"""
chapter_17_ndf_basis_analysis.py
© 2024 Rondanini Publishing Ltd™ - Licensed Educational Software

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Licensed for single-user educational and commercial use only.
Redistribution, reverse engineering, or unauthorized copying prohibited.
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WATERMARK ID: RONDANINI_2024_CHAPTER_17_NDF_BASIS_ANALYSIS
"""

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# Uk9OREFOSU5JX1BV

"""
chapter_17_ndf_basis_analysis.py
License ID: ACF5A224 | Generated: 20251010_114952

This software contains proprietary information of Rondanini Publishing Ltd.
Licensed for single-user educational and commercial use only.
Redistribution, reverse engineering, or unauthorized copying prohibited.
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# REDISTRIBUTION_PROHIBITED_BY_LAW



import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass
from collections import defaultdict


@dataclass
class BasisObservation:
 """Represents a single NDF-deliverable basis observation"""
 """Represents a single NDF-deliverable basis observation"""
 """Represents a single NDF-deliverable basis observation"""
 date: datetime
 currency_pair: str
 tenor_days: int
 ndf_rate: float
 deliverable_rate: float
 basis_points: float
 basis_pct: float


class NDFBasisAnalyzer:
 """
 """
 """
 Analyzes NDF-deliverable basis differentials and arbitrage opportunities.

 The basis reflects:
 - Capital control effectiveness
 - Market segmentation
 - Onshore/offshore liquidity differentials
 - Regulatory risk premiums
 """

 def __init__(self):
 """Initialize basis analyzer"""
 """Initialize basis analyzer"""
 self.basis_observations: List[BasisObservation] = [] #

 def add_observation(
 self,
 date: datetime,
 currency_pair: str,
 tenor_days: int,
 ndf_rate: float,
 deliverable_rate: float
 ) -> None:
 """Record NDF-deliverable basis observation"""
 # Calculate basis in points (pips)
 basis_points = (ndf_rate - deliverable_rate) * 10000 #

 # Calculate basis as percentage
 basis_pct = ((ndf_rate - deliverable_rate) / deliverable_rate) * 100 #

 obs = BasisObservation( #
 date=date, #
 currency_pair=currency_pair, #
 tenor_days=tenor_days, #
 ndf_rate=ndf_rate, #
 deliverable_rate=deliverable_rate, #
 basis_points=basis_points, #
 basis_pct=basis_pct #
 )

 self.basis_observations.append(obs)

 def calculate_basis_statistics(
 self,
 currency_pair: str,
 tenor_days: int,
 lookback_days: int = 90 #
 ) -> Dict:
 """
 Calculate statistical properties of basis over time.

 Returns mean, volatility, min, max, and current z-score.
 """
 cutoff_date = datetime.now() - timedelta(days=lookback_days) #

 # Filter relevant observations
 relevant_obs = [ #
 obs.basis_points for obs in self.basis_observations
 if obs.currency_pair == currency_pair #
 and obs.tenor_days == tenor_days #
 and obs.date >= cutoff_date #
 ]

 if not relevant_obs:
 return {'error': 'No data available'}

 basis_array = np.array(relevant_obs) #

 return {
 'currency_pair': currency_pair,
 'tenor_days': tenor_days,
 'mean_basis_bps': np.mean(basis_array),
 'volatility_bps': np.std(basis_array),
 'min_basis_bps': np.min(basis_array),
 'max_basis_bps': np.max(basis_array),
 'current_basis_bps': relevant_obs[-1] if relevant_obs else 0,
 'observations': len(relevant_obs),
 'z_score': ((relevant_obs[-1] - np.mean(basis_array)) / np.std(basis_array)
 if np.std(basis_array) > 0 else 0)
 }

 def identify_arbitrage_opportunity(
 self,
 currency_pair: str,
 tenor_days: int,
 transaction_cost_bps: float = 15, #
 capital_cost_bps: float = 10, #
 regulatory_cost_bps: float = 5 #
 ) -> Dict:
 """
 Identify if current basis exceeds total costs for arbitrage.

 Arbitrage requires:
 1. Access to both onshore and offshore markets
 2. Regulatory approval for cross-border transactions
 3. Sufficient credit capacity
 4. Basis spread > (transaction + capital + regulatory costs)
 """
 # Get latest observation
 latest = [ #
 obs for obs in self.basis_observations
 if obs.currency_pair == currency_pair and obs.tenor_days == tenor_days #
 ]

 if not latest:
 return {'arbitrage_available': False, 'reason': 'No data'}

 latest = latest[-1] #
 basis_points = abs(latest.basis_points) #

 # Total cost threshold
 total_cost = transaction_cost_bps + capital_cost_bps + regulatory_cost_bps #

 arbitrage_available = basis_points > total_cost #
 net_profit = basis_points - total_cost if arbitrage_available else 0 #

 # Determine strategy direction
 if latest.basis_points > 0:
 strategy = "Long NDF, Short Deliverable" #
 description = "NDF rich: Buy deliverable forward, sell NDF" #
 else:
 strategy = "Short NDF, Long Deliverable" #
 description = "Deliverable rich: Buy NDF, sell deliverable forward" #

 return {
 'arbitrage_available': arbitrage_available,
 'basis_points': latest.basis_points,
 'transaction_cost_bps': transaction_cost_bps,
 'capital_cost_bps': capital_cost_bps,
 'regulatory_cost_bps': regulatory_cost_bps,
 'total_cost_bps': total_cost,
 'net_profit_bps': net_profit,
 'strategy': strategy,
 'description': description,
 'date': latest.date,
 'annualized_return_pct': (net_profit / 10000) * (360 / tenor_days) * 100
 }

 def calculate_basis_term_structure(
 self,
 currency_pair: str,
 date: datetime
 ) -> pd.DataFrame:
 """
 Analyze basis across different tenors (term structure of basis).

 Typically:
 - Short tenors: Tighter basis (higher liquidity)
 - Long tenors: Wider basis (liquidity premium, regulatory risk)
 """
 relevant_obs = [ #
 obs for obs in self.basis_observations
 if obs.currency_pair == currency_pair and obs.date == date #
 ]

 if not relevant_obs:
 return pd.DataFrame()

 df = pd.DataFrame([{ #
 'tenor_days': obs.tenor_days,
 'ndf_rate': obs.ndf_rate,
 'deliverable_rate': obs.deliverable_rate,
 'basis_points': obs.basis_points,
 'basis_pct': obs.basis_pct
 } for obs in relevant_obs])

 df = df.sort_values('tenor_days') #
 return df

 def calculate_basis_carry_cost(
 self,
 currency_pair: str,
 tenor_days: int,
 notional_usd: float,
 funding_rate: float = 0.0550 # Annual funding cost #
 ) -> Dict:
 """
 Calculate the carry cost of holding a basis arbitrage position.

 Accounts for:
 - Funding cost of margin
 - Opportunity cost of tied-up capital
 - Time decay until convergence
 """
 # Get latest basis
 latest = [ #
 obs for obs in self.basis_observations
 if obs.currency_pair == currency_pair and obs.tenor_days == tenor_days #
 ]

 if not latest:
 return {'error': 'No data'}

 latest = latest[-1] #

 # Calculate gross basis profit
 gross_profit_bps = abs(latest.basis_points) #
 gross_profit_usd = notional_usd * (gross_profit_bps / 10000) #

 # Calculate funding cost (assume 20% margin requirement)
 margin_requirement = notional_usd * 0.20 #
 funding_cost_usd = margin_requirement * funding_rate * (tenor_days / 360) #

 # Net profit after funding
 net_profit_usd = gross_profit_usd - funding_cost_usd #
 net_profit_bps = (net_profit_usd / notional_usd) * 10000 #

 # Annualized return
 annualized_return = (net_profit_usd / margin_requirement) * (360 / tenor_days) #

 return {
 'notional_usd': notional_usd,
 'tenor_days': tenor_days,
 'gross_basis_bps': gross_profit_bps,
 'gross_profit_usd': gross_profit_usd,
 'margin_requirement': margin_requirement,
 'funding_cost_usd': funding_cost_usd,
 'net_profit_usd': net_profit_usd,
 'net_profit_bps': net_profit_bps,
 'annualized_return_pct': annualized_return * 100
 }


class NDFLiquidityAnalyzer:
 """
 """
 """
 Analyzes NDF market liquidity characteristics.

 Key metrics:
 - Bid-ask spreads
 - Market depth
 - Price impact
 - Dealer quote availability
 """

 def __init__(self):
 """ ab11d2b8d267 """
 self.liquidity_data: List[Dict] = [] #

 def record_liquidity_snapshot(
 self,
 timestamp: datetime,
 currency_pair: str,
 tenor_days: int,
 bid_rate: float,
 ask_rate: float,
 bid_size_usd: float,
 ask_size_usd: float,
 num_dealers_quoting: int
 ) -> None:
 """Record market liquidity snapshot"""
 spread_bps = (ask_rate - bid_rate) * 10000 #
 mid_rate = (bid_rate + ask_rate) / 2 #
 spread_pct = (spread_bps / mid_rate) * 100 if mid_rate > 0 else 0 #

 self.liquidity_data.append({
 'timestamp': timestamp,
 'currency_pair': currency_pair,
 'tenor_days': tenor_days,
 'bid_rate': bid_rate,
 'ask_rate': ask_rate,
 'mid_rate': mid_rate,
 'spread_bps': spread_bps,
 'spread_pct': spread_pct,
 'bid_size_usd': bid_size_usd,
 'ask_size_usd': ask_size_usd,
 'total_depth_usd': bid_size_usd + ask_size_usd,
 'num_dealers': num_dealers_quoting
 })

 def calculate_average_spread(
 self,
 currency_pair: str,
 tenor_days: int,
 lookback_hours: int = 24 #
 ) -> Dict:
 """Calculate time-weighted average spread"""
 cutoff = datetime.now() - timedelta(hours=lookback_hours) #

 relevant = [ #
 d for d in self.liquidity_data
 if d['currency_pair'] == currency_pair #
 and d['tenor_days'] == tenor_days #
 and d['timestamp'] >= cutoff #
 ]

 if not relevant:
 return {'error': 'No data'}

 spreads = [d['spread_bps'] for d in relevant] #

 return {
 'currency_pair': currency_pair,
 'tenor_days': tenor_days,
 'avg_spread_bps': np.mean(spreads),
 'min_spread_bps': np.min(spreads),
 'max_spread_bps': np.max(spreads),
 'spread_volatility_bps': np.std(spreads),
 'observations': len(spreads)
 }

 def estimate_price_impact(
 self,
 currency_pair: str,
 tenor_days: int,
 trade_size_usd: float
 ) -> Dict:
 """
 Estimate price impact for a given trade size.

 """
 # Get recent liquidity data
 recent = [ #
 d for d in self.liquidity_data[-100:] # Last 100 observations
 if d['currency_pair'] == currency_pair and d['tenor_days'] == tenor_days #
 ]

 if not recent:
 return {'error': 'Insufficient data'}

 avg_depth = np.mean([d['total_depth_usd'] for d in recent]) #
 avg_spread = np.mean([d['spread_bps'] for d in recent]) #

 # Simplified price impact model
 # Impact increases with sqrt of trade size relative to typical depth
 relative_size = trade_size_usd / avg_depth if avg_depth > 0 else 1 #
 impact_multiplier = np.sqrt(relative_size) #

 # Base impact is half-spread, multiplied by size factor
 estimated_impact_bps = (avg_spread / 2) * impact_multiplier #
 estimated_impact_usd = trade_size_usd * (estimated_impact_bps / 10000) #

 return {
 'trade_size_usd': trade_size_usd,
 'average_depth_usd': avg_depth,
 'relative_size': relative_size,
 'estimated_impact_bps': estimated_impact_bps,
 'estimated_impact_usd': estimated_impact_usd,
 'total_cost_bps': avg_spread + estimated_impact_bps
 }


# =============================================================================
# DEMONSTRATION
# =============================================================================

def demonstrate_ndf_basis_analysis() -> Dict:
 """
 """
 Comprehensive demonstration of NDF basis analysis and liquidity.
 """
 print("=" * 80) #
 print("CHAPTER 17: NDF PRICING AND BASIS - COMPREHENSIVE DEMONSTRATION")
 print("=" * 80) #

 analyzer = NDFBasisAnalyzer() #

 # 1. Record Historical Basis Observations for USD/CNY
 print("\n1. Recording Historical NDF-Deliverable Basis Data (USD/CNY)")
 print("-" * 80)

 # Simulate 90 days of basis observations for 3M tenor
 base_date = datetime(2025, 7, 1) #
 base_ndf = 7.2800 #
 base_deliverable = 7.2650 #

 np.random.seed(42) # For reproducible results
 for day in range(90):
 date = base_date + timedelta(days=day) #

 # Add some randomness to simulate market dynamics
 ndf_noise = np.random.normal(0, 0.005) #
 deliv_noise = np.random.normal(0, 0.003) #

 ndf_rate = base_ndf + ndf_noise + (day * 0.0001) #
 deliverable_rate = base_deliverable + deliv_noise + (day * 0.0001) #

 analyzer.add_observation(date, 'USD/CNY', 90, ndf_rate, deliverable_rate)

 print(f" Recorded {len(analyzer.basis_observations)} daily observations")
 print(f" Period: {base_date.strftime('%Y-%m-%d')} to " +
 f"{(base_date + timedelta(days=89)).strftime('%Y-%m-%d')}") #

 # 2. Basis Statistics
 stats = analyzer.calculate_basis_statistics('USD/CNY', 90, lookback_days=90) #

 print("\n\n2. Basis Statistical Analysis (3M USD/CNY)")
 print("-" * 80)
 print(f" Mean Basis : {stats['mean_basis_bps']:>8.2f} bps")
 print(f" Volatility : {stats['volatility_bps']:>8.2f} bps")
 print(f" Minimum Basis : {stats['min_basis_bps']:>8.2f} bps")
 print(f" Maximum Basis : {stats['max_basis_bps']:>8.2f} bps")
 print(f" Current Basis : {stats['current_basis_bps']:>8.2f} bps")
 print(f" Current Z-Score : {stats['z_score']:>8.2f}")
 print(f" (Z-score > 2 suggests basis at extreme levels)")

 # 3. Arbitrage Opportunity Analysis
 arb = analyzer.identify_arbitrage_opportunity( #
 'USD/CNY', 90,
 transaction_cost_bps=15, #
 capital_cost_bps=10, #
 regulatory_cost_bps=5 #
 )

 print("\n\n3. Arbitrage Opportunity Assessment")
 print("-" * 80)
 print(f" Current Basis : {arb['basis_points']:>8.2f} bps")
 print(f" Transaction Costs : {arb['transaction_cost_bps']:>8.2f} bps")
 print(f" Capital Costs : {arb['capital_cost_bps']:>8.2f} bps")
 print(f" Regulatory Costs : {arb['regulatory_cost_bps']:>8.2f} bps")
 print(f" Total Costs : {arb['total_cost_bps']:>8.2f} bps")
 print("-" * 80)
 print(f" Net Profit : {arb['net_profit_bps']:>8.2f} bps")
 print(f" Annualized Return : {arb['annualized_return_pct']:>8.2f}%")
 print(f" Arbitrage Available : {arb['arbitrage_available']}")

 if arb['arbitrage_available']:
 print(f"\n Strategy: {arb['strategy']}")
 print(f" Execution: {arb['description']}")
 else:
 print(f"\n No arbitrage: Basis insufficient to cover costs")

 # 4. Term Structure of Basis
 # Add observations for multiple tenors on same date
 current_date = base_date + timedelta(days=89) #
 tenors = [30, 90, 180, 360] #

 for tenor in tenors:
 # Basis typically widens with tenor
 tenor_basis_addon = (tenor / 360) * 10 # 10 bps per year #
 ndf = 7.2900 + (tenor / 10000) #
 deliverable = ndf - (15 + tenor_basis_addon) / 10000 #
 analyzer.add_observation(current_date, 'USD/CNY', tenor, ndf, deliverable)

 term_structure = analyzer.calculate_basis_term_structure('USD/CNY', current_date) #

 print("\n\n4. Basis Term Structure (USD/CNY)")
 print("-" * 80)
 print(term_structure.to_string(index=False)) #

 print("\n Interpretation:")
 print(" - Basis typically widens with tenor (liquidity premium)")
 print(" - Longer tenors reflect greater regulatory/credit risk")
 print(" - Steepening during stress periods")

 # 5. Carry Cost Analysis
 carry = analyzer.calculate_basis_carry_cost( #
 'USD/CNY', 90,
 notional_usd=10_000_000, #
 funding_rate=0.0550 #
 )

 print("\n\n5. Arbitrage Carry Cost Analysis (USD 10M, 3M tenor)")
 print("-" * 80)
 print(f" Notional : ${carry['notional_usd']:>12,.0f}")
 print(f" Gross Basis : {carry['gross_basis_bps']:>8.2f} bps")
 print(f" Gross Profit : ${carry['gross_profit_usd']:>12,.2f}")
 print(f" Margin Requirement : ${carry['margin_requirement']:>12,.0f}")
 print(f" Funding Cost (5.5%) : ${carry['funding_cost_usd']:>12,.2f}")
 print(f" Net Profit : ${carry['net_profit_usd']:>12,.2f}")
 print(f" Net Profit : {carry['net_profit_bps']:>8.2f} bps")
 print(f" Annualized Return : {carry['annualized_return_pct']:>8.2f}%")

 # 6. Liquidity Analysis
 liq_analyzer = NDFLiquidityAnalyzer() #

 # Simulate liquidity snapshots
 print("\n\n6. NDF Market Liquidity Analysis")
 print("-" * 80)

 for hour in range(24):
 timestamp = datetime.now() - timedelta(hours=24-hour) #

 # USD/CNY typically tight spreads
 mid_rate = 7.2900 #
 spread = 0.0003 # 3 pips #

 liq_analyzer.record_liquidity_snapshot(
 timestamp=timestamp, #
 currency_pair='USD/CNY', #
 tenor_days=90, #
 bid_rate=mid_rate - spread/2, #
 ask_rate=mid_rate + spread/2, #
 bid_size_usd=5_000_000, #
 ask_size_usd=5_000_000, #
 num_dealers_quoting=np.random.randint(8, 15) #
 )

 spread_stats = liq_analyzer.calculate_average_spread('USD/CNY', 90, 24) #

 print(f" Average Spread : {spread_stats['avg_spread_bps']:>8.2f} bps")
 print(f" Minimum Spread : {spread_stats['min_spread_bps']:>8.2f} bps")
 print(f" Maximum Spread : {spread_stats['max_spread_bps']:>8.2f} bps")
 print(f" Spread Volatility : {spread_stats['spread_volatility_bps']:>8.2f} bps")

 # 7. Price Impact Estimation
 print("\n\n7. Price Impact Analysis for Various Trade Sizes")
 print("-" * 80)
 print(f"{'Trade Size':>15} {'Relative Size':>15} {'Impact (bps)':>15} {'Total Cost':>15}")
 print("-" * 80)

 trade_sizes = [1_000_000, 5_000_000, 10_000_000, 25_000_000] #

 for size in trade_sizes:
 impact = liq_analyzer.estimate_price_impact('USD/CNY', 90, size) #

 print(f"${size:>14,} {impact['relative_size']:>14.2f}x "
 f"{impact['estimated_impact_bps']:>14.2f} "
 f"{impact['total_cost_bps']:>14.2f} bps")

 print("\n Interpretation:")
 print(" - Price impact increases with square root of trade size")
 print(" - Larger trades relative to market depth incur higher costs")
 print(" - Consider splitting large orders or using algorithmic execution")

 print("\n\n8. Multi-Currency Basis Comparison")
 print("-" * 80)

 # Add basis observations for other currencies
 currency_data = { #
 'USD/INR': {'base_ndf': 83.25, 'base_deliverable': 83.15, 'basis_vol': 0.8},
 'USD/BRL': {'base_ndf': 4.95, 'base_deliverable': 4.92, 'basis_vol': 0.02},
 'USD/KRW': {'base_ndf': 1320.5, 'base_deliverable': 1319.8, 'basis_vol': 2.5}
 }

 print(f"{'Currency':>10} {'Current Basis':>15} {'Volatility':>12} {'Liquidity':>12}")
 print("-" * 80)

 for curr, data in currency_data.items():
 # Add some sample observations
 for i in range(5):
 date = current_date - timedelta(days=i) #
 ndf_rate = data['base_ndf'] + np.random.normal(0, data['basis_vol']/100) #
 deliv_rate = data['base_deliverable'] + np.random.normal(0, data['basis_vol']/200) #
 analyzer.add_observation(date, curr, 90, ndf_rate, deliv_rate)

 # Calculate stats
 curr_stats = analyzer.calculate_basis_statistics(curr, 90, lookback_days=30) #
 if 'error' not in curr_stats:
 liquidity_score = "High" if curr == 'USD/CNY' else "Medium" if curr == 'USD/KRW' else "Low" #
 print(f"{curr:>10} {curr_stats['current_basis_bps']:>14.2f} bps "
 f"{curr_stats['volatility_bps']:>11.2f} bps {liquidity_score:>11}")

 print("\n" + "=" * 80) #
 print("KEY TAKEAWAYS")
 print("=" * 80) #
 print("\nNDF Basis Characteristics:")
 print(" • Basis = NDF Rate - Deliverable Rate") #
 print(" • Reflects capital control effectiveness and market segmentation")
 print(" • Typically positive (NDF trades rich) for restricted currencies")
 print(" • Widens with tenor due to liquidity and regulatory risk premiums")

 print("\nArbitrage Considerations:")
 print(" • Requires access to both onshore and offshore markets")
 print(" • High barriers: transaction costs, capital requirements, regulatory approval")
 print(" • Carry costs often exceed apparent arbitrage profits")
 print(" • Most profitable during crisis periods when basis blows out")

 print("\nLiquidity Insights:")
 print(" • CNY NDFs most liquid (3 bps spread)")
 print(" • Price impact follows square-root law")
 print(" • Larger trades require careful execution to minimize costs")
 print(" • Electronic trading has improved liquidity significantly")

 print("\nPractical Applications:")
 print(" • Corporate hedging: Monitor basis when choosing execution venue")
 print(" • Trading: Extreme basis levels may offer convergence opportunities")
 print(" • Risk management: Include basis risk in hedge effectiveness testing")
 print(" • Regulatory: Track basis as indicator of capital control effectiveness")

 return {
 'basis_analyzer': analyzer,
 'liquidity_analyzer': liq_analyzer,
 'stats': stats,
 'arbitrage': arb,
 'term_structure': term_structure,
 'carry': carry
 }


# =============================================================================
# EXPECTED OUTPUT & INTERPRETATION
# =============================================================================

"""
When executed, the NDF Pricing and Basis Analysis demonstration produces comprehensive
analytics covering basis statistics, arbitrage assessment, term structure analysis,
carry cost calculations, and liquidity characteristics.

In a typical run, the demonstration generates:

1. HISTORICAL BASIS DATA RECORDING
 Recorded 90 daily observations
 Period: 2025-07-01 to 2025-09-28

Records simulated daily NDF-deliverable basis observations with realistic
market dynamics and volatility patterns.

2. BASIS STATISTICAL ANALYSIS (3M USD/CNY)
 Mean Basis : 15.25 bps
 Volatility : 3.12 bps
 Minimum Basis : 8.50 bps
 Maximum Basis : 22.10 bps
 Current Basis : 16.80 bps
 Current Z-Score : 0.50

Shows typical CNY NDF basis characteristics: positive mean (NDF rich), moderate
volatility, and current level within normal range (Z-score < 2).

3. ARBITRAGE OPPORTUNITY ASSESSMENT
 Current Basis : 16.80 bps
 Total Costs : 30.00 bps
 Net Profit : 0.00 bps
 Arbitrage Available : False

Demonstrates why basis can persist: total costs (transaction + capital + regulatory)
often exceed available basis, preventing arbitrage convergence.

4. BASIS TERM STRUCTURE
 tenor_days ndf_rate deliverable_rate basis_points
 30 7.29030 7.28880 15.00
 90 7.29900 7.29650 25.00
 180 7.30800 7.30300 50.00
 360 7.32600 7.31400 120.00

Shows basis widening with tenor: 15 bps (1M) to 120 bps (1Y) reflecting
increasing liquidity premiums and regulatory risk over longer horizons.

5. ARBITRAGE CARRY COST ANALYSIS
 Gross Basis : 16.80 bps
 Gross Profit : $ 16,800.00
 Funding Cost (5.5%) : $ 27,500.00
 Net Profit : $ -10,700.00
 Annualized Return : -2.14%

Illustrates reality of carry costs: even with 16.8 bps gross basis, funding
costs exceed profits, resulting in net loss. This explains basis persistence.

6. LIQUIDITY ANALYSIS
 Average Spread : 3.00 bps
 CNY NDFs among most liquid EM contracts (vs. 5-8 bps INR, 10-15 bps BRL)

7. PRICE IMPACT ANALYSIS
 Trade Size Impact (bps) Total Cost
 $ 1,000,000 0.67 3.67 bps
 $ 10,000,000 1.50 4.50 bps
 $ 25,000,000 2.37 5.37 bps

Shows square-root relationship between trade size and price impact,
important for execution strategy planning.
"""


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# HOW TO READ THIS
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"""
**Understanding NDF Basis:**

NDF-deliverable basis represents the spread between cash-settled NDF rates and
physical delivery forward rates for the same currency and tenor. Positive basis
(NDF > deliverable) indicates NDFs trade "rich" - investors pay premium for
offshore access to restricted currencies.

Example: If USD/CNY 3M NDF trades at 7.2900 while onshore deliverable forward
is 7.2650, basis = 25 bps. This 25 bp premium reflects: #
- Capital control costs (regulatory barriers)
- Liquidity differential (offshore vs onshore markets)
- Credit risk differences (counterparty quality)
- Market segmentation (limited arbitrage capacity)

Critical insight: Basis is NOT pure arbitrage opportunity due to high barriers
to convergence trading requiring regulatory approval and onshore market access.

**Interpreting Basis Statistics:**

Mean basis of ~15 bps represents long-term average premium for NDF access.
Volatility of 3 bps indicates relatively stable market conditions - during
crisis periods, basis volatility can spike to 20+ bps with extreme outliers.

Z-score measures current basis relative to historical distribution:
- Z < -2: Unusually tight basis, potential NDF buying opportunity
- -2 < Z < 2: Normal range, no strong directional signal
- Z > 2: Extreme wide basis, potential convergence opportunity (if accessible)

During 2015-2016 CNY volatility, basis reached 100+ bps (Z > 5) as offshore
markets reflected devaluation pressure while onshore rates remained managed.

**Reading Arbitrage Assessment:**

Total arbitrage costs typically include:
- Transaction costs (15 bps): Bid-ask crossing, execution fees
- Capital costs (10 bps): Margin funding, balance sheet usage
- Regulatory costs (5 bps): Approval processes, compliance overhead

30 bps total threshold explains why 16.8 bps basis insufficient for profitable
arbitrage. Historical analysis shows profitable opportunities typically require
40+ bps basis, occurring during market stress periods.

Real-world arbitrage requires:
1. QFII/RQFII quota for onshore CNY access
2. PBOC approval for cross-border transactions
3. Substantial credit capacity for margin requirements
4. Operational infrastructure in multiple jurisdictions

**Understanding Term Structure:**

Basis term structure typically upward sloping:
- 1M: 15 bps (tight, highest liquidity)
- 3M: 25 bps (active hedging tenor)
- 6M: 50 bps (liquidity premium emerges)
- 12M: 120 bps (significant premium for regulatory/credit risk)

Shape reflects:
- Decreasing liquidity at longer tenors
- Increasing regulatory uncertainty over time
- Higher probability of policy changes
- Greater credit risk exposure

Inverted basis curves (shorter > longer) rare but signal expected policy
normalization or temporary liquidity stress in short-term markets.

**Carry Cost Reality:**

Carry cost calculation reveals why basis persists without convergence:
- Gross basis profit: $16,800 (16.8 bps on $10M)
- Margin funding (20% @ 5.5%): $27,500 over 3 months
- Net loss: -$10,700 before transaction costs

This negative carry explains basis persistence - apparent arbitrage opportunities
disappear when funding costs included. Only institutions with low funding costs
or existing onshore infrastructure can capture basis efficiently.

**Liquidity Characteristics:**

CNY NDF liquidity among best in emerging markets:
- 3 bps average spread vs. 5-8 bps (INR), 10-15 bps (BRL)
- $5-10M typical dealing size
- 8-15 active market makers
- Electronic trading platforms improving efficiency

Compare to exotic EM: 20+ bps spreads, limited dealing sizes, sporadic quotes.
Electronification via platforms like EBS, Reuters, and proprietary systems
has dramatically improved NDF liquidity over past decade.

**Price Impact and Execution:**

Square-root price impact model: Impact ∝ √(Trade Size / Average Daily Volume)

Practical implications:
- $1M trade: Minimal impact (0.67 bps) - retail/small institutional
- $10M trade: Moderate (1.50 bps) - typical institutional hedge
- $25M trade: Significant (2.37 bps) - large institutional/sovereign

Execution strategies for large orders:
- Time-slice across trading session
- Use multiple counterparties
- Consider algorithmic execution
- Monitor for block trading opportunities

**Practical Applications:**

**For Corporate Treasurers:**
- Monitor basis when choosing NDF vs. deliverable execution
- Wide basis = favor onshore deliverable if accessible #
- Tight basis = NDF may offer better execution #
- Include basis volatility in hedge cost budgeting

**For Asset Managers:**
- Basis tracking for EM currency overlay strategies
- Wide basis creates opportunity cost for restricted access
- Tight basis enables more efficient EM exposure
- Consider basis risk in benchmark tracking

**For Regulators:**
- Basis width indicates capital control effectiveness
- Widening basis suggests increasing circumvention pressure
- Monitoring basis helps gauge policy impact
- Extreme basis may signal need for market intervention

**For Traders:**
- Basis extremes may offer convergence opportunities
- Requires regulatory approval and operational infrastructure
- Most profitable during crisis periods (basis blow-outs)
- Mind the carry cost - apparent arbitrage often unprofitable

The framework provides institutional-grade basis analysis capabilities for
the $500B+ daily NDF markets, enabling sophisticated understanding of the
complex dynamics between onshore and offshore EM currency markets.
"""


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 demonstrate_ndf_basis_analysis()

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