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Hybrid versus Highbred -A New Approach to Combine Economic Models with Time-series Analyses. Ming-Yuan Leon Li Quantitative Finance (SSCI journal), 10, 637-647 (2008). Motivations. Economic models - PowerPoint PPT Presentation
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1
Hybrid versus Highbred-A New Approach to Combine Economic Models with Time-
series Analyses
Ming-Yuan Leon Li
Quantitative Finance (SSCI journal), 10, 637-647 (2008)
2
3
Motivations Economic models
They try to measure and quantify the relationships between exchange rates and a set of economic fundamentals
Meese and Rogoff (1983): the forecast performances of exchange rates produced by economic models based on fundamentals are no better than those using random walk models
Longer horizons or nonlinear methods
4
Motivations
Time-series approaches The lagged values of the change in the
lagged exchange rates could be used to predict their future values
ARMA (Auto-Regressive Moving Average) model
5
Motivations
Could we design a composite model that incorporated both of the economic models and time series techniques?
The information from both of fundamental variables derived from economic theory and their own lagged variables should be valuable for market participants
6
Motivations Portfolio managers should weigh the information
from fundamental variables from the economic theories and the own lagged data
Moreover, in some periods, we argue that managers should pay more attention to the economic models (time-series approach) and vice versa in other period
One of the main obstacles is how to decide the weights of each of these two different forecasting techniques
7
Motivations Employ the Markov Switching (MS) mechanism
to decide the time-varying weights of the various alternatives
In brief, we set up a framework with two states to capture two different forecasting alternatives. Moreover, one of the features of the MS model is to estimate the probabilities of the specific state at each time point by data itself
In this paper, we use the estimated and time-varying probabilities to serve as the weights of each technique.
8
Few Interesting Questions
The composite models with time-varying loading outperform each of these two techniques and the random walk models?
What are the relationships between the various volatility regimes and various forecasting techniques?
Examining the exchange rates of developing countries’ currencies and comparing the differences between them Extreme Price Movements
9
Engle and Hamilton (1990)
Employed the MS techniques and examined the long term swing behaviors of exchange rates
Extend the MS system developed by Engle and Hamilton (1990) Marsh (2000), Bessec (2003), Clarida, et
al. (2003), De Grauwe and Vansteenkiste (2001), and Frommel, et al. (2005).
10
Unlike prior studies The effects of fundamental variables on
exchange rates would vary according to the phase of market state
Engle and Hamilton (1990) two states on the constant term of regression
equation versus a framework with two states on the slop terms
Highlighting the dynamics of return volatility in exchange rates.
11
Unlike prior studies What are the relationships between various
volatility states and forecasting techniques? are investors more concerned with fundamental
variables or lagged exchange rates during the volatile periods?
The comparative study of exchange rates in both mature and emerging economies.
To our knowledge, few if any, previous studies have explored these crucial exchange rate issues.
12
Model Specifications
Time-series Approach
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Model Specifications
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Model Specifications
Hybrid Model with Constant Weights
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Model Specifications
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16
Model Specifications
Hybrid versus highbred Highbred= Hybrid model + a
restriction Shortcoming of the constant weight
the weights of w and (1-w) remain constant throughout the whole entire sample period.
17
Model Specifications
Hybrid Model with Time-varying Weights
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Model Specifications
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Model Specifications
p(st|It): filtering probability
p(st|IT): smoothing probability
p(st|It-1) : Predicting Probability
20
Model Specifications
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21
Model Specifications
The difference between the two hybrid models
In contrast with studies by Engle and Hamilton (1990) and Frommel, et al. (2005)
22
Empirical Results The monthly bilateral exchange rates (in
U.S. dollars per unit of foreign currency) for the currency of four industrialized countries (France, Germany, U.K. and Japan) and two developing Asian countries (South Korea and Taiwan)
The data period is from January, 1980 to August, 2000 for 248 observations
The data source is AREMOS database
23
Forecasting Performance
Table 3 In-sample Forecasting Performances of Various Model Specifications for Exchange Rates (a) Mean Square Error (MSE)
Highbred Model Hybrid Model ARMA (1, 1)
Model Economic
Model Constant Weights
Time-varying Weights
Mature Countries
France 10.218
[2.623%] 10.300
[1.843%] 10.616
[-1.169%] 9.931*
[5.356%]
Germany 10.648
[0.896%] 10.694
[0.467%] 10.547
[1.830%] 10.139*
[5.633%]
Japan 12.442
[0.471%] 12.120
[3.044%] 12.753
[-2.013%] 12.070*
[3.449%]
U.K. 9.318
[3.208%] 9.358
[2.795%] 9.434
[2.008%] 9.222*
[4.207%] Emerging Countries
South Korea 11.274
[0.718%] 11.420
[-0.567%] 11.465
[-0.957%] 11.061*
[2.597%]
Taiwan 1.970
[4.067%] 2.062
[-0.446%] 2.065
[-0.595%] 1.892*
[7.866%]
24
(b) Mean Absolute Error (MAE) Highbred Model Hybrid Model
ARMA (1, 1) Model
Economic Model
Constant Weights Time-varying
Weights Mature Countries
France 2.487
[1.982%] 2.519
[0.713%] 2.568
[-1.229%] 2.407*
[5.141%]
Germany 2.532
[0.357%] 2.545
[-0.159%] 2.549
[-0.318%] 2.442*
[3.905%]
Japan 2.782
[-1.444%] 2.713
[1.074%] 2.777
[-1.259%] 2.663*
[2.896%]
U.K. 2.345
[0.206%] 2.305
[1.932%] 2.284*
[2.795%] 2.303
[2.020%] Emerging Countries
South Korea 1.267
[0.639%] 1.313
[-2.958%] 1.340
[-5.116%] 1.189*
[6.713%]
Taiwan 0.851
[1.390%] 0.879
[-1.854%] 0.882
[-2.202%] 0.827*
[4.114%]
25
Parameter Estimates
(a) Highbred Model: ARMA (1, 1) Model cont. α β σ Log-Lik. Mature Countries
France 0.001
(0.011) 0.968*** (0.019)
-0.955*** (0.034)
3.201*** (0.144)
-637.873
Germany 0.026
(0.098) 0.638
(0.632) -0.565 (0.675)
3.258*** (0.147)
-642.236
Japan -0.273 (0.226)
0.201 (0.361)
-0.141 (0.344)
3.530*** (0.159)
-661.986
U.K, -0.330 (0.397)
-0.885*** (0.034)
0.975*** (0.014)
3.212*** (0.145)
-638.718
Emerging Countries
South Korea 0.177
(0.184) 0.328
(0.277) -0.217 (0.281)
3.347*** (0.151)
-648.864
Taiwan -0.006 (0.02)
0.896*** (0.066)
-0.785*** (0.089)
1.391*** (0.063)
-431.968
26
(b) Highbred Model: Economic Model cont. γ δ σ Log-Lik. Mature Countries
France 0.398
(0.249) 1.119* (0.649)
-1.240 (0.967)
3.214*** (0.145)
-638.863
Germany 0.017*** (0.001)
0.493 (0.572)
-1.289 (0.930)
3.265*** (0.147)
-642.778
Japan -0.964***
(0.322) -0.323 (0.348)
-2.641** (1.072)
3.484*** (0.157)
-658.753
U.K. -0.753***
(0.291) 0.332
(0.406) 2.726*** (1.031)
3.219*** (0.145)
-639.244
Emerging Countries
South Korea 0.302
(0.230) -0.151 (0.356)
-0.034 (0.239)
3.368*** (0.152)
-650.449
Taiwan -0.034 (0.086)
0.048 (0.094)
0.908* (0.529)
1.423*** (0.064)
-437.601
27
(c) Hybrid Model with Constant Weights cont. α β γ δ σ Log-Lik. Mature Countries
France 0.145
(0.131) 0.630*** (0.198)
-1.868*** (0.648)
0.843* (0.492)
-0.532 (0.500)
3.187*** (0.143)
-636.798
Germany 0.085
(0.447) -0.400* (0.243)
1.511* (0.799)
1.082* (0.593)
-2.038 (1.507)
3.252*** (0.146)
-641.727
Japan -0.866** (0.377)
0.111 (0.255)
-0.301 (0.864)
-0.319 (0.355)
-2.354** (1.182)
3.482*** (0.157)
-658.666
U.K. -1.263***
(0.445) -0.561** (0.242)
1.653** (0.816)
0.445 (0.341)
5.227*** (1.554)
3.008*** (0.135)
-622.515
Emerging Countries
South Korea 0.187
(0.232) 0.421
(0.342) -0.993 (0.834)
-0.351 (0.312)
0.098 (0.628)
3.338*** (0.15)
-648.240
Taiwan -0.038 (0.177)
0.885*** (0.086)
-0.776*** (0.117)
0.039 (0.09)
1.033 (0.826)
1.403*** (0.190)
-429.745
28
(d) Hybrid Model with Time-varying Weights cont. α β γ δ σ1 σ2 P11 P22 Log-Lik. Mature Countries
France -0.013 (0.029)
0.994*** (0.043)
-0.894*** (0.088)
0.604* (0.363)
0.481 (1.779)
2.307*** (0.357)
4.100*** (0.555)
0.571** (0.293)
0.359* (0.221)
-633.096
Germany 0.133
(0.292) -0.606***
(0.089) 0.900*** (0.033)
1.056* (0.613)
-1.165 (1.581)
2.424*** (0.198)
3.854*** (0.290)
0.973*** (0.020)
0.971*** (0.023)
-634.158
Japan -0.975***
(0.389) -0.673***
(0.192) 0.844*** (0.124)
-0.232 (0.424)
-2.706** (1.364)
2.335*** (0.308)
3.813 (0.238)
0.951 (0.036)
0.984 (0.014)
-656.062
U.K. -0.160 (0.168)
0.079 (0.246)
-0.370* (0.230)
0.171 (0.520)
1.895*** (0.897)
2.026*** (0.172)
3.460*** (0.197)
0.994*** (0.197)
0.996*** (0.006)
-615.810
Emerging Countries
South Korea 0.017
(0.025) 0.877*** (0.100)
-0.564*** (0.250)
-1.193 (2.026)
1.469 (2.938)
0.635*** (0.068)
8.600 (1.270)
0.981 (0.013)
0.884 (0.070)
-355.370
Taiwan -0.013 (0.017)
0.793*** (0.096)
-0.701*** (0.110)
0.256 (0.470)
4.640* (2.698)
0.652*** (0.063)
2.711*** (0.365)
0.881*** (0.045)
0.553*** (0.140)
-367.134
29
Parameter Estimates
The high volatility state (st=2) versus the low volatility state (st=1)
The high (low) volatility state corresponds to the forecasting technique of the Economic model (Time-series approach)
30
Parameter Estimates
The two ARMA components are significant in 1%
The fundamental variables are significant
South Korea: a special case
31
Explanations of Our Empirical Results
The composite model with non-constant loadings on two forecasting techniques outperforms the setting with constant loadings
32
Explanations of Our Empirical Results
The high (low) volatility state corresponds to the forecasting technique of the economic model (time-series approach)
33
Explanations of Our Empirical Results
The speed of convergence toward theoretical values which are derived from economic theories should be greater as the deviation from theoretical values rises in absolute value
The great/small deviation from theoretical values should be closely associated with the high/low volatility state
34
Explanations of Our Empirical Results
The state of the time-series approach with the own lagged values corresponds to the state of low volatility
Investors might well picture the future exchange rates via their own past values during the stable periods
35
Explanations of Our Empirical Results
However, during the volatile period, the fundamental variables are insignificant for the case of South Korea
36
High/Low Volatility
Table 2 Measurement of Volatility at High Volatility State Relative to Low Volatility State for the Hybrid Model with Time-varying weights Mature Countries Emerging Countries
France Germany Japan U.K. South Korea Taiwan (σ2/σ1) 1.777 1.590 1.6191 1.708 13.543 4.158
37
Explanations of Our Empirical Results
-15
-10
-5
0
5
10
15
1980/1 1982/1 1984/1 1986/1 1988/1 1990/1 1992/1 1994/1 1996/1 1998/1 2000/1
-20
-10
0
10
20
30
40
50
1980/1 1982/1 1984/1 1986/1 1988/1 1990/1 1992/1 1994/1 1996/1 1998/1 2000/1
0
0.25
0.5
0.75
1
1980/1 1982/1 1984/1 1986/1 1988/1 1990/1 1992/1 1994/1 1996/1 1998/1 2000/1
0
0.25
0.5
0.75
1
1980/1 1982/1 1984/1 1986/1 1988/1 1990/1 1992/1 1994/1 1996/1 1998/1 2000/1
(a) France (b) South Korea Unusual Regime Crisis Regime
38
Explanations of Our Empirical Results
Asian financial crisis of 1997 Substantial dollar depreciations and large
scale of capital flights. So the exchange rate volatility was a larger
amount than what was originally planned. Investors’ irrational overreaction behaviors
39
Out-off-sample Performance
The in-sample performance tests of various alternatives give an indication of their historical performance.
Investors in markets would be more concerned with how well they can do in the future using alternative forecasting techniques.
40
This paper withholds the last 12 twelve observations (namely one year data) of the sample for each market are withheld, and conducts a rolling estimation process is conducted
As in the in-sample test, the out-of-sample forecasting performances of alternative model specifications for exchange rates are also compared with the random walk model.
41
(a) Mean Square Error (MSE) Highbred Model Hybrid Model
ARMA (1, 1) Model
Economic Model
Constant Weights Time-varying
Weights Mature Countries
France 9.504
[-25.319%] 8.129
[-7.191%] 10.965
[-44.581%] 8.062*
[-6.309%]
Germany 6.874
[14.519%] 8.099
[-0.712%] 7.992
[0.619%] 6.565*
[18.365%]
Japan 19.927
[-84.178 %] 11.107
[-2.664%] 19.325
[-78.620%] 10.746*
[0.671%]
U.K. 4.942
[-19.428%] 4.490
[-8.523%] 6.120
[-47.912 %] 3.656*
[11.654%] Emerging Countries
South Korea 4.491
[-48.958%] 3.257*
[-8.037%] 4.743
[-57.320] 3.294
[-9.272%]
Taiwan 0.735
[-3.521%] 0.739
[-4.101%] 0.970
[-36.532%] 0.725*
[-2.070%]
42
(b) Mean Absolute Error (MAE) Highbred Model Hybrid Model
ARMA (1, 1) Model
Economic Model
Constant Weights Time-varying
Weights Mature Countries
France 2.573
[-16.004%] 2.421
[-9.173 %] 2.961
[-33.504%] 2.372*
[-6.939%]
Germany 2.201
[5.184%] 2.310
[0.493%] 2.512
[-8.199%] 2.200*
[5.220%]
Japan 3.639
[-32.458%] 2.679*
[2.501%]] 3.509
[-27.721%] 2.741
[2.061%]
UK 1.792
[-4.116%] 1.736
[-0.861%] 2.088
[-21.269%] 1.640*
[4.730%] Emerging Countries
South Korea 1.661
[-19.805%] 1.486*
[-5.886%] 1.869
[-34.801%] 1.546
[-11.499%]
Taiwan 0.646
[-4.531%] 0.664
[-7.442] 0.869
[-40.581%] 0.641*
[-3.733%]
43
Conclusion and Extensions
First, market investors will more heavily emphasize on the fundamental variables derived from economic models when exchange rates are more volatile. By conversely, during the stable periods, market participants would increase the loadings of the effects of the lagged values of exchange rates.
44
Second, the hybrid model with time-varying loadings outperforms the highbred model for most cases. By contrast, the performancess of the hybrid model with constant weights are trivial.
45
Finally, compared with random walk models, the present findings lend support to the superiority of the hybrid model with time-varying loadings in the out-of-sample forecasting performances for mature economies such as Germany, Japan and U.K., but not for emerging markets such as South Korea and Taiwan.
46
Conclusion and Extensions Two caveats should be mentioned Future areas of work may include applying this
approach to combine more complex time-series models (e.g., GARCH-based models) and other economic models (e.g., considering relative income levels, and explicitly including the crisis dummy variables)
Future researchers might employ other testing methods
47
Other applications of MRS models
Li, Ming-Yuan Leon* (2008) Could the jump diffusion technique enhance the effectiveness of futures hedging models? A reality test, Mathematics and Computers in Simulation, accepted and forthcoming 【 SCI 】
Li, Ming-Yuan Leon* (2008) The dynamics of the relationship between spot and futures markets under high and low variance regimes, Applied Stochastic Models in Business and Industry, accepted and forthcoming 【 SCI 】
Li, Ming-Yuan Leon* (2007) Purchasing power parity under high and low volatility regimes, Applied Economics Letters, 14, 581-589.【 SSCI 】
48
Li, Ming-Yuan Leon* (2007) Volatility state and international diversification of international stock markets, Applied Economics, 39, 1867-1876. 【 SSCI 】
Li, Ming-Yuan Leon*, Hsiou-wei William Lin and Hsiu-Hua Rau (2005) Performance of Markov-switching model on business cycle identification revisited, Applied Economics Letters, 12, 513-520.【 SSCI 】
Li, Ming-Yuan Leon* and Hsiou-wei William Lin (2004) Estimating value at risk via Markov switching ARCH models - An empirical study on stock index returns, Applied Economics Letters, 11, 679-692.【 SSCI 】