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How can a dynamic currency overlay strategy, using a forecasting model based on the RMB central parity rate policy introduced by the People’s Bank of China in 2015, help global investors manage currency risk in Chinese equity positions through long and short CNH positions? Michael Melvin discusses.

Image courtesy of interviewee. November 7, 2024

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This video discusses the currency forecasting mechanisms of the People's Bank of China (PBOC) and how they set the central parity rate for the Chinese Yuan (CNY) against the US dollar. Previously, the rate was set at the discretion of the PBOC without market signals, but the current system incorporates market forces based on the previous day’s closing rate and supply-demand conditions. This video change provides more predictability for investors.

The speaker, drawing from experience in both academia and industry, highlights that forecasting exchange rates is challenging. Historical models have often failed to outperform a simple random walk, which predicts that today's rate is the best estimator for the future rate. In their work, they introduce a forecasting model that uses the PBOC's morning rate to predict daily changes in the Yuan's value.

They report a success rate of about 67% in predicting the direction of change, which is significantly better than the random walk's 50% chance. This video model is particularly beneficial for investors holding Chinese equities, as it allows them to hedge currency risks dynamically. Unlike static hedging strategies, their model adapts based on directional forecasts, improving performance during market fluctuations.

Overall, the text reflects on the evolution of currency forecasting methods, including the impact of current technologies such as AI on model development and the shift toward data-driven approaches. The speaker emphasizes the importance of practical application of forecasting rather than just academic benchmarks, suggesting that understanding currency dynamics is vital for investment strategy.

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Here are five key points extracted from the video:

1. **People's Bank of China (PBOC) and Central Parity Rate**:
The People's Bank of China sets a daily central parity rate for the Chinese Yuan (CNY) against the US dollar based on the previous day's closing rate and current supply and demand conditions. This approach introduces a level of predictability to the market that was previously absent.

2. **Random Walk Theory in Currency Forecasting**:
The random walk theory suggests that predicting exchange rates is effectively as reliable as using today's rate as a forecast. Academic researchers have struggled to develop models that can outperform this baseline, leading to a general belief that future rates cannot be reliably predicted.

3. **Predictability of Currency Changes**:
The author’s forecasting model utilizes the PBOC's central parity rate to predict changes in the CNH (offshore Chinese Yuan) exchange rate. The model claims to outperform the random walk, achieving about a 67% success rate in predicting the direction of currency changes, making it useful for practitioners managing investments.

4. **Dynamic Currency Hedging Strategies**:
The video discusses the advantages of implementing dynamic hedging strategies compared to static hedges in currency risk management. The proposed dynamic hedge adjusts positions based on predicted currency movements, thereby improving performance compared to a static hedge.

5. **Challenges in Currency Forecasting and AI**:
Currency forecasting is described as a difficult field, with new variables and models constantly being proposed by academics. The advent of machine learning and AI tools has changed the landscape of data analysis in forecasting, enabling a more exploratory approach but raising concerns about the rigor of the methodologies behind such models.

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Here are some key quotes and points extracted from the video :

1. **Market Signals and Central Parity Rate:**
- "There were no market based signals. It was discretion on the part of the People's Bank of China as to where they would set a parity rate each day."
- "The people's bank would set a central parity rate before business hours begin... based on what they call the closing rate yesterday."

2. **Forecasting Challenges:**
- "All the forecasting models that people were using in the early seventies, none of them could outperform a random walk."
- "Today's exchange rate is the best you can do... that’s a very disappointing view, I think, of forecasting."

3. **Random Walk Hypothesis:**
- "The random walk has emerged as the benchmark for currency forecasting. It tells you just use today's forecast."
- "We have about a 67% success rate in predicting the direction of change [unlike the random walk which is 50/50]."

4. **Practitioner Perspective:**
- "If you're managing money you care about direction of change and suggested magnitude of that direction of change."
- "The forecasting model... takes the early morning People's Bank of China central parity rate as an indicator of where the exchange rate should move."

5. **Currency Hedging Strategies:**
- "We built an implementable model... combine your long position in Chinese equities with a currency hedge portfolio."
- "We can design a dynamic hedging strategy where you're short when your model forecasts currency depreciation, but you're long when the model forecasts currency appreciation."

6. **IMF and Currency Politics:**
- "I believe this was done in order for the IMF to curry favor with the Chinese authorities... it was politics."
- "The Chinese currency is a managed currency... it doesn't fit with the dollar, the Euro, the other currencies in the SDR."

7. **Dynamic vs Static Hedging:**
- "Dynamic hedge... gives much better performance than the static... hedge."
- "A currency overlay strategy... will add real value relative to a static hedge on your global equity positions."

8. **Future of Currency Forecasting:**
- "Currency forecasting is a very challenging business and the game for academics... is always this mean square error of the forecast error course race against the random walk."
- "Now, with machine learning models and AI tools, it’s just outright high powered data mining."

These quotes encapsulate key themes of market dynamics, the effectiveness of currency forecasting, and the challenges faced by both academics and practitioners in the field of currency predictions and risk management.

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Were no market based signals.

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It was, discretion on the part of the People's Bank

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of China as to where they would set a parody rate each day.

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And when they moved to this new system,

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they made an announcement

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that it would be based more on market forces.

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So, and that turned out as, as we showed it in our article,

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turned out to be something

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that we think was very useful for investors.

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The people's bank would set a,

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or does fill today, sets a central par

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so called central parity rate in the morning, really

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before business hours begin.

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And that rate is set based on

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what they call the closing rate yesterday.

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What was the value in the market of the exchange rate?

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The CNY Chinese Yuan against the US dollar

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at late afternoon the day before, and supply

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and demand conditions since then.

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And that's the nebulous part right there.

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Supply and demand conditions are undefined,

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but it does, it's suggestive that

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when banks are giving input

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to the people's bank in the early morning as to

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where they think the currency should be trading early in the

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morning, they're asked to use yesterday's co quote,

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closing rate and supply and demand conditions.

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So the commercial banks are supposed to look at

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where were we late yesterday afternoon?

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What's happened since then in order to come up with a rate

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that they submit to the central bank

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that the Central bank can then use to come up

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with their daily parity rate.

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So it adds a degree of predictability to the market

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that did not exist before.

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You mentioned Macy and Rogoff.

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This, Dick Macy was one

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of my former colleagues when I was at BlackRock years ago.

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So it's a friend of mine

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and he wrote an article in the early seventies

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with Ken Roff at Harvard that showed that

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all the forecasting models

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that people were using in the early seventies, none

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of them could outperform a random walk.

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What does that mean? It means a random walk means your best

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forecast of a future exchange rate is today's exchange rate.

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It's as likely to go up as it is to go down.

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So today's exchange rate is the best you can do.

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That's a very disappointing view, I think, of forecasting.

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And since that article, that has been the game

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for economists who forecast exchange rates to try

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and outperform the random walk.

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So the random walk has emerged as the benchmark

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for currency forecasting.

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So the random walk tells you just use today's forecast.

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And if you're forecasting, say 30 days ahead

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or 90 days ahead, you use today's

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Forecast as the best forecast of

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that future exchange rate.

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And then researchers would build all these forecasting

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models to try and outperform the random walk

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or outperform to research.

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Economists has traditionally been in terms

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of means square error of the forecast.

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So forecast being square error has been the horse race

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that people have run against the random walk.

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And that's fine for academic work,

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but that's not the way practitioners think in terms of

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forecasting for investing purposes.

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A practitioner wants to know which way

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the exchange rate's going.

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Is the currency going to go up in value?

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Is it gonna go down in value?

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So the practitioner wants direction of change

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and the mean forecast error, it can be small

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and it can be wrong every time on the direction,

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but it could be smaller than a random walk.

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And the, the researcher would say,

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I beat the random walk.

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I have a great forecasting model.

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So for us, what we wanted to do, you know,

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bef I was a professor for many years,

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then I went into industry for 11 years

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managing currency hedge funds,

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and now I'm back In academia, if you're managing money

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and you're investing in this market,

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you care about direction of change

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and suggested magnitude of that direction of change,

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you don't care about mean square forecast error.

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You want to be on the right side

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of the change in the exchange rate that is realized.

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So the forecasting model that we have in this paper takes

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that early morning, people's Bank

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of China central parity rate as an indicator of

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where the exchange rate should move

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today relative to yesterday.

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So we're forecasting the change in the CNH exchange rate.

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That's the onshore trade that you can do in Hong Kong.

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That's a market where people

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around the world can trade in the, so-called onshore market,

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you can't trade the, so-called CNY,

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the mainland Chinese currency onshore, you trade

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that currency offshore

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and hedge funds like I used to manage, they trade.

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So-called non-deliverable forward contracts on the CNH

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where you don't settle in any, rebi,

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you settle in US dollars.

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That's the, the market for offshore.

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So we're using the CNH, which is a market

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that everyone can trade in forecasting

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how it changes from yesterday to today,

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taking the morning's central parody rate from the central

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bank as indicative of where that should change.

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And we find it has significant predictability.

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We outperform the random, so the game

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of the academic economist,

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we outperform the random walk in terms of mean square error,

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But we also generate about a two thirds probability

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of getting the direction correct.

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Whereas the random walk, it's 50 50, it's likely

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to go up, it's down.

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We have about a 67% success rate in predicting the

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direction of change.

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So that makes it of use to practitioners.

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We take it from a perspective

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of an investor in Chinese equities.

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If I am based in the us which I am in California

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and I hold us, I have a portfolio of Chinese equities.

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I'm long the Chinese equities,

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I have bought Chinese equities,

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that means I'm implicitly long, rebi as well

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because Chinese equities are priced in the local currency.

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Well, when Chinese equities sell off,

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say there's a big drop in the market, we see

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that generally the currency also sells off at the same time

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and you get a big drop in the currency market.

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So exploiting that fact for risk,

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you mentioned risk management purposes.

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If you combine your long position in Chinese equities

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with a currency hedge portfolio,

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then when the market sells off, you are

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winning from your hedge portfolio.

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So generally international investors hedge typically

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around 50% of the exposure

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that they have in a currency from their international

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equity investments.

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So if I've got say one US dollar worth of Chinese equities

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and I short 50 cents US worth of Chinese currency,

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then that hedge will give me a good performance when the

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currency market's selling off in China when the equity

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market's selling off.

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So that helps protect me.

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On the downside, we built an implementable model, something

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that you could actually trade.

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Typical currency hedging is a so-called static hedge

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where you just always,

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you're always short Chinese currency if you're long Chinese

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equities, but you, as we did,

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we can design a dynamic hedging strategy

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where you're short when your model forecast

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currency depreciation, but you're long when the model

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forecast currency appreciation.

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And we found that

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that gives much better performance than the static.

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So-called static hedge, I believe,

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and I have no visibility into the internal politics

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of the IMF,

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but I believe this was done in order for the IMF

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to curry favor with the Chinese authorities

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because the currencies that made up the SDR,

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if you look at the rules, the IMF rules, they have

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to be fully convertible currencies, fully trade, easily,

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implementable and strategies

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where you can freely trade them.

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You can't do that with the Chinese currency.

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It is not a freely tradable currency.

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It's a managed currency.

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And there are restrictions on foreigners holding

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and trading the currency.

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So it doesn't fit with the dollar, the Euro,

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the British pound and the Japanese Yen,

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the other currencies in the SDR,

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that's why I believe it was a political move had no

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importance in terms of how you would trade, the r and b.

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It was, politics.

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We actually had several different strategies

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that we investigated.

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We found when international investors can hedge their

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currency portfolio specifically by shorting the currency,

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as I said, and we built that strategy,

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or they could have a dynamic hedge where they vary

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the size and sign of their hedge.

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Sometimes they're long, sometimes they're short,

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but they time it with the signals, trading signals

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that come from our model.

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Or they can use what's known as a,

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currency overlay strategy.

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And this is very popular

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for currency hedge fund managers like,

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like I was when I was at, BGI,

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Barclays Global Investors in BlackRock.

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So there, a big asset owner

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who has global equity positions will buy a currency

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overlay strategy, which is an active

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currency investing strategy that runs in parallel

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with the global equity portfolio.

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So the thinking is the correlations are very low generally,

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but the value added from going long

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or short currencies will add real value relative

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to a static hedge on your global equity positions.

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And we show in our paper that that does add the most value

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because it's the less the least constrained model

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and it lets the investor take long

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and short positions vary the size

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of those positions over time.

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And that, increased flexibility in the investment

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strategy pays off in the sample period

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that we used when we wrote the paper Future research

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on currency forecasting.

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It's, it's generally the way this has gone over time

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is a researcher will come up with a new variable.

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For instance, we should look at this variable.

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It will be useful in forecasting currencies.

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And if you look at the literature over time,

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you'll see this sequence of papers that are written

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that say, we beat the random wall.

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But when you then look at those signals over a longer period

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of time, they don't hold up.

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Currency forecasting is a very challenging business

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and the game for academics again,

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is always this mean square error

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of the forecast error course race against the random walk.

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And so I suspect we'll see researchers in the future doing

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the same thing, coming up with new ways of forecasting.

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And of course in the modern era right now,

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people are using all sorts of

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AI tools to generate new models for forecasting.

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Where, it's funny

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because when I was in the industry quantitative investing as

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as I did, I led quant investment teams, at BlackRock

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and BGI, there was a rule, no data mining.

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You know, you've gotta have a story.

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Here's why I think this works.

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And then you test it with the data,

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but you can't just get a bunch of variables

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and see if they work and not understand why.

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Well, where we've gone now with machine learning models

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and AI tools, it's just outright high powered data mining.

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And so we've seen this 300,

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I'd say 180 degree swing in terms of

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how people think about data mining.

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If basically it's, if it works, it works

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and we'll figure out maybe why it works if we see

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that we've got something that can actually predict.

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So there are all sorts

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of things being done these days in terms of forecasting

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would just have a huge menu of variables

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and let the model sort it out, what seems to work

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and then we'll tell our stories about why it works.

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Perhaps if we can figure that out.

Market Forces and Currency Parity: Analyzing the People's Bank of China's Approach

00:00:00 - Were no market based signals. It was, discretion on the part of the People's Bank of China as to where they would set a parody rate each day. And when they moved to this new system, they made an announcement that it would be based more on market forces. So, and that turned out as, as we showed it in our article, turned out to be something that we think was very useful for investors. The people's bank would set a, or does fill today, sets a central par

00:00:34 - before business hours begin. And that rate is set based on what they call the closing rate yesterday. What was the value in the market of the exchange rate? The CNY Chinese Yuan against the US dollar at late afternoon the day before, and supply and demand conditions since then. And that's the nebulous part right there. Supply and demand conditions are undefined,

00:01:08 - when banks are giving input to the people's bank in the early morning as to where they think the currency should be trading early in the morning, they're asked to use yesterday's co quote, closing rate and supply and demand conditions. So the commercial banks are supposed to look at where were we late yesterday afternoon? What's happened since then in order to come up with a rate that they submit to the central bank

Currency Forecasting: The Random Walk Challenge

00:01:40 - with their daily parity rate. So it adds a degree of predictability to the market that did not exist before. You mentioned Macy and Rogoff. This, Dick Macy was one of my former colleagues when I was at BlackRock years ago. So it's a friend of mine and he wrote an article in the early seventies with Ken Roff at Harvard that showed that all the forecasting models that people were using in the early seventies, none of them could outperform a random walk.

00:02:15 - forecast of a future exchange rate is today's exchange rate. It's as likely to go up as it is to go down. So today's exchange rate is the best you can do. That's a very disappointing view, I think, of forecasting. And since that article, that has been the game for economists who forecast exchange rates to try

00:02:43 - So the random walk has emerged as the benchmark for currency forecasting. So the random walk tells you just use today's forecast. And if you're forecasting, say 30 days ahead or 90 days ahead, you use today's Forecast as the best forecast of that future exchange rate. And then researchers would build all these forecasting models to try and outperform the random walk or outperform to research.

Forecasting Currency Movements: Practitioners vs. Academics

00:03:14 - of means square error of the forecast. So forecast being square error has been the horse race that people have run against the random walk. And that's fine for academic work, but that's not the way practitioners think in terms of forecasting for investing purposes. A practitioner wants to know which way the exchange rate's going. Is the currency going to go up in value? Is it gonna go down in value?

00:03:46 - and the mean forecast error, it can be small and it can be wrong every time on the direction, but it could be smaller than a random walk. And the, the researcher would say, I beat the random walk. I have a great forecasting model. So for us, what we wanted to do, you know, bef I was a professor for many years, then I went into industry for 11 years managing currency hedge funds, and now I'm back In academia, if you're managing money

00:04:17 - you care about direction of change and suggested magnitude of that direction of change, you don't care about mean square forecast error. You want to be on the right side of the change in the exchange rate that is realized. So the forecasting model that we have in this paper takes that early morning, people's Bank of China central parity rate as an indicator of where the exchange rate should move

Forecasting CNH: Predictability and Market Implications

00:04:49 - So we're forecasting the change in the CNH exchange rate. That's the onshore trade that you can do in Hong Kong. That's a market where people around the world can trade in the, so-called onshore market, you can't trade the, so-called CNY, the mainland Chinese currency onshore, you trade that currency offshore and hedge funds like I used to manage, they trade.

00:05:21 - where you don't settle in any, rebi, you settle in US dollars. That's the, the market for offshore. So we're using the CNH, which is a market that everyone can trade in forecasting how it changes from yesterday to today, taking the morning's central parody rate from the central bank as indicative of where that should change. And we find it has significant predictability.

00:05:50 - of the academic economist, we outperform the random walk in terms of mean square error, But we also generate about a two thirds probability of getting the direction correct. Whereas the random walk, it's 50 50, it's likely to go up, it's down. We have about a 67% success rate in predicting the direction of change. So that makes it of use to practitioners. We take it from a perspective

Hedging Strategies for Chinese Equity Investments

00:06:21 - If I am based in the us which I am in California and I hold us, I have a portfolio of Chinese equities. I'm long the Chinese equities, I have bought Chinese equities, that means I'm implicitly long, rebi as well because Chinese equities are priced in the local currency. Well, when Chinese equities sell off, say there's a big drop in the market, we see that generally the currency also sells off at the same time

00:06:54 - So exploiting that fact for risk, you mentioned risk management purposes. If you combine your long position in Chinese equities with a currency hedge portfolio, then when the market sells off, you are winning from your hedge portfolio. So generally international investors hedge typically around 50% of the exposure that they have in a currency from their international equity investments.

00:07:25 - and I short 50 cents US worth of Chinese currency, then that hedge will give me a good performance when the currency market's selling off in China when the equity market's selling off. So that helps protect me. On the downside, we built an implementable model, something that you could actually trade. Typical currency hedging is a so-called static hedge where you just always, you're always short Chinese currency if you're long Chinese

Dynamic Hedging Strategies and Currency Politics

00:07:57 - we can design a dynamic hedging strategy where you're short when your model forecast currency depreciation, but you're long when the model forecast currency appreciation. And we found that that gives much better performance than the static. So-called static hedge, I believe, and I have no visibility into the internal politics of the IMF, but I believe this was done in order for the IMF to curry favor with the Chinese authorities

00:08:34 - if you look at the rules, the IMF rules, they have to be fully convertible currencies, fully trade, easily, implementable and strategies where you can freely trade them. You can't do that with the Chinese currency. It is not a freely tradable currency. It's a managed currency. And there are restrictions on foreigners holding and trading the currency. So it doesn't fit with the dollar, the Euro,

00:09:01 - the other currencies in the SDR, that's why I believe it was a political move had no importance in terms of how you would trade, the r and b. It was, politics. We actually had several different strategies that we investigated. We found when international investors can hedge their currency portfolio specifically by shorting the currency, as I said, and we built that strategy, or they could have a dynamic hedge where they vary

Dynamic Currency Strategies for Enhanced Portfolio Value

00:09:36 - Sometimes they're long, sometimes they're short, but they time it with the signals, trading signals that come from our model. Or they can use what's known as a, currency overlay strategy. And this is very popular for currency hedge fund managers like, like I was when I was at, BGI, Barclays Global Investors in BlackRock. So there, a big asset owner who has global equity positions will buy a currency

00:10:09 - currency investing strategy that runs in parallel with the global equity portfolio. So the thinking is the correlations are very low generally, but the value added from going long or short currencies will add real value relative to a static hedge on your global equity positions. And we show in our paper that that does add the most value because it's the less the least constrained model

00:10:41 - and short positions vary the size of those positions over time. And that, increased flexibility in the investment strategy pays off in the sample period that we used when we wrote the paper Future research on currency forecasting. It's, it's generally the way this has gone over time is a researcher will come up with a new variable. For instance, we should look at this variable.

Challenges and Evolution in Currency Forecasting

00:11:12 - And if you look at the literature over time, you'll see this sequence of papers that are written that say, we beat the random wall. But when you then look at those signals over a longer period of time, they don't hold up. Currency forecasting is a very challenging business and the game for academics again, is always this mean square error of the forecast error course race against the random walk.

00:11:44 - the same thing, coming up with new ways of forecasting. And of course in the modern era right now, people are using all sorts of AI tools to generate new models for forecasting. Where, it's funny because when I was in the industry quantitative investing as as I did, I led quant investment teams, at BlackRock and BGI, there was a rule, no data mining. You know, you've gotta have a story.

00:12:13 - And then you test it with the data, but you can't just get a bunch of variables and see if they work and not understand why. Well, where we've gone now with machine learning models and AI tools, it's just outright high powered data mining. And so we've seen this 300, I'd say 180 degree swing in terms of how people think about data mining. If basically it's, if it works, it works and we'll figure out maybe why it works if we see that we've got something that can actually predict.

Evolution of Forecasting: From Rigid Models to Data-Driven Insights

00:12:57 - of things being done these days in terms of forecasting would just have a huge menu of variables and let the model sort it out, what seems to work and then we'll tell our stories about why it works. Perhaps if we can figure that out.

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