Portfolio Modeling with Time Dependent Correlation Structure Computational Finance Rachel Chiu Sean...
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Transcript of Portfolio Modeling with Time Dependent Correlation Structure Computational Finance Rachel Chiu Sean...
Portfolio Modeling with Time Dependent Correlation Structure
Computational Finance
Rachel ChiuSean Zeng
Ricardo AffinitoSarah Thomas
Dr. Katherine Ensor
Motivation“Risk management and oversight now focuses too much on the idiosyncratic risk that affects an individual firm and too little on the systematic issues that could affect market liquidity as a whole. To put it somewhat differently, the conventional risk-management framework today focuses too much on the threat to a firm from its own mistakes and too little on the potential for mistakes to be correlated across firms.”
~ Timothy F. Geithner, President and CEO of the Federal Reserve Bank
Objective
To develop a mixture model that captures the complex correlations
present in a given portfolio
Outline
• A Brief Introduction To Stocks And Stock Data
• Exploratory Data Analysis (EDA)– Tools and Results
• What Lies Beyond– Model Introduction
Stock Exchange: Key Concepts• Stocks and stock prices
– Stock: A share of ownership– Whenever prices match, a trade takes place
• Ticker Symbols– Ex. GOOG(Google), RDS-B(Shell), V(Visa), etc.
• Returns and adjusted returns– Percent gain or loss in a given period– To accurately calculate returns, adjust for splits and dividends
• Portfolios– A collection of stocks invested
Data Selection Methodology
There are five main steps for selecting the data suitable for this project.1. Sector selection2. Market capitalization analysis3. Portfolio creation4. Weighing5. Calculate returns
Sector Selection Criteria
• History– Past performance and availability of data
• Sector characteristics– Startups vs. traditional
• Inter-relationship between the sectors– Ex. Oil Market Leaders and Solar -> Positive– Ex. Oil Market Leaders and Airlines -> Negative
Sectors Examined
• Wind• Solar• Emerging Markets• Oil Market Leaders• Oil Growth
• Technology• Finance• Airline• Automotive
Market Capitalization (MC)
• Seek companies that best represent the performance and characteristics of the chosen sector
• Formula: – NSO = Number of Shares Outstanding– SP = Share Price
• Market capitalization = the public opinion of a company’s net worth
• It helps us select the leaders in a sector
SPNSOMC
Portfolio Creation
• The stock market is too big; a portfolio limits the scope of the study
• A portfolio defines a pseudo world for complex correlations
Weighing Methods
• Weights = Percent of money invested in a sector or a firm
• Equally weighted within sectors• Across sectors, for example…
– Equally weighted– Optimally weighted (diversify and minimize
covariance)
Portfolio [Simple Net] Return
• Simple Net Return for Portfolio– Consists of N Assets– Simple Weighted Average of Assets
• Portfolio P places weight wi on asset I
• Simple Return of P at time t is:
where, Ri,t is the simple return of asset i and
N
itittp RwR
1,,
11
N
iiw
Exploratory Data Analysis5
00
01
00
00
15
00
02
00
00
25
00
0
Portfolio Sector's Growth
Date
Pri
ce
2007-09-04 2007-09-27 2007-10-22 2007-11-14 2007-12-10 2008-01-04 2008-01-30 2008-02-25 2008-03-19 2008-04-14 2008-05-07 2008-06-02
Wind
Solar
Emkt
Oil ML
Oil G
Airl
Auto
Tech
Fina
Outline
• A Brief Introduction To Stocks And Stock Data
• Exploratory Data Analysis (EDA)– Tools and Results
• What Lies Beyond– Model Introduction
EDA Statistical Methods/Tools
• Population and Sample Moments• Covariance, Correlation, Autocorrelation• Regression Methods (OLS, Quantile)
Moments Defined for Continuous R.V.’s
• nth Moment of a continuous R.V. X:
• nth Central Moment of a continuous R.V. X:
dxxfxXEm nn
n )(
dxxfxXEm n
xn
xn )()(
• Normal Distributions can be uniquely determined bythe first two moments (mean, variance)
• For non-Normal Distributions higher-order momentsare also of interest (skewness, kurtosis)
Mean and Variance
• Mean– The expected value of a random variable. What is
expected to come based on the information from the data collected in the past.
• Variance– The mean subtracted from the random variable,
squared. A measure of the dispersion of values. • Standard Deviation is the square root of variance.
T
xxxE
T
tt
x
1ˆ][
T
ttx xx
Ts
1
222
1
1̂
Skewness & Kurtosis Interpretation• Skewness : Measurement of distribution symmetry
– Symmetric:– Right Skewed:– Left Skewed:
• Excess Kurtosis : Heavier Tails than Normal Dist?– Because Kurtosis of the Normal Dist. = 3.– Positive ( ) means heavy tails (ref. to Normal Dist).
• a.k.a. leptokurtic distribution
– Negative ( ) means light tails (ref. to Normal Dist).• a.k.a. platykurtic distribution
0xS0xS
0* xK
0* xK
0xS
3* xx KK
SkewnessS
Example of Skewness
-0.2 0.0 0.2 0.4 0.6
02
46
81
01
21
4
Distribution of Simple Returns Group 3 - Renewable NRG (Other)
Stocks = JASO,ESLR,TSL,FSLR,SPWR,YGE,STP
Simple Returns
Fre
qu
en
cy
Mean = -0.0004742SDev = 0.04667
Skewness = 2.861
-0.2 0.0 0.2 0.4 0.6
02
46
810
1214
Distribution of Simple Returns Group 3 - Renewable NRG (Other)
Stocks = JASO,ESLR,TSL,FSLR,SPWR,YGE,STP
Simple Returns
Fre
quen
cy
Mean = -0.0004742SDev = 0.04667
Skewness = 2.861
Covariance and Correlation
• Covariance– Like variance, the measure of the change between
two different variables.
• Correlation– Measures the strength of the linear relationship
between two variables.– Ranges between -1 and 1.
])][])([[(),( YEYXEXEYXCOVXY
YXYX
YXCOV
),(
,
Multi-Variate Mean Vector & Covariance Matrix
• Consider a Random Vector:
• Mean Vector / Covariance Matrix (Population):
• Mean Vector / Covariance Matrix (Sample):
},...,,{ 21 Txxx
TXXX ECov μXμXΣX
)(
T
PX XEXEE )(),...,(}{ 1μX
PXXX ,....,, 21X
provided the expectations exist.
Sample:
Txt
T
txtx
T
ttx T
xT
)ˆ()ˆ(1
1ˆ1ˆ11
μxμxΣ μ
Correlation MatrixAM
SC
GCT
AF
VWSY
F
WN
DEF
ZOLT
JASO
ESLR
TSL
FSLR
SPW
R
YGE
STP
COP
CVX
RDS.
B
TOT
XOM
APA
CAM
HES
NBL
OXY CA
L
DAL
JBLU
LUV
NW
A
AMSC 1.00 0.24 0.27 0.04 0.29 0.36 0.47 0.33 0.34 0.47 0.39 0.37 0.37 0.33 0.24 0.31 0.34 0.36 0.34 0.29 0.35 0.33 0.14 0.11 0.20 0.25 0.05GCTAF 0.24 1.00 0.45 0.02 0.24 0.18 0.25 0.28 0.17 0.29 0.28 0.31 0.21 0.21 0.24 0.32 0.22 0.25 0.24 0.17 0.19 0.20 -0.06 -0.04 -0.03 0.03 -0.04VWSYF 0.27 0.45 1.00 -0.06 0.25 0.27 0.27 0.33 0.27 0.31 0.28 0.33 0.31 0.27 0.30 0.37 0.27 0.25 0.34 0.26 0.24 0.25 0.00 0.01 0.05 0.08 -0.02WNDEF 0.04 0.02 -0.06 1.00 -0.02 0.13 0.03 0.08 0.15 0.12 0.08 0.07 0.00 -0.01 -0.02 -0.01 -0.02 -0.01 0.09 0.04 0.02 0.00 0.01 0.03 0.00 -0.02 -0.03ZOLT 0.29 0.24 0.25 -0.02 1.00 0.29 0.29 0.37 0.36 0.31 0.27 0.29 0.28 0.23 0.21 0.29 0.25 0.27 0.27 0.23 0.26 0.25 0.19 0.18 0.27 0.27 0.14JASO 0.36 0.18 0.27 0.13 0.29 1.00 0.53 0.58 0.70 0.61 0.65 0.59 0.43 0.42 0.29 0.36 0.41 0.41 0.46 0.39 0.41 0.43 0.05 0.06 0.11 0.06 0.01ESLR 0.47 0.25 0.27 0.03 0.29 0.53 1.00 0.46 0.56 0.55 0.50 0.48 0.50 0.42 0.37 0.45 0.46 0.46 0.36 0.37 0.40 0.47 0.06 0.05 0.19 0.14 0.02TSL 0.33 0.28 0.33 0.08 0.37 0.58 0.46 1.00 0.52 0.51 0.61 0.51 0.46 0.43 0.33 0.42 0.44 0.40 0.47 0.45 0.41 0.43 0.05 0.07 0.10 0.09 0.01FSLR 0.34 0.17 0.27 0.15 0.36 0.70 0.56 0.52 1.00 0.57 0.57 0.57 0.45 0.42 0.32 0.37 0.42 0.39 0.42 0.37 0.41 0.44 0.05 0.05 0.09 0.04 0.03SPWR 0.47 0.29 0.31 0.12 0.31 0.61 0.55 0.51 0.57 1.00 0.57 0.63 0.42 0.37 0.33 0.37 0.37 0.42 0.46 0.39 0.42 0.44 0.13 0.08 0.16 0.16 0.04YGE 0.39 0.28 0.28 0.08 0.27 0.65 0.50 0.61 0.57 0.57 1.00 0.61 0.39 0.34 0.33 0.35 0.37 0.37 0.41 0.36 0.42 0.37 0.05 0.01 0.09 0.01 -0.05STP 0.37 0.31 0.33 0.07 0.29 0.59 0.48 0.51 0.57 0.63 0.61 1.00 0.40 0.36 0.34 0.37 0.35 0.38 0.45 0.37 0.35 0.40 -0.08 -0.05 0.07 -0.08 -0.08COP 0.37 0.21 0.31 0.00 0.28 0.43 0.50 0.46 0.45 0.42 0.39 0.40 1.00 0.77 0.24 0.63 0.74 0.71 0.50 0.61 0.57 0.66 0.07 0.05 0.18 0.17 0.02CVX 0.33 0.21 0.27 -0.01 0.23 0.42 0.42 0.43 0.42 0.37 0.34 0.36 0.77 1.00 0.24 0.70 0.81 0.67 0.51 0.60 0.55 0.65 0.09 0.07 0.17 0.19 0.03RDS.B 0.24 0.24 0.30 -0.02 0.21 0.29 0.37 0.33 0.32 0.33 0.33 0.34 0.24 0.24 1.00 0.26 0.19 0.20 0.23 0.22 0.24 0.28 0.00 0.01 0.00 0.08 -0.03TOT 0.31 0.32 0.37 -0.01 0.29 0.36 0.45 0.42 0.37 0.37 0.35 0.37 0.63 0.70 0.26 1.00 0.71 0.58 0.44 0.50 0.47 0.56 -0.02 -0.02 -0.01 0.09 -0.06XOM 0.34 0.22 0.27 -0.02 0.25 0.41 0.46 0.44 0.42 0.37 0.37 0.35 0.74 0.81 0.19 0.71 1.00 0.64 0.48 0.58 0.53 0.63 0.12 0.09 0.21 0.22 0.05APA 0.36 0.25 0.25 -0.01 0.27 0.41 0.46 0.40 0.39 0.42 0.37 0.38 0.71 0.67 0.20 0.58 0.64 1.00 0.54 0.58 0.63 0.62 -0.02 -0.05 0.02 0.05 -0.13CAM 0.34 0.24 0.34 0.09 0.27 0.46 0.36 0.47 0.42 0.46 0.41 0.45 0.50 0.51 0.23 0.44 0.48 0.54 1.00 0.60 0.71 0.64 0.01 -0.03 -0.02 -0.01 -0.04HES 0.29 0.17 0.26 0.04 0.23 0.39 0.37 0.45 0.37 0.39 0.36 0.37 0.61 0.60 0.22 0.50 0.58 0.58 0.60 1.00 0.67 0.73 -0.03 -0.03 0.06 0.01 -0.07NBL 0.35 0.19 0.24 0.02 0.26 0.41 0.40 0.41 0.41 0.42 0.42 0.35 0.57 0.55 0.24 0.47 0.53 0.63 0.71 0.67 1.00 0.72 0.05 0.03 0.08 0.11 -0.03OXY 0.33 0.20 0.25 0.00 0.25 0.43 0.47 0.43 0.44 0.44 0.37 0.40 0.66 0.65 0.28 0.56 0.63 0.62 0.64 0.73 0.72 1.00 0.00 -0.03 0.12 0.08 -0.10CAL 0.14 -0.06 0.00 0.01 0.19 0.05 0.06 0.05 0.05 0.13 0.05 -0.08 0.07 0.09 0.00 -0.02 0.12 -0.02 0.01 -0.03 0.05 0.00 1.00 0.80 0.52 0.67 0.77DAL 0.11 -0.04 0.01 0.03 0.18 0.06 0.05 0.07 0.05 0.08 0.01 -0.05 0.05 0.07 0.01 -0.02 0.09 -0.05 -0.03 -0.03 0.03 -0.03 0.80 1.00 0.45 0.63 0.86JBLU 0.20 -0.03 0.05 0.00 0.27 0.11 0.19 0.10 0.09 0.16 0.09 0.07 0.18 0.17 0.00 -0.01 0.21 0.02 -0.02 0.06 0.08 0.12 0.52 0.45 1.00 0.54 0.43LUV 0.25 0.03 0.08 -0.02 0.27 0.06 0.14 0.09 0.04 0.16 0.01 -0.08 0.17 0.19 0.08 0.09 0.22 0.05 -0.01 0.01 0.11 0.08 0.67 0.63 0.54 1.00 0.60NWA 0.05 -0.04 -0.02 -0.03 0.14 0.01 0.02 0.01 0.03 0.04 -0.05 -0.08 0.02 0.03 -0.03 -0.06 0.05 -0.13 -0.04 -0.07 -0.03 -0.10 0.77 0.86 0.43 0.60 1.00
WIND SOLAR OIL MARKET LEADERS OIL GROWTH AIRLINES
Auto-Correlation Function
• Assuming Weakly Stationary Time Series:
** is the lag-l autocorrelation of rt.
• For a given sample, we can estimate ACF as:
0)(
),(
)()(
),(
l
t
ltt
ltt
lttl rVAR
rrCOV
rVARrVAR
rrCOV
10;)(
))((ˆ
1
2
1
Tlrr
rrrr
T
tt
T
ltltt
l
ACF Data
• Auto-Correlation Function (ACF)– The correlation of the data with itself, at different
points in time.ACF
WIND SOLAR EMKT OILML OILG TECH FINA AIRL AUTOlag1 -0.02535 0.001937 -0.12514 0.098406 0.024246 -0.02548 0.149731 0.030379 -0.01032lag2 0.040051 -0.01604 -0.03283 0.028327 -0.0206 -0.02146 0.019471 -0.05436 0.039133lag3 -0.0392 -0.04285 0.115005 0.008292 -0.03115 -0.01141 -0.04077 0.001591 0.024963lag4 0.017539 -0.02221 -0.12275 -0.02576 -0.0132 -0.00852 -0.05975 -0.02699 0.043242
ACF Expanded• Might need Autoregressive (AR) Model if empirical
auto-correlation is high.• For Order Determination of the AR Model the PACF
(Partial ACF, a function of the time series’ ACF is used)… [Along with other (likelihood based) criteria such as AIC]
• For time-varying variance (as opposed to mean) a conditional heteroskedasticity (CH) component must be added to the model proposed.
Regression
• Ordinary Least Squares Regression– Estimates the conditional mean– Minimizes the sum of squared residuals– Does not show the tail behavior
• Quantile Regression– Estimates the Quantiles (percentiles)– Not as affected by outliers– Shows the tail behavior (associations)
Ordinary Least Squares (OLS) Regression Analysis
• Conditional Mean Function Modeling
• Objective Function = Sum of Squared Residuals
• Minimizing…
• Leads to the Normal Equations:• Solving:
),0(~
]|[2
..1
11111
N
E
IIDmi
mnT
mnmmm
εβXεXYY
2
11
2
1 nT
mnmmS βXYr
2
111minarg n
Tmnmn
βXYβ
yXβXX TT 1ˆ
n
yXX)Xβ T-1T(ˆ1 n
Quantile Regression
• What is a quantile?
• Define Loss (Piecewise Linear) Function
dd
)1,0(
))0(()( uIuuT
th})(|inf{}{1 xFxF
= Quantile of X.
for some
Quantile Regression
• Conditional Quantile Function Modeling…
• Objective Function = Sum of Weighted Differences
for any
1)|( nT
mnY x )β(XQ
||1
bxyS Tii
n
i
)1,0(
Quantile Regression
0.0 0.2 0.4 0.6 0.8 1.0
-0.0
20.
000.
02
Intercept Lag 1, Coefficient vs Quantile
0.0 0.2 0.4 0.6 0.8 1.0
-0.4
-0.2
0.0
0.2
0.4
Oil G Lag 1, Coefficient vs Quantile
0.0 0.2 0.4 0.6 0.8 1.0
-0.6
-0.2
0.2
0.4
0.6
Oil ML Lag 1, Coefficient vs Quantile
0.0 0.2 0.4 0.6 0.8 1.0-0
.20.
00.
20.
4
Wind Lag 1, Coefficient vs Quantile
0.0 0.2 0.4 0.6 0.8 1.0
-0.1
00.
000.
10
Solar Lag 1, Coefficient vs Quantile
0.0 0.2 0.4 0.6 0.8 1.0
-0.1
0.0
0.1
0.2
0.3
EMKT Lag 1, Coefficient vs Quantile
Quantile Regression
0.0 0.2 0.4 0.6 0.8 1.0
-0.0
3-0
.01
0.0
10.0
3
Intercept Lag 1, Coefficient vs Quantile
0.0 0.2 0.4 0.6 0.8 1.0
-0.3
-0.2
-0.1
0.0
0.1
0.2
Technology Lag 1, Coefficient vs Quantile
0.0 0.2 0.4 0.6 0.8 1.0
-0.2
-0.1
0.0
0.1
Finance Lag 1, Coefficient vs Quantile
0.0 0.2 0.4 0.6 0.8 1.0
-0.0
50.0
50.1
00.1
50.2
0
Energy Lag 1, Coefficient vs Quantile
Quantile Regression Benefits
• Robust Estimation // Modeling• Better View of Overall Portfolio Distribution as
compared to conventional Conditional Mean Modeling.
• Explore Sources of Heterogeneity in the Portfolio Response (Observed Return)
EDA Results (1)
• Distribution of Returns vary in shape from sector to sector.– Traditional Energy (left skewed)– Other Sectors (right skewed)
• Correlations:– Stronger within some sectors (e.g. energy, etc.)– Weaker between sectors (e.g. technology & finance,
etc.)– Others negatively correlated (e.g. oil & airlines)– Depend on the timeframe inspected (structure
change)
EDA Results (2)• Autocorrelations:
– Low, at the time do not plan to adjust for any sector autocorrelations (AR model)
• Regression Methods:– Used as a tools to inspect distributional shape– Portfolio VaR (tails) related to individual security
worst case returns (tail behavior).
Outline
• A Brief Introduction To Stocks And Stock Data
• Exploratory Data Analysis (EDA)– Tools and Results
• What Lies Beyond– Model Introduction
Incorporating Dynamic Volatility…
• Several Modeling Techniques have been developed:– General Autoregressive Conditional Heteroskedastic (GARCH) Models– Regime Switching Approaches
• For Incorporating Co-Volatility and External Influences– Multivariate GARCH– Factor MGARCH
• Some Plausible Options…– Parametric (MV Normals or weighted MV Normal and Additional MV
Distribution)– Non-Parametric
Mixture-Modeling (Parametric)
• Model Portfolio Daily Returns• Mixture-Model Approach• We observe the portfolio Return at time t• Returns Dist’n (Portfolio) (Yt) is a combination of
distributions with different behavior (Lt, Mt, Ht), and with weights constraint (P1,t+P2,t+P3,t=1).
• These random variables vary as a function of time. We seek building the model based on the empirical data observed.
ttttttt HPMPLPY ,3,2,1 observedun-observed
Looking at Exogenous Predictors…• We are also looking at external predictors to use as
part of the model.
• Example: Energy –– Commodities Pricing and their association with Energy
Stocks. (NYMEX, ETC.)– CPI and PPI relationships to stocks (also other sectors)
(BLS)– Data for energy consumption per sectors, etc. (EIA)– Heating/Cooling Degree Days (NCDC)
• These factors (data), known to influence certain sectors (supplies, investments) should provide opportunities to build improved models.
Questions…
We would like to thank…VIGRE, NSF, CoFES
Please send any questions to…Ricardo Affinito ([email protected])Rachel Chiu ([email protected])Sean Zeng ([email protected])