In Forex Trading, hedging is a tactic to safeguard your position in a currency pair from unexpected shifts. It’s a short-term move often used when there’s concern about news or an event causing turbulence in currency markets.
When it comes to hedging forex pairs, there are three main strategies followed:
1. Direct hedging Strategy:
A direct hedging strategy in Forex involves simultaneously initiating both a buy and sell position for the same currency pair, providing a means to mitigate potential risks by offsetting market exposure. This approach allows traders to navigate market fluctuations more effectively through a balanced position in the selected currency pair.
2. Correlation hedging strategy:
Utilizing the correlation between currency pairs is a widely adopted Forex hedging strategy, allowing traders to navigate market volatility by simultaneously trading highly positively or negatively correlated pairs for effective risk mitigation.
3. Options Hedging Strategy:
Engaging in options trading serves as an alternative method to hedge trading risks, offering a less intricate approach compared to concurrently managing long and short positions in specific currency pairs. Options, as contractual instruments, grant traders the right, without obligation, to buy or sell currency at a predetermined rate on or before a specified future date, typically characterized by expiration dates.
Here, we’re diving into the Correlation hedging strategy. The approach involves selecting the optimal basket (commonly used to denote pairs of currency pairs) by assessing the correlation between them. Our focus is on identifying pairs that exhibit a notably positive correlation — where movements positively impact each other — hence opening opposite positions for the currency pairs in the basket. Subsequently, employing the hedging ratio, we’ll analyze the most effective way to construct our hedging portfolio.
You might be wondering about the concept of the hedging ratio. Let me break it down for you.
Hedging Ratio
The hedge ratio is essentially the proportion or comparative value of a hedge in relation to the overall position. It serves as a crucial risk management metric, providing insights into the potential risk associated with movements in the hedging instrument.
Another significant factor in hedging is the optimal hedge ratio, also known as the minimum variance hedge ratio. This ratio plays a crucial role in investment risk management, indicating the percentage of a hedging instrument — be it an asset or liability — that an investor should use for effective hedging. The optimal hedge ratio is particularly relevant in cross-hedging scenarios, where there might not be a perfect match between the hedging instrument and the asset being hedged. It helps minimize variance and optimize risk management strategies in the dynamic landscape of financial markets.
Where:
ρ = The correlation coefficient of the changes in the spot and futures prices
σs = Standard deviation of changes in the spot price ‘s’
σp = Standard deviation of changes in the futures price ‘f’
Expanding on this concept to suit our needs, we apply a formula for the hedging factor in a pair of currency pairs X and Y:
This formula helps us determine the hedging factor, offering insights into the relationship between the two currency pairs and aiding in the construction of an effective hedging portfolio.
Now, in the process of selecting our optimal basket of currency pairs, we’ll start by creating a correlation heatmap. This visual representation allows us to identify pairs with the most negative correlation, laying the foundation for our hedging strategy.
Correlation heatmap for Month Data
correlation_matrix_month = df_month.corr()# Create a heatmap using seaborn
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix_month, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5)
plt.title('Correlation Matrix of Forex Codes')
plt.show()
Correlation heatmap for Day Data
correlation_matrix_day = df_day.corr()# Create the day Data heatmap using seaborn
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix_day, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5)
plt.title('Correlation Matrix of Forex Codes')
plt.show()
Looking at the data, we’ve identified two separate groups of currency pairs — one for daily data for a month and another for hourly data. This highlights the inherent volatility of the forex market, where the relationships between currency pairs constantly shift, impacting our hedging strategies in different ways.
Now let’s identify the most positively correlated pairs for month and day data:
For Monthly data:
stacked_corr = correlation_matrix_month.stack()
filtered_corr = stacked_corr[stacked_corr < 1]
sorted_corr = filtered_corr.sort_values(ascending=False)
sorted_corr
Hence through Monthly Data analysis, we choose NZDUSD and CHFUSD as our basket with a correlation equal to 0.97 (most positively correlated basket)
For Day data:
stacked_corr_day = correlation_matrix_day.stack()
filtered_corr_day = stacked_corr_day[stacked_corr_day < 1]
sorted_corr_day = filtered_corr_day.sort_values(ascending=False)
sorted_corr_day
Hence through Day Data analysis, we choose SEKUSD and EURUSD as our basket with a correlation equal to 0.99 (most positively correlated basket)
Now that we have identified our basket of currency pairs for both daily and hourly data, let’s enhance our understanding by creating visualizations.
Now, to visualize the daily return values, we need to calculate the daily returns for each individual currency pair as follows:
# Daily Returns for Month Datadf_month_CHFUSD = tm.timeseries(currency='CHFUSD', start="2023-11-10", end="2023-12-10", interval="daily", fields=["open", "high", "low", "close"]) # Fetching Month Data for CHFUSD
df_month_CHFUSD['Daily_Return'] = ((df_month_CHFUSD['close'] - df_month_CHFUSD['open']) / df_month_CHFUSD['open']) * 100 # Creating Daily Return Value for CHFUSD
df_month_NZDUSD = tm.timeseries(currency='NZDUSD', start="2023-11-10", end="2023-12-10", interval="daily", fields=["open", "high", "low", "close"]) # Fetching Month Data for NZDUSD
df_month_NZDUSD['Daily_Return'] = ((df_month_NZDUSD['close'] - df_month_NZDUSD['open']) / df_month_NZDUSD['open']) * 100 # Creating Daily Return Value for NZDUSD
# Daily Returns for Day Data
df_day_SEKUSD = tm.timeseries(currency='SEKUSD', start="2023-12-13-00:00", end="2023-12-14-00:00", interval="hourly", fields=["open", "high", "low", "close"]) # Fetching day Data for SEKUSD
df_day_SEKUSD['Daily_Return'] = ((df_day_SEKUSD['close'] - df_day_SEKUSD['open']) / df_day_SEKUSD['open']) * 100 # Creating hourly Return Value for SEKUSD
df_day_EURUSD = tm.timeseries(currency='EURUSD', start="2023-12-13-00:00", end="2023-12-14-00:00", interval="hourly", fields=["open", "high", "low", "close"]) # Fetching day Data for EURUSD
df_day_EURUSD['Daily_Return'] = ((df_day_EURUSD['close'] - df_day_EURUSD['open']) / df_day_EURUSD['open']) * 100 # Creating hourly Return Value for EURUSD
df_day_CHFUSD = tm.timeseries(currency='CHFUSD', start="2023-12-13-00:00", end="2023-12-14-00:00", interval="hourly", fields=["open", "high", "low", "close"]) # Fetching day Data for CHFUSD
df_day_CHFUSD['Daily_Return'] = ((df_day_CHFUSD['close'] - df_day_CHFUSD['open']) / df_day_CHFUSD['open']) * 100 # Creating hourly Return Value for CHFUSD
df_day_NZDUSD = tm.timeseries(currency='NZDUSD', start="2023-12-13-00:00", end="2023-12-14-00:00", interval="hourly", fields=["open", "high", "low", "close"]) # Fetching day Data for NZDUSD
df_day_NZDUSD['Daily_Return'] = ((df_day_NZDUSD['close'] - df_day_NZDUSD['open']) / df_day_NZDUSD['open']) * 100 # Creating hourly Return Value for NZDUSD
1. Comparison of Daily Returns of CHFUSD & NZDUSD on a monthly timeframe
plt.figure(figsize=(10, 6))
plt.style.use('ggplot')plt.plot(df_month_CHFUSD['date'], df_month_CHFUSD['Daily_Return'], label='CHFUSD', marker='o')
plt.plot(df_month_CHFUSD['date'], df_month_NZDUSD['Daily_Return'], label='NZDUSD', marker='o')
plt.title('CHFUSD/NZDUSD Returns Comparison')
plt.xlabel('Date')
plt.ylabel('Returns')
plt.legend()
plt.xticks(rotation = 50)
plt.grid(True)
plt.show()Image by Author
Examining the visual representation, a clear pattern emerges: when one currency pair experiences an upward movement, the other tends to move in the opposite direction, underscoring our negative correlation. This observable inverse relationship is crucial for our hedging strategy, as movements in one pair act as a counterbalance to the other within our selected basket. Similarly, let’s visualize the data for the 1-day timeframe.
2. Comparison of Hourly Returns of SEKUSD & EURUSD on a 1-day timeframe
plt.figure(figsize=(10, 6))
plt.style.use('ggplot')plt.plot(df_day_SEKUSD['date'], df_day_SEKUSD['Daily_Return'], label='SEKUSD', marker='o')
plt.plot(df_day_SEKUSD['date'], df_day_EURUSD['Daily_Return'], label='EURUSD', marker='o')
plt.title('SEKUSD/EURUSD Returns Comparison')
plt.xlabel('Hour')
plt.ylabel('Returns')
plt.legend()
plt.xticks(rotation = 50)
plt.grid(True)
plt.show()
3. Comparison of Hourly Returns of CHFUSD & NZDUSD on a 1-day timeframe
plt.figure(figsize=(10, 6))
plt.style.use('ggplot')plt.plot(df_day_CHFUSD['date'], df_day_CHFUSD['Daily_Return'], label='CHFUSD', marker='o')
plt.plot(df_day_CHFUSD['date'], df_day_NZDUSD['Daily_Return'], label='NZDUSD', marker='o')
plt.title('CHFUSD/NZDUSD Returns Comparison')
plt.xlabel('Hour')
plt.ylabel('Returns')
plt.legend()
plt.xticks(rotation = 50)
plt.grid(True)
plt.show()
Comparing the visualizations of the hourly data for CHFUSD & NZDUSD and SEKUSD & EURUSD, it’s apparent that they share a high degree of similarity. However, based on the correlation data, a more significant positive correlation is between SEKUSD & EURUSD at the hourly level. Therefore, we will opt for SEKUSD & EURUSD for our hourly analysis.
In the initiation of forming hedging profiles, a pivotal metric comes into play — the hedge ratio. This metric not only illuminates the trend that the hedge is following but also provides a critical ratio guiding our investment decisions. The hedge ratio acts as a quantitative indicator, offering insights into the optimal allocation for effective risk management. By understanding and leveraging this metric, traders can make informed decisions on how to allocate their investments in alignment with market trends and potential risks.
Now, to compute hedge ratios, we’ll need to calculate both the correlation and standard deviation. Let’s streamline the process by combining these calculations into a single function, as outlined below:
Hedge Ratio Function
def calculate_hedging_ratio(df1, df2):# Merge the two DataFrames on the 'date' column
merged_df = pd.merge(df1[['date', 'Daily_Return']], df2[['date', 'Daily_Return']], on='date', suffixes=('_1', '_2'))
# Calculate the correlation coefficient between the returns of the two currency pairs
correlation = merged_df['Daily_Return_1'].corr(merged_df['Daily_Return_2'])
# Calculate the hedging ratio
hedging_ratio = - correlation * (df1['Daily_Return'].std() / df2['Daily_Return'].std())
print(f"Correlation between the two currency pairs: {correlation}")
print(f"Hedging Ratio: {hedging_ratio}")
#Calling the function to get the results
calculate_hedging_ratio(df_month_CHFUSD,df_month_NZDUSD) #for daily data
calculate_hedging_ratio(df_day_SEKUSD,df_day_EURUSD) #for hourly data
The results obtained for the ‘calculate_hedging_ratio’ function are as follows:
1. Hourly data:
