Market Outlook 2024 J. P. Morgan Research

Market outlook for 2024: Slow global growth clouds forecast for equities

What does a challenging macro environment of slowing growth and stubborn inflation mean for markets? Explore outlooks for equities, commodities, currencies, emerging markets, and more.

Key takeaways

J. P. Morgan Research sees only a modest risk of a global recession in the near term, but projects the end of the global expansion by mid-2025.
Stubborn inflation above central banks' comfort zones is expected to push interest rates higher for the long term. Current market expectations for an early start to the developed markets (DM) easing cycle are likely to be disappointed.
A more challenging macro environment is expected for the equity market in 2024. Weak earnings growth and geopolitical risks weigh on the equity outlook. J. P. Morgan analysts expect S& P 500 earnings growth of 2-3% and a price target of 4, 200 with a downward bias.

"As we approach 2024, we expect both inflation data and economic demand to soften, weakening the tailwinds for growth and risk markets. Overall, we are cautious on risk asset performance and the broader macro outlook for the year ahead, given financial headwinds, geopolitical risks, and expensive asset valuations.

Chief Global Markets Strategist and Global Co-Head of Research, J. P. Morgan

Global market outlook

2023 started with low and declining expectations for global growth and heightened fears of a recession. However, China’s reopening, large fiscal stimulus in the U.S. and Europe, and the residual strength of U.S. consumers stabilized growth. Additional market optimism was related to ChatGPT, luxury goods, weight-loss drugs, the expectation of Federal Reserve (Fed) rate cuts and the bitcoin rally, resulting in a broadly positive performance for risk markets. That was despite the largest increase in interest rates in decades, major wars, an energy crisis, a regional banking crisis, recession in parts of the eurozone and emerging signs of credit and consumer deterioration in the U.S.

Contemporaneous positive economic data was enough to lift risk markets, which could be seen as complacency against a backdrop of declining consumer strength and increased credit stress (e.g. rising credit card and auto loan delinquencies). Household liquidity trends indicate that for 80% of consumers, excess savings from the COVID era are already gone, and by mid-2024 it is likely that only the top 1% of consumers by income will be better off than before the pandemic.

“We expect both inflation data and economic demand to soften in 2024. Should investors and risky assets welcome an inflation decline and bid up bonds and stocks, or will the fall in inflation indicate the economy is sliding toward a recession? We think the decline in inflation and economic activity we forecast for 2024 will at some point make investors worry or perhaps even panic,” said Marko Kolanovic, Chief Global Markets Strategist and Global Co-Head of Research at J.P. Morgan.

“Overall, we are not positive on the performance of risky assets and the broader macro outlook over the next 12 months. The primary reason is the interest rate shock (over the past 18 months) will negatively impact economic activity. Geopolitical developments are an additional challenge as they impact commodity prices, inflation, global trade in goods and services and financial flows. At the same time, valuations of risky assets are expensive on average,” Kolanovic added.

It is hard to see an acceleration of the economy or a lasting risk rally without a significant reduction in interest rates and reversal of quantitative tightening. This is a catch-22 situation, in which risk assets can’t have a sustainable rally at this level of monetary restriction, and there will likely be no decisive easing unless risky assets correct (or inflation declines due to, for example, weaker demand, thus hurting corporate profits). This would imply that some market declines and volatility would need to take place first during 2024 before easing of monetary conditions and a more sustainable rally.

Avoiding recession has now become consensus thinking but looking at the relatively small number of recessions throughout history as a reference point, yield curve inversion signals indicate recession risk is highest between 14 and 24 months after the onset of inversion.

“That time period will cover most of 2024 and should make it another challenging year for market participants,” Kolanovic said.

Equity market outlook

In 2022, the S&P 500 slid close to 20% in the wake of the Fed’s decision to rapidly hike interest rates. However, equity markets advanced in 2023, recovering some lost ground.

While stocks have remained positive year to date, the outlook for earnings growth has not been as strong as investors hoped. Equity concentration in the S&P 500 is now at levels not seen since the 1970s, meaning the rise in stocks this year has been driven by a cluster of tech mega-cap stocks. This dynamic, which has been seen ahead of previous economic slowdowns — along with an end to a period of record pricing power as 40-year high inflation begins to soften — suggests corporate margins are set to face major headwinds in 2024.

S& P 500 Outlook for 2024

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J. P. Morgan Research expects 2%-3% earnings growth for the S& P 500 in 2024, with a price target of 4, 200.

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"The Fed Absent rapid easing by the Fed, consumer behavior will soften at a time when investor positioning and sentiment have nearly reversed, predicting a tougher macro environment for stocks next year. Equities are currently richly valued with volatility near historic lows, but geopolitical and political risks remain elevated. "We expect stocks to decline from current levels due to weak global earnings growth," said Dubravko Lakos-Bujas, global head of U. S. equity and quantitative strategy at J. P. Morgan.

For the S& P 500, J. P. Morgan Research has a downward bias, forecasting earnings growth of 2-3% next year, earnings per share (EPS) of $225, and a price target of $4, 200.

J. P. Morgan economists have a 2024 target of $1. 2 billion. We expect U. S. and global growth to slow by the end of the year. At the same time, liquidity continues to shrink as major central banks reduce their balance sheets at unprecedented rates, and borrowing rates remain restrictive for consumers and businesses alike.

According to Morgan Research estimates, U. S. households have decreased from $ 3. 4 trillion (t) at the peak of $ 3. 4 trillion (t) at the peak, almost exhausted by the second quarter of 2024. It is expected to do.

"It is difficult to identify the start and depth of the economic recession in advance, but we have a recession, despite the fact that investors have consistently incorporated this uncertainty regardless of the region, style, or sector. I think it is a living risk next year.

J. P. Morgan, Chief Global Equity Strategist

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The geopolitical risks are still high, two major conflicts are ongoing, and national elections will soon be held in 40 countries, including the United States. As a result, the volatility of the stock is expected to rise generally in 2024 than 2023, and the upwear is determined by the timing and seriousness of the economic recession.

"It is difficult to identify the start and depth of the economy in advance, but investors have not yet organized this uncertainty, regardless of the region, style, or sector, and the risk of next year. We think we are alive, "said Laco s-Bujas.

From a regional point of view, the United States continues to have quality premiums to other markets due to its sector configuration and cas h-rich meg a-cap stocks.

Other than the United States and the International Invisory Market (DM), the outlook for British shares is optimistic because the valuation is greatly supported and the sector configuration is good.

"Despite the cheap variety, European stocks are expected to be V-shaped and will be relatively flat this year. On the other hand, Japan may recover the entry of retail companies, strong balance sheets. Misraf Mateka states that J. P. Ta.

High interest rates, geopolitical trends, and dollars increase, resulting in unevenness at the beginning of the emerging market (EM). However, emerging nations should be more attractive until 2024 due to the gap between the growth of emerging countries and the Middle East, the declining diversified investment demand from the United States, and the decline in investors' positioning.

For China, which was greatly delayed this year, it is expected that performance will be improved if the geopolitical risk is suppressed.

"Bond yields will peak ahead of rate cuts, and stocks will likely adjust due to the disconnect between a slowing economy and unrealistic consensus earnings expectations.

Global Head of Cross-Asset Strategy, J. P. Morgan

Global economic forecast

Global growth exceeded expectations in 2023. Despite synchronized monetary tightening from central banks around the world, the private sector proved to be resilient and positive fiscal and commodity price shocks also provided relief.

J.P. Morgan economists expect the global economy to avoid a near-term recession, but an end to the global expansion by mid-2025 remains the most likely scenario.

In this scenario, inflation remains sufficiently sticky at around 3%, meaning central banks will maintain higher-for-longer policy stances. This will ultimately lead to an earlier end to the expansion than currently anticipated by many.

But at the same time, with a healthy private sector that has weathered the monetary tightening cycle surprisingly well and some disinflationary signs emerging, soft-landing optimism is on the rise.

"Our top-down view has become more tolerant of a soft landing scenario (up to 40%), but we remain biased towards an end to the global expansion by mid-2025.

Chief Global Economist, J. P. Morgan

Our outlook for the global economy is as follows:

Growth will slow as rising yields and credit crunch take hold while positive shocks weaken.
Inflation is expected to moderate due to lingering supply damage and shifting inflation sentiment.
This pressure will likely be concentrated in the corporate sector, where margins will shrink, encouraging a slowdown in hiring and spending.
Vulnerabilities will increase over time: The likelihood of a recession is 25% by H1 2024, 45% by H2 2024, and 65% by H1 2025. We see inflation at 60%.
Inflation will not fall to our target on a sustained expansionary path, but recent developments have eased skepticism.
The U. S. supply-side performance has been impressive this year, with labor markets easing despite strong growth.
Lack of domestic demand in China and Europe suggests disinflation may persist.
A soft landing hinges on lower inflation allowing monetary easing to begin by around mid-year.
A mild recession would not be a benign event and would produce far worse outcomes than a low-growth soft landing.

Since mid-2022, J. P. Morgan Research's World Economic Outlook has moved away from focusing on a single scenario and instead focused on recognizing a range of outcomes, each with significant potential.

"It is no surprise that soft-landing optimism is now emerging, boosting asset prices and expectations of early policy easing. Bruce Kussman, chief global economist at J. P. Morgan, said, "Our top-down view has become more tolerant of a soft-landing scenario (up to 40%), but we remain open to a soft-landing scenario in 2025." We remain biased in our view that the global expansion will end by mid-2024.

We place the most weight on the “boiled frog” scenario, where rising interest rates eventually push the global economy into recession. This has a 60% chance,” Kassman added.

World real GDP

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We expect EM countries to outgrow DM countries in real GDP growth in both the first and second half of 2024.

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Rates forecast

The reversal of the fastest and most synchronized DM central bank tightening cycle of 2022–23 will start in the second half of 2024, against a backdrop of muted growth and falling inflation.

On the monetary policy side, the global tightening cycle across DM central banks will be most likely completed by the end of 2023. Central banks will be patient in holding policy rates if confidence around the convergence of inflation to target holds, but some will be under pressure to make additional hikes if the decline is too slow.

"It is expected that the yield will decrease in 2024 and the curve will be urgent. The 1 0-year yield is 4. 25%for the year and 3. 75%at the end of 2024."

J. P. Morgan, the US interest rate strategy c o-manager

The inflation rate in 2024 is expected to continue to decline due to a decline in energy pressure and weakening the labor market.

Headline inflation rate of DM countries

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By the end of 4Q24, the inflation rate of the developed country market is expected to approach the target value set by the central bank.

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Since the tenacity in the downturn is expected, the central bank has a lon g-term hig h-stop pressure, and early interes t-down observations retreat. On the other hand, the downward pressure of inflation will give the Central Bank with the confidence that the tightening conducted to approach the target of inflation is effective.

"If a macr o-like bass line called soft landing is developed, a steady and gentle easing cycle for the neutral level of interest rates is expected in the Middle East and African countries, and the start, pace, and termination are depending on the country and region. Fabio Bass, the European interest rate strategy of J. P., said, "However, in a recession scenario where the macr o-like prospects justify monetary easing, the risk is tilted in the direction of accelerating interest rates." I mentioned.

In the United States, the US Federal Open Market Committee (FOMC) is likely to start a 2 5-bp pace for each meeting from the third quarter of 2024, and quantitative tightening (QT) will continue until 2024.

"In 2024, the yield is expected to decrease and the curve steep is expected, and the largest movement is expected to occur after spring. J. P. Morgan's US Interest Strategy c o-operator Jay Barry said," The 1 0-year yield is the annual yield. We expect 4. 25%to 3. 75%at the end of 2024.

Commodity markets outlook

After falling in 2023, J.P. Morgan Research expects Brent oil prices to remain largely flat in 2024 and edge down a further 10% in 2025.

“Our Brent forecast has not changed since June and is expected to average $83 per barrel (bbl) in 2024,” said Natasha Kaneva, Head of Global Commodities Strategy at J.P. Morgan.

This will be buttressed by solid supply-demand fundamentals. “Despite sustained economic headwinds, we see oil demand rising by 1.6 million barrels per day (mbd) in 2024, underpinned by robust emerging markets, a resilient U.S. and a weak but stable Europe,” Kaneva said.

"Despite the sustainable headwind of the economy, the demand for oil in 2024 is supported by a stable emerging market, a resilient United States, a stable but stable European, and an increase of 1. 6 million barrels (MBD). I'm watching.

J. P. Morgan, Global Commodity Strategy Head < Span> See Infographic Text Version

"It is expected that the yield will decrease in 2024 and the curve will be urgent. The 1 0-year yield is 4. 25%for the year and 3. 75%at the end of 2024."

J. P. Morgan, the US interest rate strategy c o-manager

The inflation rate in 2024 is expected to continue to decline due to a decline in energy pressure and weakening the labor market.

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See the text version

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See Infographic See the text version

FX outlook

Against an uncertain macro backdrop, how will FX perform in 2024?

“Foreign exchange (FX) market participants’ view on the macro outlook remains wide, spanning from a soft landing and additional Fed hikes to recession. Needless to say, they will need to navigate the transition among these scenarios tactically as these would imply different outcomes for the U.S. dollar,” said Meera Chandan, Co-Head of Global FX Strategy at J.P. Morgan.

While the road ahead for the U.S. dollar (USD) looks bumpy, the greenback is expected to remain at elevated levels, with potential for new highs. “If rate cuts are realized, the dollar would still yield more than 56% of global currencies on a real basis in 2024,” Chandan said.

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"It is expected that the yield will decrease in 2024 and the curve will be urgent. The 1 0-year yield is 4. 25%for the year and 3. 75%at the end of 2024."

J. P. Morgan, the US interest rate strategy c o-manager

The inflation rate in 2024 is expected to continue to decline due to a decline in energy pressure and weakening the labor market.

Headline inflation rate of DM countries

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Emerging markets outlook

By the end of 4Q24, the inflation rate of the developed country market is expected to approach the target value set by the central bank.

See Infographic See the text version

The US cycle will be a major EM driver

EM growth is set to moderate

Inflation will return to central bank comfort zones for most

J. P. Morgan, Global Commodity Strategy Head

However, to keep the oil market in balance, the OPEC+ coalition will need to continue to curb production. J. P. Morgan Research expects Saudi Arabia and Russia to extend their voluntary production and export cuts into the first quarter of 2024. Assuming Saudi Arabia pumps more oil and Russia boosts exports, global oil inventories will likely remain flat in 2024.

Global Research

In the U. S. gas market, oversupply likely limits upside risk to U. S. gas prices in 2024. “We see two stories for this year. The first is oversupply and weak prices, which could extend into the first half of 2024 and into the summer natural gas injection season,” says Shikha Chaturvedi, head of global natural gas and natural gas liquids strategy at J. P. Morgan. "Another is that feed gas demand could offset, but even exceed, regional supply growth.

The increase in appetite for obesity drugs

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In 2024, Brent crude oil is expected to average $83/bbl, natural gas $3. 34/MMBtu, gold $2, 175/oz, silver $30/oz, and wheat $6. 33/oz.

Energy supercycle: Will oil prices keep rising?

Turning to metals, gold and silver are expected to outperform other sectors. Driven by the Fed rate-cutting cycle and declining US real yields, gold prices are expected to reach a new nominal high in mid-2024, averaging $2, 175/oz in the fourth quarter. In the same way, silver prices will follow gold, averaging $30/oz in the fourth quarter. ounces.

"Across all metals, we have the highest conviction in medium-term bullish forecasts for gold and silver through 2024 and the first half of 2025," said Gregory Shearer, head of base and precious metals strategy at J. P. Morgan.

In agriculture markets, price risks are skewed to the upside from current spot levels, particularly through the first half of 2024," said Tracy Allen, J. P. Morgan agricultural strategist. "Our price forecasts are bullish for sugar through 2024, with modest gains for grain, oilseed and cotton markets. Sugar prices are expected to average $0. 30/lb and wheat prices to average $6. 33/bushel in 2024.

Major Currency Pair Outlook

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Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis

By December 2024, EUR/USD is expected to reach 1. 13, GBP/USD 1. 26, USD/JPY 146, AUD/USD 0. 68, CAD/USD 1. 33, and NZD/USD 0. 60.

View Infographic View Text Version Turning to the euro, a convincing recovery in 2024 seems unlikely as the region is on the brink of recession amidst capped interest rates. For the single currency to recover, it needs not only Fed easing but also improved growth prospects for the region. "J. P. Morgan Research expects the euro/dollar pair to hover between parity and 1. 05 in the first half of 2024.

The outlook is similar for the British pound, with the market oscillating between persistent inflation and low growth in 2024. The crucial question for the British pound in 2024 will depend on the extent to which policy tightening this year slows growth and the labor market and whether the Bank of England (BOE) feels comfortable enough about the inflation outlook to cut the bank rate.

"We are bearish on the pound heading into 2024, but we are mindful that the economy is more resilient to policy tightening than we think," Chandan added. J. P. Morgan Research expects the sterling/dollar pair to fall to 1. 18 in the first quarter of 2024 before rising to 1. 26 by December.

1. Introduction

In Asia, structural pressures will continue to weigh on the Japanese Yen in 2024. "We expect the yen to strengthen in the second half of 2024 due to short-term factors, namely changes in relative policy rates. However, this strengthening may be shallow due to the underlying long-term downward trend.

"Our focus through 2024 will be on the US economy and how business cycle uncertainties resolve.

Head of Global Macro Research, J. P. Morgan

Overall, the EM outlook will be heavily driven by US growth and the monetary policy cycle. Europe, Middle East and Africa in 2024 will be driven by these three themes:

What is noted is the Soft Landing Scenario and the US cycle, where the economic recession emerges. "J. P. Morgan's global macro research head, Luis Oganes, said," There are hundreds of scenarios' risk premiere of EM assets, hundreds of Basis points. "I until 2024. It is the US economy, and there is room for the short term in the short term. Until the cycle is dominant, EM's monetary policy and default cycle should be the focus of investment opportunities, "Oganes says.

The growth rate of EM is expected to slow down from 4. 1%to 3. 8%slightly below the trend in 2024. China's growth rate will decrease to 4. 9%, but the number of policy support for the first half will exceed the first half of this year's growth rate of 5%(AR). By region, Asia EM growth will accelerate, exceeding EMEA's strong growth and further deceleration in Latin America.

J. P. Morgan Research expects headline inflation and core inflation in EM countries, excluding China and Turkey, and will converge near the end of 2024 to nearly 3. 5%yea r-o n-year. Monetary policy will only be a suppressed interest rate.

"J. P. Morgan Global Research is looking forward to providing insights and ideas for investment in 2024 as you navigate the market that becomes more and more complicated, and since then.

2. Model

J. P. Morgan Research Section Global C o-Common Office

Global Research

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This report is for information only. For more information, including important disclosure, please read the JP Morgan Research report related to this content. JP Morgan Chase, its affiliated companies and/ or subsidiaries (hereinafter collectively J. P. Morgan) form a market for securities, other financial products, and other asset classes We are trading as a principal.

This material is created based on market prices, data, and other information obtained from the sources that are reliable, but J. P. J. P. Morgan does not guarantee its integrity or accuracy, except for the disclosure of securities, other financial products, and other asset classes) and the relationship between analysts. Any opinions and predictions may be changed without notice, at the date of the date of this material. Past achievements do not suggest future results. This material does not solicit the buying and selling of financial products. J. P. Morgan Research does not provide individual customized investment advice. The opinions and recommendations described here do not take into account the status, purpose, and needs of each customer, and does not recommend specific securities, financial products, and strategies for specific customers. Please make your own decisions on or relevant securities, financial products, and strategies described here. Based on specific trends, presentations, market conditions, and other generally available information, we may provide the latest information on companies, issues, or industries. However, J. P. Morgan may restrict the information included in this communication for regulation or for other reasons. You will need to contact analysts through your JP Morgan subsidiary or affiliated company in your own JP Morgan subsidiary unless otherwise stipulated according to compliant law.

As long as J. P. Morgan's explicit consent, all or part of this communication cannot be redistributed or resigned in any format or method. It is forbidden to use or disclose without permission. If this information is received and confirmed, it is assumed that the content and information included in this communication will not be redistributed or resent without obtaining a explicit permission from the J. P. Morgan authority. 。 < SPAN> This material is created based on market prices, data, and other information obtained from the information sources that seem to be trusted, but it can be eligible for J. P. J. P. Morgan does not guarantee its integrity or accuracy, except for the disclosure of the company (or other financial products, other financial products, and other asset classes) and analysts. Any opinions and predictions may be changed without notice, at the date of the date of this material. Past achievements do not suggest future results. This material does not solicit the buying and selling of financial products. J. P. Morgan Research does not provide individual customized investment advice. The opinions and recommendations described here do not take into account the status, purpose, and needs of each customer, and does not recommend specific securities, financial products, and strategies for specific customers. Please make your own decisions on or relevant securities, financial products, and strategies described here. Based on specific trends, presentations, market conditions, and other generally available information, we may provide the latest information on companies, issues, or industries. However, J. P. Morgan may restrict the information included in this communication for regulation or for other reasons. You will need to contact analysts through your JP Morgan subsidiary or affiliated company in your own JP Morgan subsidiary unless otherwise stipulated according to compliant law.

3. Data

As long as J. P. Morgan's explicit consent, all or part of this communication cannot be redistributed or resigned in any format or method. It is forbidden to use or disclose without permission. If this information is received and confirmed, it is assumed that the content and information included in this communication will not be redistributed or resent without obtaining a explicit permission from the J. P. Morgan authority. 。 This material is created based on market prices, data, and other information obtained from the sources that are reliable, but J. P. J. P. Morgan does not guarantee its integrity or accuracy, except for the disclosure of securities, other financial products, and other asset classes) and the relationship between analysts. Any opinions and predictions may be changed without notice, at the date of the date of this material. Past achievements do not suggest future results. This material does not solicit the buying and selling of financial products. J. P. Morgan Research does not provide individual customized investment advice. The opinions and recommendations described here do not take into account the status, purpose, and needs of each customer, and does not recommend specific securities, financial products, and strategies for specific customers. Please make your own decisions on or relevant securities, financial products, and strategies described here. Based on specific trends, presentations, market conditions, and other generally available information, we may provide the latest information on companies, issues, or industries. However, J. P. Morgan may restrict the information included in this communication for regulation or for other reasons. You will need to contact analysts through your JP Morgan subsidiary or affiliated company in your own JP Morgan subsidiary unless otherwise stipulated according to compliant law.

3.1. Data Descriptions

Use the Garch-Midas model to extract long and short-term volatility components of cryptocurrency. As a potential driver of Bitcoin's volatility, the US stock market volatility and risk indicators and global economic activities indicators were taken into account. As a result, the volatility of S & amp; p 500 has a negative and significant effect on lon g-term bitcoin volatility. This discovery is typical in the c o-motion of volatility throughout the financial market. In addition, S & amp; p 500 volatility risk premium has a significant effect on the lon g-term volatility of bitcoin. Finally, it was found that there is a strong positive correlation between Baltic Dry Index and Bitcoin's lon g-term volatility. This shows that bitcoin volatility is closely related to global economic activities. As a whole, our knowledge can be used to build improved predictions of Bitcoin lon g-term volatility.

keyword

JEL classification

C53; C58; f31; G15

3.2. Summary Statistics

"After Lehman Brothers collapsed in September 2008, US stocks fell by more than 20 % on the 24th, and officially entered the weak market area. The coin did the same thing on Wednesday in less than six hours.

Financial Times-November 30, 2017-Bitcoin goes back to being bullish to bearish in one day

Bitcoin rising, subsequent drops, and unstable fluctuations are attracting attention by scholars and business leaders. There are many critics. For example, the Nobel Prize winner Joseph Staglitz states that Bitcoin should be illegal, and Nobel laureato Robert Silla is also an attractive to some investors. The government states that there is a rebellious atmosphere. Many business leaders, including Karl Ichan and Warren Buffett, describe the bubble of the rising price of bitcoin as a bubble. JP Morgan's Jamie Dimon's CEO, CEO, predicted that it would eventually imply a collapsed bubble and eventually blown away. Lloyd Blank Fine, CEO of Goldman Sachs, also acknowledged that the currency would be possible if the volatility decreased, but the currency was a means of fraud.

Cryptocurrencies also have their advocates and enthusiasts. CME Group listed Bitcoin futures in mid-December 2017, and Nasdaq plans to launch Bitcoin futures later this year. They also have many supporters in Silicon Valley. With the listing of Bitcoin futures and the popularity of cryptocurrencies in general, the literature on the topic has also grown.

4. Empirical Results

4.1. Macro and Financial Drivers of Long-Term Bitcoin Volatility

Most of the existing studies focus on Bitcoin returns. For example, Baur et al. (2017) show that Bitcoin returns are essentially uncorrelated with traditional asset classes such as stocks and bonds, pointing out the potential for diversification. Others have investigated the determinants of Bitcoin returns. Among them, the findings of Li and Wang (2017) suggest that measures of financial and macroeconomic activity are drivers of Bitcoin returns. Kristoufek (2015) considers financial uncertainty, Bitcoin trading volume in Chinese yuan, and Google Trends as potential drivers of Bitcoin returns. The inclusion of Google Trends as a proxy for some type of sentiment or interest is fairly common in the literature (e. g., Polasik et al. A recurring theme in the literature is the question of which asset class Bitcoin belongs to, with many comparing it to gold, others to precious metals or speculative assets (see, in particular, Baur et al. (2017) or Bouri et al. (2017)). Some classify Bitcoin as somewhere between a currency and a commodity (see, for example, Dyhrberg (2016)). For other recent contributions, see Cheah et al. (2018); Khuntia and Pattanayak (2018); and Koutmos (2018). Cryptocurrencies also have their advocates and enthusiasts. The CME Group listed Bitcoin futures in mid-December 2017, and Nasdaq is set to launch Bitcoin futures later this year. It also has many supporters in Silicon Valley. The listing of Bitcoin futures and the popularity of cryptocurrencies in general have led to a growing literature on the topic. Most of the existing research focuses on Bitcoin returns. For example, Baur et al. (2017) show that Bitcoin returns are essentially uncorrelated with traditional asset classes such as stocks and bonds, pointing out the potential for diversification. Others have also investigated the determinants of Bitcoin returns. Among others, Li and Wang (2017) find that measures of financial and macroeconomic activity are drivers of Bitcoin returns. Kristoufek (2015) considers financial uncertainty, Bitcoin trading volume in Chinese yuan, and Google Trends as potential drivers of Bitcoin returns. The inclusion of Google Trends as some kind of proxy for sentiment or interest is fairly common in the literature (e. g., Polasik et al. A recurring theme in the literature is the question of which asset class Bitcoin belongs to, with many comparing it to gold and others to precious metals or speculative assets (see, in particular, Baur et al. (2017), or Bouri et al. (2017)). Some even categorize Bitcoin as somewhere between a currency and a commodity (see, for example, Dyhrberg (2016)). For other recent contributions, see Cheah et al. (2018); Khuntia and Pattanayak (2018); and Koutmos (2018).

The second body of literature attempts to model Bitcoin volatility. Among the first papers, Balcilar et al. (2017) analyze the causal relationship between trading volume and Bitcoin returns and volatility. They find that trading volume does not help predict Bitcoin return volatility. Dyhrberg (2016) investigates Bitcoin volatility using a GARCH model. The model estimated in Dyhrberg (2016) suggests that Bitcoin has some similarities to both gold and the dollar. Bouri et al. (2017) find no evidence of asymmetry in Bitcoin's conditional volatility when considering the period after December 2013 and investigate the relationship between the VIX index and Bitcoin volatility. Al-Khazali et al. (2018) consider a model of daily Bitcoin returns and show that Bitcoin volatility tends to decrease in response to positive news about the US economy. Finally, Katsiampa (2017) explores the applicability of several ARCH-type specifications to model Bitcoin volatility and selects the AR-CGARCH model as the preferred specification. Katsiampa (2017) suggests that Bitcoin volatility consists of long-term and short-term components, but does not investigate the determinants of Bitcoin volatility.

We use Engle et al.'s (2013) GARCH-MIDAS model to investigate the economic determinants of Bitcoin volatility over the long term. While all prior studies have examined Bitcoin returns/volatility and their potential determinants at the same (daily) frequency, the MIxed Data Sampling (MIDAS) methodology provides a unique framework to investigate macroeconomic and financial variables that are sampled at a lower (monthly) frequency than Bitcoin returns as potential drivers of Bitcoin volatility. Specifically, the two-component GARCH-MIDAS model consists of a short-term GARCH component and a long-term component. The model allows explanatory variables to enter directly into the specification of the long-term component.

We consider macroeconomic and financial variables such as the Baltic Dry Index and VIX as well as Bitcoin-specific variables such as trading volume as potential drivers of Bitcoin volatility. Furthermore, we analyze the drivers of volatility in the S& P 500, Nikkei 225, gold and copper. This allows us to compare the impact on different assets, providing further useful insights into the classification of Bitcoin as an asset class.

Our main findings are as follows: First, Bitcoin volatility is negatively related to US stock market volatility. This observation is consistent with investors considering Bitcoin as a safe haven. Second, in contrast to stock market volatility, Bitcoin volatility is procyclical, i. e., it rises when the level of global economic activity is high. Third, the response of Bitcoin volatility to rising US stock market volatility is opposite to that of gold volatility. This calls into question the meaningfulness of the comparison between Bitcoin and gold. Finally, while most prior studies focus on short-term relationships using only daily data, our results highlight the importance of also investigating long-term Bitcoin volatility and its relationship with economic drivers.

Section 2 introduces the GARCH-MIDAS model we apply to our setting. Section 3 describes the data. Empirical results are presented in Section 4. Section 5 concludes the paper.

We model Bitcoin volatility as a GARCH-MIDAS process. Engle et al. (2013) discuss the technical details of this class of models, where the conditional variance is multiplicatively decomposed into a short-term (high-frequency) component and a long-term (low-frequency) component. The long-term component is expressed as a function of observable explanatory variables. This allows us to investigate the financial and macroeconomic determinants of Bitcoin volatility. In the empirical application, we consider daily Bitcoin returns and monthly explanatory variables. We define the daily Bitcoin return as r i , t = 100 - ( ln ( P i , t - ln ( P i - 1 , t ) ) , where t = 1 ,... , T represents the monthly frequency and i = 1 ,... , N t represents the number of days in month t . We assume that the conditional mean of Bitcoin's return is constant, r i , t = μ + ε i , t , ε i , t = h i , t τ t Z i , t . h i , t and τ t represent the short-term and long-term components of the conditional variance, respectively. The short-term component h i , t varies with daily frequency and follows a unit variance GARCH(1, 1) process. h i , t = ( 1 - α - β ) + α ε i - 1 , t 2 τ t + β h i - 1 , t ,

where α > 0 , β ≥ 0 , α + β

τ t = m + ∑ k = 1 K φ k ( ω 1 , ω 2 ) X t - k ,

4.2. Bitcoin Specific Explanatory Variables

where X t represents the explanatory variables and φ k ( ω 1 , ω 2 ) represents a weighting scheme. We choose the beta weighting scheme, which is given by

5. Conclusions

φ k ( ω 1 , ω 2 ) = k / ( K + 1 ) ω 1 - 1 - k / ( K + 1 ) ω 2 - 1 ∑ j = 1 K j / ( K + 1 ) ω 1 - 1 - j / ( K + 1 ) ω 2 - 1 .

By construction, the weights φ k ( ω 1 , ω 2 ) ≥ 0 , k = 1 , ... , K , sum to 1. In the empirical application, we impose the constraint ω 1 = 1, which means that the weights are monotonically decreasing. Following Conrad and Loch (2015), we adopt three MIDAS lag years, i. e., we choose K = 36 for monthly explanatory variables. Our empirical results show that this choice is appropriate in the sense that the estimated weights approach zero before lag 36. Similar to Engle et al. (2013), we estimate GARCH-MIDAS models with quasi-maximum likelihood and construct heteroscedasticity- and autocorrelation-consistent (HAC) standard errors.

Our analysis utilizes cryptocurrency-specific data, indicators of financial conditions, and indicators of macroeconomic activity from May 2013 to December 2017. Data are collected from a number of sources, which are described in detail below.

We constructed monthly realized volatility for Bitcoin using daily squared returns. Bitcoin (BTC) trading volume by currency is the simple sum of all BTC traded in a given currency during a given period. However, it is worth noting that traders can trade in any currency of their choice regardless of their geographic location.

Author Contributions

Monthly realized volatility and daily returns for commodity ETFs, luxury goods index, S& P 500 and Nikkei 225, VIX index, and variance risk premium. For the luxury goods index, we use the S& P Global Luxury Index (Glux). This provides exposure to over 80 luxury brands across many countries. For commodities, we use SPDR Gold Shares ETF (GLD) and iPath Bloomberg Copper ETF (JJC).

Acknowledgments

Monthly realized volatility for the S& P500 is constructed using the daily realized variance R V a r i , t S P based on 5-minute intraday returns from the Oxford Mann Institute of Quantitative Finance. Using the daily realized volatility, we construct the annualized monthly realized volatility as R V o l t S P = 12 - ∑ i = 1 N t R V a r i , t S P . Monthly realized volatility for the Nikkei 225 is constructed similarly. The Chicago Board Options Exchange (Cboe) VIX index is calculated from a panel of option prices and is a "risk-neutral" implied volatility measure for the stock market. Often referred to as the "fear index," it is a measure of two-way volatility. The variance risk premium (V R P t ) is calculated as the difference between the squared VIX and the expected realized variance. Assuming that the realized variance is a random walk, this is a purely data-driven risk premium measure.

Conflicts of Interest

The BDI is an economic index published by the London-based Baltic Exchange and was first published in January 1985. The BDI is a composite of four different Baltic indices: Capesize, Handysize, Panamax, and Supermax. Each day, a panel submits current freight quotes for various routes. These freight rates are weighted by size to produce the BDI. The index covers a range of shipping companies that transport many goods, providing an assessment of the cost of raw materials by water. The BDI is often considered a good indicator of future economic growth and production.

References

As Bitcoin has been in the news, we follow Kristoufek (2015) and use data from Google Trends to see how this contributes to Bitcoin volatility. We use a monthly index constructed by Google Trends for all web searches and a monthly index for news searches only. Spikes in the index have coincided with major events, both positive and negative. Additionally, we were able to match large weekly fluctuations in the index to specific events throughout our sample period. Periods in our sample when Bitcoin did not experience any major events were associated with low index values and consistent interest. We therefore believe the Google Trends index is a fair proxy for large events, both positive and negative, that may impact Bitcoin volatility.
Table 1 provides summary statistics. Panel A provides descriptive statistics for Bitcoin returns and those of the S& P 500, Nikkei, gold, and copper. During the sample period, Bitcoin's average daily return was 0. 271%. This corresponds to an annualized return of about 68%, which is much higher than other assets (e. g., 11. 34% for the S& P 500). However, the minimum and maximum daily returns of Bitcoin are much more extreme than other assets. This is also reflected in the kurtosis of 11. 93 (5. 99 for the S& P 500). Note that Bitcoin trades seven days a week, whereas the other assets do not trade on weekends or bank holidays. Bitcoin's anomalous price history is depicted in Figure 1. The price movements in 2017 were especially dramatic, with Bitcoin prices increasing by 1318% from January 2017 to December 2017!
Monthly realized volatility (RV) is shown in Panel B. Clearly, Bitcoin’s realized volatility is by far the highest. Annualized average Bitcoin volatility is 73%, compared to 11% for the S& P 500. Figure 2 shows the time series of annualized monthly realized volatility. Bitcoin’s realized volatility far exceeds that of all other assets over the entire sample period. Specifically, 2017 was characterized by unusually low stock market volatility: in 2017, the Cboe volatility index, the VIX, fell to its lowest level in the past 23 years, and U. S. stock market realized volatility was the lowest since the mid-1990s. In contrast, Bitcoin volatility was elevated for most of the year.
Panels C and D provide summary statistics for macro/financial and Bitcoin-specific explanatory variables. All explanatory variables are standardized before estimation.
Table 2 shows the correlation between the volatility of different assets at the same time. There is a strong coexistence between S & amp; P500 and the Nikkei average volatility, and both RVs have a very strong correlation with the volatility of the hig h-end product index, but the Volatility of Bitcoin is the other assets. There is only a weak correlation with RV. The correlation at the same time is close to zero, but the correlation between R V OL T BIT and R V OL T-1 S P P P PP i s-0. 1236, and the correlation between R V-LT B IT and R V OL T-2 S P P P P P P P P P P P P P P P P P ps P P P P P P. This suggests that the lag of S & amp; p 500 volatility may be a useful prediction factor in future bitcoin volatility.
In the empirical analysis, explanatory variables are used at the level. This is because the sustainability of the explanatory variables is not very strong in the monthly frequency. For example, the primary sel f-correlation of Baltic Dry Index is 0. 79, and the US dollar transaction volume is 0. 48. Nevertheless, we estimated the GARCH-MIDAS model using the first floor difference of explanatory variables. All results were Robast for this correction.
This section analyzes the determination of the lon g-term volatility of Bitcoin. In general, when lon g-term fluctuation elements are explained, shor t-term fluctuation elements are often explained by the GARCH (1, 1) process. As a potential factor in Bitcoin's volatility, he will examine the US stock market volatility and risk, and the global economic activity. These indicators have shown an important driver in the US stock market (especially (English et al. 2013; Conrad and Loch 2015; And Conrad and Kleeen 2018)). Bouri et al. (2017) only finds weak evidence of the US stock market volatility and the volatility of Bitcoin. However, their analysis was based on daily data and focused on shor t-term effects. In contrast, the GARCH-MIDAS model can investigate whether the US stock market volatility affects long-term bitcoin volatility. For comparison, these indicators also indicate how they are related to S & amp; P 500, Nikkei Stock Average, gold and copper volatility 3.
As a benchmark model, we estimated a simple GARCH(1, 1) for Bitcoin returns. The parameter estimates are shown in the first row of Table 3. The mean constant and the two GARCH parameters are highly significant. The sum of the estimates of α and β slightly exceeds 1. Thus, the estimated GARCH model does not satisfy the condition of covariance stationarity. This result is likely due to the extreme fluctuations in Bitcoin volatility and suggests that a two-component model may be more appropriate. 4 We also estimated GJR-GARCH and found that Bouri et al.
The remainder of Table 3 shows the parameter estimates for the GARCH-MIDAS model. In these models, the estimates of α and β satisfy the condition of covariance stationarity. First, we use the realized volatility of the S& P500 as an explanatory variable for Bitcoin's long-term volatility. Interestingly, we find that R V o l t S P has a negative and significant effect on Bitcoin's long-term volatility. Since the estimated weighting scheme places a weight of 0. 09 on the first lag, our parameter estimates imply that a one standard deviation increase in R V o l t S P this month predicts a 17% decline in long-term Bitcoin volatility next month. The finding that R V o l t S P is negatively correlated with Bitcoin volatility contrasts with the usual findings in other markets. For comparison, Tables 4 and 5 show parameter estimates for the GARCH-MIDAS model applied to the S& P 500 and the Nikkei 225. As expected, higher levels of R V o l t S P are associated with higher long-term volatility of the S& P 500 and higher long-term volatility of the Nikkei 225. Second, we find that the VIX and RV-Glux are negatively correlated with long-term Bitcoin volatility. This result is not surprising, since both indices are positively correlated with R V o l t S P (see Table 2). Again, Tables 4 and 5 show the opposite effects for the two stock markets.
Third, Table 3 suggests that the VRP has a significantly positive impact on Bitcoin’s long-term volatility. A high VRP generally reflects a high overall level of risk aversion (Bekaert et al. We find a similar effect for the Nikkei Stock Average (see Table 5), but not for the S& P 500 (see Table 4).
Fourth, we find a strong positive correlation between the Baltic Dry Index and Bitcoin's long-term volatility. The finding that Bitcoin's volatility is pro-cyclical is noteworthy because it contrasts with the counter-cyclical behavior typically observed in financial volatility (see Schwert (1989); or Engle et al. (2013)).
According to the Akaike Information Criterion and the Bayesian Information Criterion, the favorable GARCH-MIDAS model for Bitcoin's volatility is based on the Baltic Dry Index (see Table 3). The left panel of Figure 3 shows the long- and short-term components estimated from this specification. Approximately 10% of the monthly conditional volatility fluctuations are due to the pro-cyclical behavior of Bitcoin. 65% can be explained by the movement of long-term volatility. For comparison, the right figure shows the long and short components of the model based on the volatility of the Luxury Goods Index. Clearly, from the comparison of the graphs, it can be confirmed that the Baltic Dry Index has a stronger power to explain Bitcoin volatility than RV-Glux.
Finally, Table 6 shows the GARCH-MIDAS estimates for gold and copper. In this table, we include only explanatory variables with significant estimates of θ. We can see that the GARCH persistence parameter β is high for both gold and copper in all models. Gold's long-term volatility is positively correlated with the realized volatility of the S& P 500, the VIX, and the realized volatility of the Luxury Goods Index. Interestingly, there is a strong negative relationship between copper's long-term volatility and the Baltic Dry Index. Higher global economic activity, combined with higher demand for copper, leads to higher copper prices and lower volatility.
In summary, it can be seen that lon g-term bitcoin volatility behavior is quite unusual. Unlike the two stock market volatility and gold/ copper volatility, Bitcoin's volatility decreases in accordance with the realization of the US stock market or the expected increase in volatility. In addition, the volatility of the stock market and the volatility of copper act ant i-cyclically, while bitcoin volatility appears to act strongly and circuly. This is an interesting result that distinguishes bitcoin not only from stocks but also from commodities and precious genus. Bitcoin is often compared to gol d-like precious metals (compared to the stock), because it has no essential value (compared to commodity). However, our results suggest that the link between Bitcoin's volatility and macro/ financial variables is very different from those variables and the link between stocks/ copper/ gold.
Next, examine the specific explanatory variable in bitcoin. Table 7 shows the parameter estimation value. As expected, it has been found that both Google Trends measured (all web search and monthly news search) are significantly positive to Bitcoin's volatility. Finally, we estimated two models, including the amount of bitcoin transactions of US dollars (US-TV) and Chinese people (CNY-TV). In each case, a significant negative effect on the transaction volume was found. We speculate that the increase in transactions predicts the decrease in bitcoin volatility as the level of "trust" or "confidence" for bitcoin as a payment system. Remember that BALCILAR et al. (2017) analyzes the consequences of transactions, bitcoin returns and volatility. They find that the amount of transactions is not useful for the volatility of Bitcoin Return. Therefore, it seems important to separate lon g-term components to find a significant pattern between volatility and transactions. < SPAN> In summary, it can be seen that lon g-term bitcoin volatility behavior is quite unusual. Unlike the two stock market volatility and gold/ copper volatility, Bitcoin's volatility decreases in accordance with the realization of the US stock market or the expected increase in volatility. In addition, the volatility of the stock market and the volatility of copper act ant i-cyclically, while bitcoin volatility appears to act strongly and circuly. This is an interesting result that distinguishes bitcoin not only from stocks but also from commodities and precious genus. Bitcoin is often compared to gol d-like precious metals (compared to the stock), because it has no essential value (compared to commodity). However, our results suggest that the link between Bitcoin's volatility and macro/ financial variables is very different from those variables and links between stocks/ copper/ gold.
Next, examine the specific explanatory variable in bitcoin. Table 7 shows the parameter estimation value. As expected, it has been found that both Google Trends measured (all web search and monthly news search) are significantly positive to Bitcoin's volatility. Finally, we estimated two models, including the amount of bitcoin transactions of US dollars (US-TV) and Chinese people (CNY-TV). In each case, a significant negative effect on the transaction volume was found. We speculate that the increase in transactions predicts the decrease in bitcoin volatility as the level of "trust" or "confidence" for bitcoin as a payment system. Remember that BALCILAR et al. (2017) analyzes the consequences of transactions, bitcoin returns and volatility. They find that the amount of transactions is not useful for the volatility of Bitcoin Return. Therefore, it seems important to separate lon g-term components to find a significant pattern between volatility and transactions. In summary, it can be seen that lon g-term bitcoin volatility behavior is quite unusual. Unlike the two stock market volatility and gold/ copper volatility, Bitcoin's volatility decreases in accordance with the realization of the US stock market or the expected increase in volatility. In addition, the volatility of the stock market and the volatility of copper act ant i-cyclically, while bitcoin volatility appears to act strongly and circuly. This is an interesting result that distinguishes bitcoin not only for stocks but also from commodity and precious genus. Bitcoin is often compared to gol d-like precious metals (compared to the stock), because it has no essential value (compared to commodity). However, our results suggest that the link between Bitcoin's volatility and macro/ financial variables is very different from those variables and the link between stocks/ copper/ gold.
Next, examine the specific explanatory variable in bitcoin. Table 7 shows the parameter estimation value. As expected, it has been found that both Google Trends measured (all web search and monthly news search) are significantly positive to Bitcoin's volatility. Finally, we estimated two models, including the amount of bitcoin transactions of US dollars (US-TV) and Chinese people (CNY-TV). In each case, a significant negative effect on the transaction volume was found. We speculate that the increase in transactions predicts the decrease in bitcoin volatility as the level of "trust" or "confidence" for bitcoin as a payment system. Remember that BALCILAR et al. (2017) analyzes the consequences of transactions, bitcoin returns and volatility. They find that the amount of transactions is not useful for the volatility of Bitcoin Return. Therefore, it seems important to separate lon g-term components to find a significant pattern between volatility and transactions.
Cryptocurrencies are a relatively unexplored area of research, and Bitcoin price fluctuations are still poorly understood. As cryptocurrencies seem to be gaining interest and legitimacy, especially with the establishment of a derivatives market, it is important to understand what drives the market movements. We explored what drives Bitcoin's long-term volatility. We found that the S& P500 realized volatility has a negative and highly significant effect on Bitcoin's long-term volatility, and the S& P500 volatility risk premium has a significant positive effect on Bitcoin's long-term volatility. In addition, we find a strong positive association between the Baltic Dry Index and Bitcoin's long-term volatility, and report that Bitcoin trading volume has a significant negative effect.
Notably, despite the general coverage on this topic, there are many series we considered, such as crime-related statistics, that do not really explain Bitcoin's volatility. We also experimented with the flight-to-safety index proposed by Engle et al. (2012) and found that long-term Bitcoin volatility tends to decrease during flight-to-safety periods. This result is consistent with our finding that there is a negative relationship between Bitcoin volatility and the risk of the U. S. stock market.
Our results suggest that Bitcoin volatility forecasts based on the GARCH-MIDAS model are better than those based on a simple GARCH model, and therefore can be used to construct improved time-varying portfolio weights, for example, when constructing a portfolio of Bitcoin and other assets such as stocks and bonds. Our results are also useful for pricing Bitcoin futures. Finally, the GARCH-MIDAS model can be used to simulate Bitcoin volatility under alternative scenarios for the development of the U. S. stock market and global economic activity. We look forward to sorting out these possibilities in future studies.
That said, we want to emphasize that all our results are based on a relatively short sample period. It will be interesting to see whether our results hold up for a longer sample or when the Bitcoin currency becomes more mature.
C. C., A. C., and E. G. contributed jointly to all sections of the paper. The authors jointly analyzed the data and wrote the paper.

Thanks to Christian Hafnner for inviting me to write under the theme of cryptocurrency. Peter Hansen and Steve Raymond gave a useful comment.

The author has stated that there is no conflict of interest.

AL-KHAZALI, OSAMAH, BOURI ELIE, and David Roubaud. 2018. THE IMPACT OF POSITIVE MACROECONOMIC NEWS SURPRISES: Gold vs. Bitcoin. ECONOMICS BULLETIN 38: 373-82. [Google School].

Balcilar, Mehmet, Elie Bouri, and David Roubaud. Economic modelling 64: 74-81. [Google School] [CrossRef].

BAUR, DIRK G., KIHOON HONG, and ADRIAN D. Lee. 561183 (Accessed on 25 APRIL 2018). .]

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