driven largely by US buyers.

Liv-ex Reports

Talking Trade 5th December: Petrus the top-traded producer as Champagne trade picks up

Bordeaux continued to take a higher share of trade value this week (38.9%). Château Mouton Rothschild was the region’s top-traded producer

• Market Intelligence

• European buyers accounted for 43.4% of trade
• Increased Opus One trade helps US wine to its largest share of trade in recent months

4,354.0 per bottle). 

Breakdown of regional trade

Champagne and the USA both had stronger weeks. Taittinger, Comtes de Champagne Blanc de Blancs sold in good volume across the 2014 and 2013 vintages. While Champagne trade beyond that was relatively muted, the region took a 12.8% share of trade value, up from 7.8% last week and just above the November average (11.4%).

Opus One was the USA’s top-traded producer, with the 2021 vintage trading in decent volume at £2,740 / $3,664, equivalent to € 261.4 per bottle. The USA took an 11.8% share of trade value, compared to 2.1% last week and a November average of 5.9%.

Bordeaux took a 37.1% share. Petrus was the region’s top-traded producer, with the 2009 and 2015 among the week’s overall top-traded wines.

Breakdown of buyer geography

Liv-ex Reports

Talking Trade 28th November: Bordeaux maintains a healthy advantage with Mouton Rothschild the top-traded producer

Bordeaux continued to take a higher share of trade value this week (38.9%). Château Mouton Rothschild was the region’s top-traded producer

• Market Intelligence

• Bordeaux continues to stretch ahead, taking 38.9% of trade value 
• Bouchard La Romanée 2001 was the top-traded wine by value 
• With Liv-ex indices due to be published next week, the Fine Wine 50 is currently up 2.0% month-on-month

Bordeaux continued to take a higher share of trade value this week (38.9%). Château Mouton Rothschild was the region’s top-traded producer, while Petrus 2022 was the region’s top traded wine, last changing hands at £45,912 / $60,516 per 12×75 (€4,354.0 per bottle). 

Burgundy’s share of trade ticked back up (19.2%), with US buyers focusing their attention on the region. 27.5% of US purchases were for Burgundy (the highest of any wine region), with high value d’Auvenay trades coming from US buyers. 

With the news of Château Rayas’ Emmanuel Reynaud passing this week, the Rhône took its largest share of trade value in recent memory (8.0%), in the process leapfrogging Champagne which had a quiet week. Multiple vintages of Rayas and Fonsalette traded, accounting for 75% of all Rhône trade.

Breakdown of buyer geography

Liv-ex Blog

Meet Paolo-Luca, Liv-ex Broker for the Asia Market 

Meet Paolo-Luca ! He is a Broker at Liv-ex, he has specialist knowledge of the Asian fine wine market, and supports merchants across Asia Pacific…

What is the role of a Broker at Liv-ex?  

At Liv-ex, the Broking team helps members to facilitate deals on the Exchange. It’s our job to understand our members interests and cellars inside out; we know what they’re looking to buy, what they’re willing to pay for those wines, what stock they are looking to sell, and again what price they’re willing sell at. We act as an extension of our members teams really. We’re an additional pair of eyes looking for relevant opportunities on the Exchange, an additional negotiator, it’s all about helping our members find opportunities and pushing them through as effectively as possible. 

The Broking team supports members in a number of ways, we:  

1. Identify opportunities to trade based on the wines we know our members are interested in buying, or the wines we know they have to sell 

2. Advising on trading strategy, where a slight adjustment could help our members to maximise margin 

3. We act as a sounding board, helping two parties match a deal 

4. We also help them understand the market they operate in, the regional nuances, and opportunities based on the current market dynamics 

What can you tell us about the Asian markets fine wine trading habits, and how has this changed in recent years?  

The market in Asia has undergone a huge transformation. Traditionally, fine wine buying was driven by consumers seeking the right brands and labels at the lowest prices, with little attention paid to vintage quality. Today, there is a focus on wines that offer drinking appeal now, with buyers increasingly focused on finding the right vintage that delivers quality. 

Every country in Asia has its own unique relationship with fine wine… Singapore is the central hub for all South East Asian trade; emerging markets in Thailand, Malaysia, Indonesia and Vietnam all operate through Singapore. In Singapore itself, younger collectors with ‘new money’ are open to new regions and styles. They’re still buying top end Burgundy and Rhone, but they’re also experimenting. 

In Vietnam the younger drinkers are also experimenting. There’s strong demand for full-bodied, over-extracted ‘Parkerised’ wines. Yes, there’s the older demographic who buy more traditionally, but the young generations want to drink wines such as Pavie, Primitivo, Aussie Shiraz and the like. 

Hong Kong has traditionally been a stock-holding hub, but it’s now a consumption-led market. Wines are often opened the night they’re bought, reflecting a recent shift in demand from long-term holding and towards immediate enjoyment. From speaking with members on the ground, it appears this resurgence in demand is being driven by local Hong Kong residents, and not by the Mainland Chinese who used to drive the local market. 

Mainland Chinese buyers have been largely absent since early 2023, following a crackdown on cross-border trade and ‘personal consumption’ allowances by the Chinese authorities. And in Hong Kong, private clients are returning after years of overstocking. Much of that inventory has now been consumed, creating new opportunities to buy back vintage wines at prices not seen for a decade. 

What challenges do Asian members encounter when trading fine wine, and how does the Broking team support them?  

Asian members operate in one of the most competitive and fast-moving regions of the fine wine market. I’d say there are three main challenges that come with this, 1. price sensitivity, 2. condition and provenance, and 3. logistics complexity. 

The market is incredible price-sensitive; private clients are not loyal, most buying happens online, and they are trying to find the cheapest deal, from the cheapest merchant. Often private collectors will buy from a new merchant just because they are offering the same wine at 10 HKD less. I spend time getting to know merchants, understanding what sells (and importantly what doesn’t), and ensuring I am offering sub-market deals to help merchants foster their client base whilst maximising their margin.   

When it comes to condition and provenance, buyers need absolute assurance that the wine they receive is in good condition. There is a lot of scepticism in the market that if a label is damaged or even some packaging gets damaged in transit that it may not be authentic. Given its location, its distance, buyers in Asia can’t simply send a wine back, lead times can be long, and delays can really impact a merchants margins. If there’s any doubt about provenance or quality, trades can be delayed or even fall though. 

On the Liv-ex Exchange members can see the condition a wine is in. Most wines are available in good condition, which has been verified by a Broker through pictures at the point a wine is listed for purchase on the Exchange. And then when the wine passes through a Liv-ex warehouse, high-risk cases are flagged and checked by our teams. Our teams are trained to look for anticounterfeiting measures such as microprint, holograms and QR codes. We work closely with producers and authentication experts to ensure we know what to look for, and that our measures and precautions are industry leading. 

Cross-border trading can be a real challenge, from customs documentation to bonded warehouse transfers, fulfilment can slow down and really impact profitability. To simplify this, Liv-ex coordinates the entire logistics process, from moving the wine from seller to buyer, to verification and authentication, right through to customs documentation. We ship directly to Hong Kong on a weekly basis, so merchants in Asia receive their wines in a timely manner. Throughout this process merchants are only ever dealing with Liv-ex for invoicing, transport, shipment. We work with the member on the other side of the deal to take on the complexities of logistics.  

Why is Lix-ex so beneficial for Asia’s fine wine merchants?  

Liv-ex is a global network of buyers and sellers. When making a trade you could be buying from any one of 550 different merchants globally, all efficiently settled and made available by us. To give you an example, one of my clients from Asia has bought off 135 different merchants this year alone… Everywhere from UK, France and Italy, to Austria, Norway and Cyprus! And we’ve handled all the hassle of moving, condition checking, and getting the wine ready at a logistics hub of your choosing. Another one 

Liv-ex enables members to understand the dynamics of the region they operate in, as well as the holistic fine wine market. They can access granular data and insights to understand the transactional pricing of a wine over the past 25 years, and they can streamline operations and logistics to trade more efficiently. 

In Hong Kong and Singapore specifically, Liv-ex has launched local pricing benchmarks, allowing members to compare regional prices with global trends. That helps identify undervalued wines, spot early price movements and time deals more effectively. 

Beyond the data and the Exchange, I meet merchants in person, speak with them regularly, and offer tailored insight into market dynamics. Being on the ground helps me to understand local pressures, and offer tailored support.  

Before you go, what drew you to the world of fine wine, and Liv-ex?  

I was first introduced to wine by my father when I was young. I used to help him serve wines at his dinner parties and helped him out in the cellar. We both grew our love for wines together and always want to show each other new and exciting regions, producers and styles. Since I moved professionally into fine wine, I have not looked back.  

What drew me to Liv-ex was the opportunity to be at the centre of the market. Liv-ex isn’t just a trading platform, we work directly with our members, speaking to them, in some cases daily, and meeting them in person. We get to truly understand the holistic fine wine market, and then use this knowledge and insight to help our members. I get to see the real-time dynamics of the entire industry: merchants, châteaux, trade, and the opinions that shape it all. It’s a level of visibility and understanding that’s unmatched.  

As a Broker, I can answer questions like “What’s happening in Asia right now?”, with confidence, because I have the data and insight to back it up. Whether it’s pricing trends, demand signals or market movement, I’m equipped to give honest, real-time insight that helps members make smarter trading decisions.  

Learn more about Liv-ex Brokers, and how your fine wine business could benefit from Liv-ex